Likelihood Ratio Research Articles

A systematic review and meta-analysis of diagnostic test accuracy of chest ultrasound in diagnosing pediatric pulmonary tuberculosis.

Despite medical advancement, pediatric pulmonary tuberculosis (PTB) still has high morbidity and mortality, due to challenging detection in clinical practice. Ultrasound has been touted as the next best diagnostic tool but currently, this claim is unfounded. Therefore, this study aims to systematically review the diagnostic parameters of chest ultrasound in diagnosing pediatric PTB. The literature search started and ended on December 23, 2023. We searched MEDLINE, Cochrane Library, Pubmed, Science Direct, and Google Scholar. Our research question could be formulated as "In pediatric patients who present with signs and symptoms of PTB such as fever, cough, and poor weight gain, how accurate is chest ultrasound in ruling in and ruling out pediatric PTB when the diagnosis is compared to culture, PCR or CXR?" This systematic review adhered to the PRISMA-DTA guidelines while the meta-analysis was conducted with STATA program using the "midas" and "metandi" commands. There are five studies included with 137 positive PTB children. The combined sensitivity is 84% (95% confidence interval [CI]: 76-89), specificity of 38% (95% CI: 24-54), and summary receiver operating curve yields an area under the curve of 0.83 (95% CI: 0.80-0.86). The I2 value is 24% (95% CI: 0-100) with a p-value of 0.13. The combined negative predictive value is 0.68 (95% CI: 0.58-0.79), and the positive predictive value is 0.57 (95% CI: 0.51-0.63). The positive likelihood ratio is 1 with a 6% increase from the baseline while the negative likelihood ratio is 0.43 with a 12% decrease from the baseline. Chest ultrasound is sensitive but currently could neither exclude nor confirm pediatric PTB.

Abstract PO3-13-02: Genomic and intrinsic subtype correlates of serum thymidine kinase activity in patients with metastatic breast cancer treated with palbociclib and fulvestrant in the PYTHIA trial

Abstract Background: Serum thymidine kinase activity (sTKa) is a novel non-invasive proliferation biomarker providing prognostic, predictive and monitoring information in patients (pts) with metastatic breast cancer (MBC). We have shown in PYTHIA that high baseline sTKa and lack of sTKa clearance at day 15 of the first cycle of treatment with palbociclib (P)+ fulvestrant (F) are poor prognostic factors in terms of PFS. Here we characterized the molecular features of tumor samples from PYTHIA, and their relation with sTKa. Methods: PYTHIA (IBCSG 53- 14/BIG 14-04; NCT02536742) is a phase II biomarker discovery trial downstream of the AURORA molecular program (BIG 14-01; NCT02102165) which enrolled 122 pts with endocrine-resistant, ER+ and HER2 - MBC receiving P+F. Intrinsic subtypes of metastatic samples from 24 pts were estimated using RNA-Seq. Somatic mutations (mut) from 66 pts were identified using an NGSpanel of 411 cancer-related genes (36 primary and 48 metastatic samples). sTKa was measured using DiviTumÒ TKa (Biovica International) before treatment (D0) and at day 15 (D15) of cycle 1 of P+F. sTKa clearance at D15 was defined as sTKa below the limit of detection (LOD). Intrinsic subtypes and ESR1 mut were assessed in metastatic samples, PIK3CA and TP53 mut were derived from a combined cohort, wheremetastatic samples were used when available and primary samples otherwise. Association of mut and continuous sTKa with PFS was assessed using Cox regression. Results: In metastatic samples, sTKa levels at D0 tended to be higher in luminal B and HER2-enriched subtypes. Lack of sTKa clearance at D15 was observed in 14% of the cases, all classified as luminal B. PIK3CA and TP53 were the most frequently altered genes (38% and 27%, respectively). ESR1 mut were found in 8% of primary and 25% of metastatic samples. Only TP53 was associated with worse PFS. sTKa levels at D0 tended to be higher in pts with TP53 mut vs wild-type (wt) and lower in PIK3CA mut vs wt. Interestingly, lack of sTKa clearance at D15 was more frequent in TP53 mut vs wt and less frequent in PIK3CA mut vs wt. Levels of sTKa were similar in pts with ESR1 mut vs wt both at D0 and at D15 (Table 1). The inclusion of TP53 as a predictor of PFS in bivariate Cox models with sTKa at either D0 (likelihood ratio test p=0.44) or D15 (p=0.21) did not add to the prognostic value provided by sTKa alone. Conclusions: Tumors with high proliferation and poor prognostic features (luminal B, HER2-enriched and TP53 mut) tend to have higher baseline sTKa and less sTKa response on-treatment, in line with sTKa being a circulating dynamic biomarker of tumor cell proliferation. After accounting for sTKa values, TP53mutational status did not contribute to prognosis. This suggests that high sTKa at D0 and lack of sTKa clearance at D15 might be associated with a worse prognosis independently of TP53 status. Further validation in larger datasets is warranted. Table 1: Results summary Citation Format: Svitlana Tyekucheva, Thayane Crestani, Michail Ignatiadis, Patrick Neven, Marco Colleoni, Stéphanie Henry, Konstantinos Papadimitriou, Antonio Bernardo, Elena Seles, Francois Duhoux, Iain Macpherson, Alastair Thomson, David Mark Davies, Mattias Bergqvist, Ilenia Migliaccio, Gabriele Zoppoli, Roswitha Kammler, Heidi De Swert, Barbara Ruepp, Angel Guerrero, Arnau Llinas, Danai Fimereli, Amal Arahmani, David Cameron, Sherene Loi, Martine Piccart, Meredith Regan, Matteo Benelli, Luca Malorni. Genomic and intrinsic subtype correlates of serum thymidine kinase activity in patients with metastatic breast cancer treated with palbociclib and fulvestrant in the PYTHIA trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-13-02.

