Training Cohort Research Articles

Development and Validation of a Prognostic Nomogram for Breast Cancer Patients With Multi-Organ Metastases: An Analysis of the Surveillance, Epidemiology, and End Results Program Database.

Multi-organ metastases represent a substantial life-threatening risk for breast cancer (BC) patients. Nonetheless, the current dearth of assessment tools for patients with multi-organ metastatic BC adversely impacts their evaluation. We conducted a retrospective analysis of BC patients with multi-organ metastases using data from the SEER database from 2010 to 2019. The patients were randomly allocated into a training cohort and a validation cohort in a 7:3 ratio. Univariate COX regression analysis, the LASSO, and multivariate Cox regression analyses were performed to identify independent prognostic factors in the training set. Based on these factors, a nomogram was constructed to estimate overall survival (OS) probability for BC patients with multi-organ metastases. The performance of the nomogram was evaluated using C-indexes, ROC curves, calibration curves, decision curve analysis (DCA) curves, and the risk classification system for validation. A total of 3626 BC patients with multi-organ metastases were included in the study, with 2538 patients in the training cohort and 1088 patients in the validation cohort. Age, grade, metastasis location, surgery, chemotherapy, and subtype were identified as significant independent prognostic factors for OS in BC patients with multi-organ metastases. A nomogram for predicting 1-year, 3-year, and 5-year OS was constructed. The evaluation metrics, including C-indexes, ROC curves, calibration curves, and DCA curves, demonstrated the excellent predictive performance of the nomogram. Additionally, the risk grouping system effectively stratified BC patients with multi-organ metastases into distinct prognostic categories. The developed nomogram showed high accuracy in predicting the survival probability of BC patients with multi-organ metastases, providing valuable information for patient counseling and treatment decision making.

Intertumoral Heterogeneity Based on MRI Radiomic Features Estimates Recurrence in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) heterogeneity impacts prognosis, and imaging is a potential indicator. To characterize HCC image subtypes in MRI and correlate subtypes with recurrence. Retrospective. A total of 440 patients (training cohort = 213, internal test cohort = 140, external test cohort = 87) from three centers. 1.5-T/3.0-T, fast/turbo spin-echo T2-weighted, spin-echo echo-planar diffusion-weighted, contrast-enhanced three-dimensional gradient-recalled-echo T1-weighted with extracellular agents (Gd-DTPA, Gd-DTPA-BMA, and Gd-BOPTA). Three-dimensional volume-of-interest of HCC was contoured on portal venous phase, then coregistered with precontrast and late arterial phases. Subtypes were identified using non-negative matrix factorization by analyzing radiomics features from volume-of-interests, and correlated with recurrence. Clinical (demographic and laboratory data), pathological, and radiologic features were compared across subtypes. Among clinical, radiologic features and subtypes, variables with variance inflation factor above 10 were excluded. Variables (P < 0.10) in univariate Cox regression were included in stepwise multivariate analysis. Three recurrence estimation models were built: clinical-radiologic model, subtype model, hybrid model integrating clinical-radiologic characteristics, and subtypes. Mann-Whitney U test, Kruskal-Wallis H test, chi-square test, Fisher's exact test, Kaplan-Meier curves, log-rank test, concordance index (C-index). Significance level: P < 0.05. Two subtypes were identified across three cohorts (subtype 1:subtype 2 of 86:127, 60:80, and 36:51, respectively). Subtype 1 showed higher microvascular invasion (MVI)-positive rates (53%-57% vs. 26%-31%), and worse recurrence-free survival. Hazard ratio (HR) for the subtype is 6.10 in subtype model. Clinical-radiologic model included alpha-fetoprotein (HR: 3.01), macrovascular invasion (HR: 2.32), nonsmooth tumor margin (HR: 1.81), rim enhancement (HR: 3.13), and intratumoral artery (HR: 2.21). Hybrid model included alpha-fetoprotein (HR: 2.70), nonsmooth tumor margin (HR: 1.51), rim enhancement (HR: 3.25), and subtypes (HR: 5.34). Subtype model was comparable to clinical-radiologic model (C-index: 0.71-0.73 vs. 0.71-0.73), but hybrid model outperformed both (C-index: 0.77-0.79). MRI radiomics-based clustering identified two HCC subtypes with distinct MVI status and recurrence-free survival. Hybrid model showed superior capability to estimate recurrence. 3 TECHNICAL EFFICACY STAGE: 2.

Deep learning-based whole-body PSMA PET/CT attenuation correction utilizing Pix-2-Pix GAN.

Sequential PET/CT studies oncology patients can undergo during their treatment follow-up course is limited by radiation dosage. We propose an artificial intelligence (AI) tool to produce attenuation-corrected PET (AC-PET) images from non-attenuation-corrected PET (NAC-PET) images to reduce need for low-dose CT scans. A deep learning algorithm based on 2D Pix-2-Pix generative adversarial network (GAN) architecture was developed from paired AC-PET and NAC-PET images. 18F-DCFPyL PSMA PET-CT studies from 302 prostate cancer patients, split into training, validation, and testing cohorts (n = 183, 60, 59, respectively). Models were trained with two normalization strategies: Standard Uptake Value (SUV)-based and SUV-Nyul-based. Scan-level performance was evaluated by normalized mean square error (NMSE), mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Lesion-level analysis was performed in regions-of-interest prospectively from nuclear medicine physicians. SUV metrics were evaluated using intraclass correlation coefficient (ICC), repeatability coefficient (RC), and linear mixed-effects modeling. Median NMSE, MAE, SSIM, and PSNR were 13.26%, 3.59%, 0.891, and 26.82, respectively, in the independent test cohort. ICC for SUVmax and SUVmean were 0.88 and 0.89, which indicated a high correlation between original and AI-generated quantitative imaging markers. Lesion location, density (Hounsfield units), and lesion uptake were all shown to impact relative error in generated SUV metrics (all p < 0.05). The Pix-2-Pix GAN model for generating AC-PET demonstrates SUV metrics that highly correlate with original images. AI-generated PET images show clinical potential for reducing the need for CT scans for attenuation correction while preserving quantitative markers and image quality.

Intraoperative quantitative crystalline lens nuclear opacities analysis based on crystalline lenSx platform

PurposeThe main objective is to quantify the lens nuclear opacity using spectral-domain optical coherence tomography (SD-OCT) and to evaluate its association with Lens Opacities Classification System III (LOCS-III) system, lens thickness (LT), and surgical parameters. The secondary objective is to assess the diagnostic model performance for hard nuclear cataract.MethodsThis study included 70 eyes of 57 adults with cataract, with 49 (70%) and 21 (30%) in training and validation cohort, respectively. Correlations of the average nuclear density (AND) /maximum nuclear density (MND) with LOCS-III scores, LT, and surgical parameters were analyzed. Univariate and multivariate logistic regression analysis, receiver operating characteristic curves and calibration curves were performed for the diagnostic of hard nuclear cataract.ResultsThe pre-operative uncorrected distance visual acuity (UDVA), intraocular pressure (IOP), mean axial length (AL), and LT were 1.20 