Abstract PO4-27-10: A Systematic Review and Meta-analysis of Diagnostic Performance of Fluorescein Guided Sentinel Lymph Node Biopsy in Early Breast Cancer

Abstract Background: Evaluation of axillary lymph nodes status in cN0 axilla is performed by sentinel lymph node biopsy (SLNB) utilizing a combination of radioactive isotope and blue dye. There is a paucity of radioactive tracer in many countries of transitional economies. Hence, alternatives to isotope have been explored, viz. indocyanine green (ICG), superparamagnetic iron oxide nanoparticles and Fluorescein Sodium (FS). This systematic review and meta-analysis, evaluates the diagnostic performance of Fluorescein Sodium guided SLNB. Objectives: The objective was to evaluate the diagnostic performance of Fluorescein Sodium for sentinel lymph node biopsy in early breast cancer. Methods: PRISMA 2020 guidelines were followed for this meta-analysis. Eligibility criteria: Studies where SLNB was performed using Fluorescein Sodium for breast carcinoma. Information sources: PubMed, EMBASE, Cochrane library and online clinical trial registers Risk of bias: Articles were assessed for risk of bias using the QUADAS-2 tool. Synthesis of results: The main summary measures were pooled detection rate and pooled false negative rate using random effects model. In order to describe heterogeneity, we constructed summary receiver operating characteristic (SROC) curves with 95% prediction regions, estimated using bivariate meta-analysis with a test level random effect only, and forest plots. Calculations were performed using the Meta-Disc v1.4 program (available from https://metadisc.software.informer.com/1.4/). Results: Out of 45 studies found on initial systematic search, 7 studies were found eligible and were included in the meta-analysis based on data on 332 patients. The pooled detection rate was 93.2% (95% confidence interval [CI] 87% to 97%).). The heterogeneity (I2 value) of the pooled detection rate was 71%. Five validation studies were included for pooling the false negative rate (FNR) and included a total of 211 patients. The pooled FNR was 5.6% (95% CI; 2.9 to 9.07). Five validation studies were included for sensitivity, specificity, diagnostic odds ratio, LR+ and LR- meta-analysis. With regards to the identification of SLN the pooled sensitivity was 80% [(95% CI, 68% to 89%); I2=79.8]; specificity was 98% [(95% CI, 95% to 100%); I2=0.0]. The likelihood ratio for a positive test LR+ was 23.2 [(95% CI, 5.91 to 95.18); I2=0.0] and likelihood ratio for a negative test a LR- of 0.41 [(95% CI, 0.16 to 1.05); I2=88.1]. The diagnostic Odds ratio OR was 76.94 (95% CI, 15.88 to 372.79). Diagnostic accuracy as measured by SROC was 99.08%. Q index was 0.96. Publication bias for pooled detection rate as per Egger’s test was -2.2 (p= 0.66) and for FNR was 1.85 (p=0.29). Visual inspection of summary ROC curve and of forest plots did not show significant heterogeneity. Conclusion: Fluorescein guided SLNB is an affordable option for detection of lymph node metastases in cN0 patients with early breast cancer. It achieves a high detection rate of 93% with a false negative rate of 5.6% for the detection of axillary lymph node metastasis. We recommend larger multinational trials to confirm its high detection rate with low false negativity. Citation Format: Sanjay Kumar Yadav, Bharath S, Sarada Khadka, Dhananjaya Sharma, Chandan Kumar Jha, Gaurav Agarwal, Manju Singh, Anurag Srivastava, Amit Goyal. A Systematic Review and Meta-analysis of Diagnostic Performance of Fluorescein Guided Sentinel Lymph Node Biopsy in Early Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-27-10.

Abstract PO1-02-02: Update of RSClin with extended TAILORx follow-up and development and validation of a new tool for risk of late distant recurrence

Abstract Background: RSClin® (Sparano et al J Clin Oncol 2020, PMID 33306425) is an educational tool that estimates the 10-year risk of distant recurrence (DR) and the absolute benefit of chemotherapy in HR-positive, HER2-negative, node-negative early breast cancer. The RSClin tool integrates the Recurrence Score® (RS) result with the clinical-pathological features tumor grade, tumor size and patient age using patient-specific meta-analysis (PSMA) applied to the NSABP B-14, TAILORx and NSABP B-20 studies. Approximately 3 years of additional follow-up data have accumulated in the TAILORx study since the RSClin tool was developed, including 50% more DR events in the RS 11-25 group (375 vs. 250), and 46% more DR events in the overall population (561 vs. 384). Methods: The RSClin tool estimates were updated to include the extended follow-up data from TAILORx. As before, the PSMA included 10,004 women with hormone-receptor positive, HER2-negative, node-negative breast cancer from the NSABP B-14 (n=577) trial receiving endocrine therapy (ET) alone and the TAILORx trial receiving ET alone (n=4854) or chemotherapy (CT) plus ET (n=4573). The baseline risk estimate used TAILORx event rates with ET alone. Since there was evidence of differing hazard ratios in years 0-5 versus 5+, models using time-varying effects were used. A patient-specific estimator of absolute CT benefit was computed by combining a PSMA estimate of the individualized relative CT effect from the TAILORx and NSABP B-20 (n=550 HER2-negative) trials with risk estimates for ET alone. Similar methods were used to develop and validate a new tool for assessing the risk of late DR (from year 5 to 10) in patients who survive DR-free for 5 years on ET alone after surgery. External validation of DR risk estimation was performed in an independent cohort of 1098 women enrolled in the Clalit Health Service registry. Results: With the updated data, the model integrating RS result with clinical-pathological features was more informative for DR risk than either RS result alone or the clinical-pathological features alone, both for 10-year DR risk and for late DR risk (likelihood ratio tests, each p≤.001). The updated 10-year RSClin risk estimates are similar to the existing RSClin tool risk estimates with slightly lower risk estimates for very high RS results and more precise estimates for mid-range RS results. The updated chemotherapy effect estimates are slightly lower than for the existing RSClin. The updated RSClin tool 10-year risk estimates were strongly associated with DR risk in the independent Clalit registry (Cox regression p< .001) and closely approximated the observed DR risk (Lin concordance 0.87). The new tool’s late DR risk estimates beyond 5 years were strongly associated with late DR risk in the Clalit registry (Cox regression p=.002) and closely approximated the observed late DR risk (Lin concordance 0.92). Conclusions: The RSClin tool 10-year DR risk estimates have been updated to incorporate extended follow-up data from the TAILORx study. A new tool for assessing late DR risk has been developed and validated that provides more prognostic information than either clinical-pathological features or genomic risk used individually. Citation Format: Joseph Sparano, Michael Crager, Robert Gray, Gong Tang, Jess Hoag, Frederick Baehner, Charles Geyer, Steven Shak, Norman Wolmark, Salomon Stemmer. Update of RSClin with extended TAILORx follow-up and development and validation of a new tool for risk of late distant recurrence [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-02-02.

Abstract PS10-06: Contribution of a breast cancer polygenic risk score to mammography artificial intelligence models and breast density for long term breast cancer risk prediction

Abstract Artificial intelligence (AI) models based on deep learning of mammogram images have been developed for lesion detection and diagnosis as well as breast cancer (BC) risk. We and others have shown that these AI models, coupled with breast density, predict long-term breast cancer risk. Common germline genetic variation, in particular a polygenic risk score (PRS) that captures the cumulative impact of hundreds of common risk variants, has been shown associated with 3-fold increased BC risk. The BC-PRS may complement imaging risk factors to improve identification of women at high BC risk and better target prevention strategies. We evaluated the contribution of a BC-PRS to two imaging-based AI models (Transpara AI cancer detection system and Mirai 5-year risk) and breast density measures to long-term BC risk. We hypothesized that the BC-PRS will improve long-term risk prediction above AI and density measures. We examined this hypothesis within a cohort of 10,271 women enrolled to the Mayo Clinic Biobank between 2009 and 2015. Eligible women had no prior cancer, were ages 35 to 85, had a full-field digital screening mammogram within two years of enrollment and genotyping by Regeneron. Incident invasive and DCIS BC was identified through the Mayo tumor registry and follow-up questionnaires, and person years were calculated from date of mammogram through date of BC, last mammogram at Mayo or last questionnaire. We estimated Transpara AI malignancy score (1-10), 5-year Mirai risk and Volpara volumetric density measures (volumetric percent density, dense volume) on screening mammograms at enrollment and obtained clinical BI-RADS density. We calculated the BC-PRS using 268 SNPs (of the 313-SNP score) and the weighted sum of the SNPs using odds ratios from the largest BC GWAS of European ancestry to date. We performed cox proportional hazards regression adjusting for age and BMI to estimate hazard ratios (HRs), 95% confidence intervals (CI), and C-statistics (AUC) to describe the contribution of BC-PRS (per standard deviation (SD)) to the association of AI scores and breast density with BC risk. Likelihood ratio tests (LRT) and bootstrapping were used to compare model performance with vs. without inclusion of BC-PRS, at 10-years follow-up. Comparisons to the Tyrer-Cuzick model are ongoing. Over a median of 9.1 years (Interquartile range, 6.8 to 10.6), 446 incident BC were identified of which 285 invasive and 103 DCIS were confirmed by pathology report (60 were self-reported); 399 (256 invasive) occurred within 10 years and were used in primary analyses. Transpara score [HR per 1 unit=1.18 (1.14, 1.23), AUC=0.655 (0.628, 0.682)] and Mirai risk [HR per SD log risk=1.54 (1.43, 1.65), AUC=0.681 (0.655, 0.706)] were both associated with BC. All three breast density measures were also significantly associated with BC, including in models with AI scores (Table). Addition of the BC-PRS to models with AI scores and breast density improved both model fit (All PLRT < 0.001; Table) and discrimination, with increases in AUC ranging from 0.033 to 0.034 for models with Transpara and density and 0.025 to 0.026 for models with Mirai and density (all P-values < 0.001; Table). Analyses restricted to invasive BC showed identical results. But, the BC-PRS contribution at 5-year follow-up was attenuated. In this cohort study, we found BC-PRS contributed to long-term risk prediction of BC above imaging AI models and breast density. Imaging and genetic risk factors are complementary and may outperform existing risk models and provide more accurate risk assessment to aid in precision prevention strategies. Citation Format: Celine Vachon, Christopher Scott, Imon Banerjee, Ramon Correa Moderno, Dan Hursh, Matthew Jensen, Yvonne Wang, Kathleen Brandt, Karla Kerlikowske, Sandhya Pruthi, Fergus Couch, Stacey Winham. Contribution of a breast cancer polygenic risk score to mammography artificial intelligence models and breast density for long term breast cancer risk prediction [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS10-06.

Abstract PO3-28-10: MRI Radiomic phenotypes derived from the ECOG-ACRIN E4112 Trial to assess high-risk ductal carcinoma in situ

Abstract INTRODUCTION Ductal carcinoma in situ (DCIS) is a non-lethal pre-invasive breast cancer that can co-exist with invasive disease. Dynamic contrast-enhanced (DCE) MRI is sensitive for the detection of high-grade DCIS and invasive cancer while Oncotype DCIS Score is a 12-gene assay that can assess recurrence risk. In practice, it is difficult to distinguish low- from high-risk DCIS, which leads to overtreatment for up to half of women diagnosed with DCIS. We hypothesize that radiomic phenotypes of DCIS derived from DCE-MRI data may serve as prognostic markers to improve risk stratification by capturing disease heterogeneity. Here we evaluate the ability of these phenotypes to predict DCIS Score and upstaging of DCIS to invasive disease on wide local excision in a multicenter trial. METHODS Data: DCE-MRI data from the ECOG-ACRIN E4112 trial were retrospectively analyzed. Primary analysis focused on participants with data on disease upstaging (N=295), with secondary analysis in a subset (N=174) of participants with DCIS Scores (dichotomized as >55 and 55) and pure DCIS. Clinical information included patient demographics, lesion morphology on MRI, background parenchymal enhancement, DCIS grade, central necrosis, and hormone receptor status. Data analysis: Radiologist-drawn lesion segmentations and publicly available software, CaPTk, were used to compute 64 radiomic features from first post-contrast images for each participant. Radiomic phenotypes were identified using hierarchical clustering on the extracted features. A Chi-square test was used to evaluate the association between radiomic phenotypes and each outcome. The likelihood ratio test was used to compare two logistic regression models: 1)clinical model using only clinical information as predictors and 2) clinical+phenotypes model using clinical information and phenotype assignment as predictors. Each model was used for the prediction of upstaging to invasive disease and DCIS score. Model performance was evaluated as the 10-fold cross-validated area under the receiver operator characteristic curve (AUC). A p< 0.05 was considered significant. RESULTS A total of 45 (15%) cases upstaged to invasive disease. Two radiomic phenotypes were identified: Phenotype 1 indicated greater lesion signal heterogeneity, while Phenotype 2 indicated lower heterogeneity. Radiomic phenotype was strongly associated with disease upstaging (p=0.0034) – with a higher rate of upstaging for Phenotype 1 – but not with DCIS score (p=0.1174, Table 1). For predicting disease upstaging, the clinical+phenotypes model yielded a higher AUC=0.72 and a significantly better fit to the data (p=0.0022) compared to the clinical model parameterized by clinical information alone (AUC=0.69). For predicting DCIS Score, the clinical+phenotypes model (AUC=0.77) showed similar performance compared to the clinical model (AUC=0.76) and no significant improvement in fit to the data (p=0.2920). CONCLUSION Radiomic phenotypes capturing disease heterogeneity show promise as prognostic predictors for predicting disease upstaging in DCIS compared to clinical information alone and may enable more efficient disease management. We observed that phenotypes did not have independent predictive outcome for DCIS score, suggesting that MRI and DCIS Score offer independent information and could be combined in future models to better predict disease recurrence or progression. Clinical applications of radiomic phenotypes may improve risk stratification and potentially result in decreased overtreatment of women diagnosed with DCIS. Table 1: Association between radiomic phenotypes and DCIS outcomes using a Chi square test. Citation Format: Kalina Slavkova, Ruya Kang, Vivian Belenky, Anum Kazerouni, Debosmita Biswas, Hannah Horng, Rhea Chitalia, Michael Hirano, Jennifer Xiao, Ralph Corsetti, Sarah Javid, Derrick Spell, Antonio Wolff, Joseph Sparano, Seema Khan, Christopher Comstock, Justin Romanoff, Jon Steingrimsson, Constantine Gatsonis, Constance Lehman, Savannah Partridge, Despina Kontos, Habib Rahbar. MRI Radiomic phenotypes derived from the ECOG-ACRIN E4112 Trial to assess high-risk ductal carcinoma in situ [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-28-10.

Abstract PO2-15-08: Exploring Essential Immune Genes Linked to Pathological Complete Response and Survival in Early-Stage Triple-Negative Breast Cancer (TNBC) after Chemotherapy Treatment

Abstract Background: De-escalation and escalation of systemic therapy in early-stage TNBC will require the implementation of biomarkers to guide therapy. To date, high expression of a variety of immune genes has been associated with higher pathological complete response (pCR) rates following neoadjuvant chemotherapy or with improved survival, or both, in early-stage TNBC. However, it is currently unknown which are the key genes influencing survival (i.e., the Core Immune Genes [CIG]) and if combinations of these genes can improve their association with both clinical endpoints. Methods: The expression of up to 185 genes was evaluated on pre-treatment tumor biopsies from 3 independent publicly available datasets of early-stage TNBC: CALGB40603 (n=388) (NCT00861705), BrighTNess (n=482) (NCT02032277), and SCAN-B (n=498). Patients in CALGB40603 and BrighTNess were treated with neoadjuvant chemotherapy-based therapy and had response data. Patients in CALGB40603 and SCAN-B had survival outcome data (event-free survival [EFS] or relapse-free interval [RFi]). Each individual gene was associated with each clinical endpoint within each dataset using Cox model and logistic regression analyses, adjusting for treatment arm. Genes found in common across datasets were selected as CIG, except for PDCD1, which was not available in BrighTNess. Using the CIGs, we evaluated whether combinations of 2 genes enhance their association with each clinical endpoint using different methods: adding, subtracting, multiplying, and ratio. Likelihood ratio testing was used to evaluate the added contribution of each gene in 2 combined cohorts evaluating pCR (i.e., CALGB40603 and BrighTNess) and survival (i.e., CALGB40603 and SCAN-B). Results: 10 CIGs related to B-cells and T-cells (i.e., IRF4, LAX1, POU2AF1, CD274, CD79A, TNFRSF17, PIM2, PDCD1, CXCR6, and SLAMF1) were associated with higher pCR rates and improved survival outcome across datasets. Of note, 5 (50%) genes are known components of the 14-gene immunoglobulin/B-cell (IGG) signature. The correlation coefficient among the 10 CIG genes was 0.63 (moderate). In general, the sum of 2 CIGs provided more prognostic information than a single gene (p< 0.001). Specifically, the sum of 2 CIGs provides more prognostic information than a single gene in 30 of 45 (66.6%) cases. Two-gene subtraction, multiplication or ratio decreased the amount of prognostic information (p< 0.001). The top 10 2-gene CIGs associated with prognosis (p< 0.001) were CD274_TNFRSF17, IRF4_CD274, PDCD1_TNFRSF17, CD79A_CD274, CD274_POU2AF1, CXCR6_TNFRSF17, LAX1_TNFRSF17, SLAMF1_TNFRSF17, CD274_LAX1 and CD79A_CXCR6. In general, the sum of 2 CIGs provided more information about pCR than a single gene (p< 0.001). Specifically, the sum of 2 CIGs provided numerically more information about pCR than 1 gene in 17 of 36 (47.2%) cases. Two-gene subtraction, multiplication or ratio decreased the amount of information about pCR (p< 0.001). The top 10 2-gene CIGs associated with pCR were CD274_PIM2, CD274_POU2AF1, CXCR6_PIM2, CD274_CXCR6, IRF4_CD274, CXCR6_POU2AF1, CD274_LAX1, IRF4_CXCR6, CXCR6_LAX1, and POU2AF1_SLAMF1. Conclusion: The expression of CIGs associated with B-cell and T-cell adaptive immune response is closely linked to pCR and survival outcomes following (neo)adjuvant chemotherapy. When combining the expression of 2 specific CIGs through addition, we found that this method provides more informative insights into pCR and survival outcomes compared to other combination methods. This approach enhances the predictive power of CIGs, enabling a better understanding of treatment response and patient prognosis, ultimately contributing to improved clinical decision-making and patient outcomes. Citation Format: Fara Brasó-Maristany, Laia Paré, Benedetta Conte, Laura Angelats, Ana Vivancos, Joel S Parker, Charles M. Perou, Patricia Villagrasa, Aleix Prat. Exploring Essential Immune Genes Linked to Pathological Complete Response and Survival in Early-Stage Triple-Negative Breast Cancer (TNBC) after Chemotherapy Treatment [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-15-08.

Correction to: A general near-exact distribution theory for the most common likelihood ratio test statistics used in Multivariate Analysis

Correction to: A general near-exact distribution theory for the most common likelihood ratio test statistics used in Multivariate Analysis

Abstract PO1-25-05: Adipose-enriched peri-tumoral stroma prognosticates poorer survival in breast cancers

Abstract Background: Adjuvant breast cancer therapy is informed by whether a tumour is positive or negative for the biomarkers ER, PgR, and HER2, often without regard to level of positivity. Quantitation has been proposed to improve therapeutic management. Adjunctive statistical standardization has been proposed to improve inter-laboratory comparability of biomarkers results. Methods: This primary report utilized adjunctive statistical standardization of machine-quantitated image analysis biomarker assessments. CCTG MA.27 (NCT00066573) is an adjuvant phase III trial of exemestane versus anastrozole in postmenopausal women with ER+ and/or PgR+ tumours. IHC ER, PgR, and HER2 were centrally assessed, with FISH (HER2;HER2/CEP17) determinations for equivocal IHC HER2. HSCOREs were statistically standardized to a mean of 0, standard deviation of 1 following Box-Cox variance stabilization transformations of square for ER and natural logarithm for PgR (0.1 was added to 0 HSCOREs). The primary endpoint was STEEP distant disease-free survival (DDFS) at the longest trial follow-up of median 4.1 years. Survival was described with Kaplan-Meier plots. The univariate Wilcoxon (Peto-Prentice) test statistic was used with usual designation of negative/positive (0; >0), and standardized cut-points at standard deviations about mean of 0(<-1; (-1,0]; (0,1]; >1). Cox multivariate regressions adjusted for age, T and N stage, grade, lymphovascular invasion, treatment, and baseline patient demographics, utilized likelihood ratio tests. Nominal significance was p=0.05. Results: Of the 7576 women accrued, 3048 had machine-quantitated image analysis results: 2900 (95%) for ER; 2726 (89%) for PgR. Only 8 women were ASCO/CAP ER- (HSCORE 0); PgR HSCORE was 0 for 533. Statistically standardized units differentiated DDFS ER levels (p< 0.001) and PgR levels (p< 0.001). In adjusted multivariate analyses, higher ER HSCORE was associated with better DDFS (p=0.05) with weak evidence of an association (p=0.11) for standardized HSCORE, and no significant association (respectively, p=0.28, p=0.54) in models with PgR. Higher PgR was associated with better DDFS (p=0.001) in all multivariate assessments, including those with ER. Conclusions: DDFS was superior for patients with higher ER and PgR standardized units compared with those with HSCOREs <-1. Adjunctive statistical standardization, similar to that mandated for clinical practice by the World Health Organization for BMD, should improve inter-laboratory comparability of biomarker results for similar patient populations. Biomarker N DDFS DDFS 5-year (%) 95% CI ER total 2900 ER <-1 506 86 (82, 91) ER (-1, 0] 934 94 (92, 96) ER ( 0, 1] 919 94 (92, 96) ER 1 541 96 (93, 98) PgR total 2726 PgR <-1 734 89 (86, 92) PgR (-1, 0] 439 92 (89, 95) PgR ( 0, 1] 967 95 (93, 96) PgR 1.0 586 98 (97,100) Citation Format: Hannah Lau, Veronique Kiak Mien Tan, Benita Kiat Tee Tan, Yirong Sim, Jelmar Quist, Aye Aye Thike, Puay Hoon Tan, Shazib Pervaiz, Anita Grigoriadis, Kanaga Sabapathy. Adipose-enriched peri-tumoral stroma prognosticates poorer survival in breast cancers [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-25-05.

Abstract PO4-08-06: Causal Relationships and Familial Aggregation of Mammogram Risk Scores: Insights from Breast Cancer in Twins and Families

Abstract Background: Mammogram risk scores based on texture and density, determined by brightness thresholds, are associated with breast cancer risk differently and could reveal distinct information about breast cancer risk. This study aimed to investigate the causal relationships between these intercorrelated risk scores and the familial aggregation of a texture-based mammogram measure, shedding light on the relevance to breast cancer aetiology. Methods: We analysed data from the Australian Mammographic Density Twins and Sisters Study, including 3195 breast-cancer-free women from 1519 families. The dataset comprised 527 pairs of monozygotic (MZ) twins, 271 pairs of dizygotic (DZ) twins, and 1599 sisters. The average age at mammography for sister pairs ranged from 40 to 70 years. Mammogram risk scores based on texture (named Cirrus) and three density-defined areas (light, bright, and brightest) that were spatially independent were generated. The Inference about Causation from Examination of Familial Confounding (ICE FALCON) method was employed for causal inference. Familial aggregation and the variance of Cirrus were assessed as a function of age using a multivariate normal model for pedigree analysis. The classic twin model was employed but allowing for varying shared environmental effects among different sister pairs. The single-nucleotide polymorphism (SNP)-based heritability was estimated using genetic variants from both closely and distantly related individuals. Model fits were compared using likelihood ratio tests and the Akaike Information Criterion. Results: Causal analysis using ICE FALCON revealed significant positive causal effects, with Cirrus, light areas, and bright areas influencing the brightest areas (accounting for 34%, 55%, and 85% of the associations, respectively). Similarly, light areas and bright areas demonstrated causal effects on Cirrus (accounting for 37% and 28% of the associations, respectively). No age-related differences in familial correlations or Cirrus variance were found for women aged 40 to 70 years. Familial correlations were estimated at 0.51 (0.03) for MZ pairs and 0.16 (0.03) for combined DZ and non-twin sister pairs, with no significant difference between DZ and non-twin sister pairs (P=0.3). Additive genetic effects accounted for up to 32% (5%) of the variance, consistent with SNP-based heritability estimates of 36% (12%). MZ-specific environmental effects contributed to at least 20% (3%), while shared environmental effects for DZ and non-twin sibling pairs accounted for less than 40% of that for MZ pairs. Conclusion: In a mammogram, the lighter (less dense) areas causally influence the brightest (highly dense) areas, including through textural features. These causal relationships provide insights into the relative importance of different mammogram features in breast cancer aetiology. For example, the brightest areas are more aetiologically important for screen-detected breast cancer, while the light areas are more aetiologically important for interval breast cancer. Additionally, specific textural features capture aetiologically independent breast cancer risk information from dense areas. We also confirm the partial heritability of Cirrus, the texture-based mammogram measure, substantially influenced by environmental factors (familial and non-familial). As well as genetic factors, Cirrus could be determined by factors operating at times of life when MZ pairs share their environments to a greater extent than do DZ and non-twin sisters, starting in utero and prior to adulthood, and remaining constant thereafter. These insights from twin and family data enhance our understanding of mammogram risk scores and their implications for breast cancer risk assessment. Citation Format: Zhoufeng Ye, Shuai Li, Gill Dite, Tuong Nguyen, Robert MacInnis, John Hopper. Causal Relationships and Familial Aggregation of Mammogram Risk Scores: Insights from Breast Cancer in Twins and Families [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-08-06.

How reliable is the soft tissue radiological knee index in predicting superficial surgical site infection following total knee arthroplasty?

The purpose of this study was to evaluate the association between the Soft Tissue Radiological Knee (SToRK) Index and the risk of developing a superficial surgical site infection (SSSI) following primary total knee arthroplasty (TKA). The SToRK Index was measured using calibrated long leg radiographs (LLR) in 174 patients undergoing TKA. A moderate correlation was found between the SToRK Index and body mass index (BMI) (rs=0.574; p < 0.001). The SToRK Index was a better predictor of SSSI in females than males. In females, a SToRK Index cutoff of 2.01 had a sensitivity and specificity of 41.3% and 74.6% for developing SSSI, respectively, with a positive likelihood ratio of 1.63. The SToRK Index can be used as an additional tool in assessing the risk for SSSI after TKA. It might be more predictive in females due to the different fat distribution compared to males.

Abstract PO1-27-10: Adjunctive statistical standardization of quantitated machine image analysis of Estrogen and Progesterone Receptors: CCTG MA.27 trial

Abstract Background: Adjuvant breast cancer therapy is informed by whether a tumour is positive or negative for the biomarkers ER, PgR, and HER2, often without regard to level of positivity. Quantitation has been proposed to improve therapeutic management. Adjunctive statistical standardization has been proposed to improve inter-laboratory comparability of biomarkers results. Methods: This primary report utilized adjunctive statistical standardization of machine-quantitated image analysis biomarker assessments. CCTG MA.27 (NCT00066573) is an adjuvant phase III trial of exemestane versus anastrozole in postmenopausal women with ER+ and/or PgR+ tumours. IHC ER, PgR, and HER2 were centrally assessed, with FISH (HER2;HER2/CEP17) determinations for equivocal IHC HER2. HSCOREs were statistically standardized to a mean of 0, standard deviation of 1 following Box-Cox variance stabilization transformations of square for ER and natural logarithm for PgR (0.1 was added to 0 HSCOREs). The primary endpoint was STEEP distant disease-free survival (DDFS) at the longest trial follow-up of median 4.1 years. Survival was described with Kaplan-Meier plots. The univariate Wilcoxon (Peto-Prentice) test statistic was used with usual designation of negative/positive (0; &amp;gt;0), and standardized cut-points at standard deviations about mean of 0(&amp;lt;-1; (-1,0]; (0,1]; &amp;gt;1). Cox multivariate regressions adjusted for age, T and N stage, grade, lymphovascular invasion, treatment, and baseline patient demographics, utilized likelihood ratio tests. Nominal significance was p=0.05. Results: Of the 7576 women accrued, 3048 had machine-quantitated image analysis results: 2900 (95%) for ER; 2726 (89%) for PgR. Only 8 women were ASCO/CAP ER- (HSCORE 0); PgR HSCORE was 0 for 533. Statistically standardized units differentiated DDFS ER levels (p&amp;lt; 0.001) and PgR levels (p&amp;lt; 0.001). In adjusted multivariate analyses, higher ER HSCORE was associated with better DDFS (p=0.05) with weak evidence of an association (p=0.11) for standardized HSCORE, and no significant association (respectively, p=0.28, p=0.54) in models with PgR. Higher PgR was associated with better DDFS (p=0.001) in all multivariate assessments, including those with ER. Conclusions: DDFS was superior for patients with higher ER and PgR standardized units compared with those with HSCOREs &amp;lt;-1. Adjunctive statistical standardization, similar to that mandated for clinical practice by the World Health Organization for BMD, should improve inter-laboratory comparability of biomarker results for similar patient populations. Biomarker N DDFS DDFS 5-year (%) 95% CI ER total 2900 ER &amp;lt;-1 506 86 (82, 91) ER (-1, 0] 934 94 (92, 96) ER ( 0, 1] 919 94 (92, 96) ER &amp;gt;1 541 96 (93, 98) PgR total 2726 PgR &amp;lt;-1 734 89 (86, 92) PgR (-1, 0] 439 92 (89, 95) PgR ( 0, 1] 967 95 (93, 96) PgR &amp;gt;1.0 586 98 (97,100) Citation Format: Judy-Anne Chapman, Jane Bayani, Sandip SenGupta, John MS Bartlett, Tammy Piper, Mary Anne Quintayo, Shakeel Virk, Paul Goss, James Ingle, Matthew Ellis, George Sledge Jr, George Budd, Manuela Rabaglio, Rafat Ansari, Richard Tozer, David D'Souza, Haji Chalchal, Silvana Spadafora, Vered Stearns, Edith A. Perez, Karen Gelmon, Timothy Whelan, Catherine Elliott, Lois Shepherd, Bingshu Chen, Karen Taylor. Adjunctive statistical standardization of quantitated machine image analysis of Estrogen and Progesterone Receptors: CCTG MA.27 trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-27-10.

Discriminative diagnosis of ovarian endometriosis cysts and benign mucinous cystadenomas based on the ConvNeXt algorithm

PurposeThe objective of this study was to develop a deep learning model, using the ConvNeXt algorithm, that can effectively differentiate between ovarian endometriosis cysts (OEC) and benign mucinous cystadenomas (MC) by analyzing ultrasound images. The performance of the model in the diagnostic differentiation of these two conditions was also evaluated. MethodsA retrospective analysis was conducted on OEC and MC patients who had sought medical attention at the Fourth Affiliated Hospital of Harbin Medical University between August 2018 and May 2023. The diagnosis was established based on postoperative pathology or the characteristics of aspirated fluid guided by ultrasound, serving as the gold standard. Ultrasound images were collected and subjected to screening and preprocessing procedures. The data set was randomly divided into training, validation, and testing sets in a ratio of 5:3:2. Transfer learning was utilized to determine the initial weights of the ConvNeXt deep learning algorithm, which were further adjusted by retraining the algorithm using the training and validation ultrasound images to establish a new deep learning model. The weights that yielded the highest accuracy were selected to evaluate the diagnostic performance of the model using the validation set. Receiver operating characteristic (ROC) curves were generated, and the area under the curve (AUC) was calculated. Additionally, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, and odds ratio were calculated. Decision curve analysis (DCA) curves were plotted. Results: The study included 786 ultrasound images from 184 patients diagnosed with either OEC or MC. The deep learning model achieved an AUC of 0.90 (95 % CI: 0.85–0.95) in accurately distinguishing between the two conditions, with a sensitivity of 90 % (95 % CI: 84 %-95 %), specificity of 90 % (95 % CI: 77 %-97 %), a positive predictive value of 96 % (95 % CI: 91 %-99 %), a negative predictive value of 77 % (95 % CI: 63 %-88 %), a positive likelihood ratio of 9.27 (95 % CI: 3.65–23.56), and a negative likelihood ratio of 0.11 (95 % CI: 0.06–0.19). The DCA curve demonstrated the practical clinical utility of the model. ConclusionsThe deep learning model developed using the ConvNeXt algorithm exhibits high accuracy (90 %) in distinguishing between OEC and MC. This model demonstrates excellent diagnostic performance and clinical utility, providing a novel approach for the clinical differentiation of these two conditions.

Association of distal radial fracture with comorbidities: model development and validation

BackgroundTo better assess the risk of distal radial fracture in the general population, we need models that take into account a wide range of risk factors other than osteoporosis. The objective was to develop and validate a model for association of patients’ characteristics with distal radial fracture that effectively incorporates multifactorial aspects and includes comorbidities. MethodWe analyzed data from a large Longitudinal Health Insurance Database between 2000 and 2013. The outcome of the study was the occurrence of distal radial fracture and the predictors were demographic and comorbidity data. Two machine learning models were developed and validated for patients ≥50 (N = 2745) and <50 (N = 1587) years of age. ResultsFor patients aged ≥50 years, selected characteristics included sex, age, urbanization level, osteoarthritis, carpal tunnel syndrome, obesity, hyperlipidemia, trigger finger, hypertension, hypothyroidism, diabetes, hyperthyroidism, and rheumatoid arthritis. For patients <50 years old, selected characteristics included age, sex, diabetes mellitus, urbanization level, carpal tunnel syndrome, hyperlipidemia, osteoarthritis, obesity, and hypertension. Accuracy, sensitivity, specificity, area under the curve, and likelihood ratio were 0.77, 0.83, 0.72, 0.77, and 2.92 for age ≥50 years and 0.73, 0.79, 0.67, 0.73, and 2.41 for age <50 years. ConclusionThe study models can serve as reliable screening tools to assess the risk of distal radial fracture in the general population before bone mineral density testing. In addition, they can be integrated into decision support systems to help healthcare providers identify high-risk patients for additional evaluation and education, ultimately improving the quality of care.

Accuracy and Cut-Off Values of Cepstral Measures in the Clinical Evaluation of Brazilian Portuguese Speakers

OBJECTIVETo identify the accuracy and cut-off values of the Cepstral Peak Prominence (CPP) and Cepstral Peak Prominence-Smoothed (CPPS) obtained from different speech tasks, to identify dysphonic voices in Brazilian Portuguese speakers, and to verify the correlation between these measures and the overall severity of dysphonia (OS). METHODIn a study with 376 subjects—277 with dysphonia and 99 controls—we recorded four speech tasks and assessed OS with a visual analog scale (VAS). We extracted CPP and CPPS from these recordings and analyzed them using receive operating characteristic (ROC) curves to determine cut-off values and other performance metrics (area under curve, accuracy, sensitivity, specificity, predictive values, and likelihood ratios). RESULTSCPP values below 28.15 dB (sustained vowel [Ɛ]), 28.77 dB (sustained vowel [a]), 28.58 dB (all CAPE-V sentences), and CPPS values below 16.42 dB (sustained vowel [Ɛ]), 17.02 dB (sustained vowel [a]), and 11.30 dB (all CAPE-V sentences) are indicative of the presence of dysphonia. CPPVE, CPPVA, CPPCAPE-V, CPPVS, CPPSVE, CPPSVA, CPPSCount, CPPSCAPE-V e CPPSVS, and CPPSVS can explain the variability of OS from 19% to 57.9%. CONCLUSIONThe cut-off values for CPPVE, CPPVA, CPPCAPE-V, CPPSVE, CPPSCAPE-V, and CPPSVA obtained from Praat software can be used as a reference for the clinical voice assessment of Brazilian Portuguese speakers in the respective speech tasks. Analysis of the correlation coefficients of the cepstral measures investigated provided us with a predictive model of OS perception based on each measure/speech task.

Diagnostic Accuracy of Transthoracic Echocardiography for Acute Type A Aortic Syndrome: A Systematic Review and Meta-Analysis

Transthoracic echocardiography (TTE) is currently recognized as the potential first-line imaging test for patients with suspected acute type A aortic syndrome (AAAS). Direct TTE sign for detecting AAAS is positive if there is an intimal flap separating two aortic lumens or aortic wall thickening seen in the ascending aorta. Indirect TTE sign indicates high-risk features of AAAS, such as aortic root dilatation, pericardial effusion, and aortic regurgitation. Our aim is to summarize the existing clinical evidence regarding the diagnostic accuracy of TTE and to evaluate its potential role in the management of patients with suspected AAAS. We included prospective or retrospective diagnostic cohort studies, written in any language, that specifically focused on using TTE to diagnose AAAS from databases such as PubMed, EMBASE, MEDLINE, and the Cochrane Library. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio [1], and hierarchical summary receiver-operating characteristic (HSROC) curve were calculated for TTE in diagnosing AAAS. We applied Quality Assessment of Diagnostic Accuracy (QUADAS-2) tool and Grading of Recommendations, Assessment, Development and Evaluation (GRADE) quality assessment criteria. Ten studies (2886 patients) were included in the meta-analysis. The pooled sensitivity and specificity of direct TTE signs were 58% (95% CI, 38-76%) and 94% (95% CI, 89-97%). For any TTE signs, the pooled sensitivity and specificity were 91% (95% CI, 85-94%) and 74% (95% CI, 61-84%). The diagnostic accuracy of direct TTE signs was significantly higher than that of any TTE signs, as measured by the area under the HSROC curve [0.95 (95% CI, 0.92-0.96) vs. 0.87 (95% CI, 0.84-0.90)] in four studies. Our study suggests that TTE could serve as the initial imaging test for patients with suspected AAAS. Given its high specificity, the presence of direct TTE signs may indicate AAAS, whereas the absence of any TTE signs, combined with low clinical suspicion, could suggest a lower likelihood of AAAS.

Blood miRNAs as Potential Diagnostic Biomarkers for Chronic Obstructive Pulmonary Disease: A Meta-Analysis.

Investigate the efficacy of blood microRNAs (miRNAs) as diagnostic biomarkers for Chronic Obstructive Pulmonary Disease (COPD). We conducted a comprehensive search in English and Chinese databases, selecting studies based on predetermined criteria. Diagnostic parameters like summarized sensitivity (SSEN), summarized specificity (SSPE), summarized positive likelihood ratio (SPLR), summarized negative likelihood ratio (SNLR), and diagnostic odds ratio (DOR), and area under the curve (AUC) of the summary receiver operating characteristic (SROC) curves were analyzed using a bivariate model. Each parameter was accompanied by a 95% confidence interval (CI). Eighteen high-quality studies were included. For diagnosing COPD with blood miRNAs, the SSEN was 0.83 (95% CI 0.76-0.89), SSPE 0.76 (95% CI 0.70-0.82), SPLR 3.50 (95% CI 2.66-4.60), SNLR 0.22 (95% CI 0.15-0.33), DOR 15.72 (95% CI 8.58-28.77), and AUC 0.86 (95% CI 0.82-0.88). In acute exacerbations, SSEN was 0.85 (95% CI 0.76-0.91), SSPE 0.80 (95% CI 0.73-0.86), SPLR 4.26 (95% CI 3.05-5.95), SNLR 0.19 (95% CI 0.12-0.30), DOR 22.29 (95% CI 11.47-43.33), and AUC 0.89 (95% CI 0.86-0.91). Blood miRNAs demonstrate significant accuracy in diagnosing COPD, both in general and during acute exacerbations, suggesting their potential as reliable biomarkers.

Reprint of: The likelihood ratio test for structural changes in factor models

A factor model with a break in its factor loadings is observationally equivalent to a model without changes in the loadings but with a change in the variance of its factors. This approach effectively transforms a high-dimensional structural change problem into a low-dimensional problem. This paper considers the likelihood ratio (LR) test for a variance change in the estimated factors. The LR test implicitly explores a special feature of the estimated factors: the pre-break and post-break variances can be a singular matrix under the alternative hypothesis, making the LR test diverging faster and thus more powerful than Wald-type tests. The better power property of the LR test is also confirmed by simulations. We also consider mean changes and multiple breaks. We apply this procedure to the factor modeling of the US employment and study the structural change problem using monthly industry-level data.

Likelihood Asymptotics in Nonregular Settings: A Review with Emphasis on the Likelihood Ratio

Likelihood Asymptotics in Nonregular Settings: A Review with Emphasis on the Likelihood Ratio

Empirical likelihood ratio tests for non-nested model selection based on predictive losses

Empirical likelihood ratio tests for non-nested model selection based on predictive losses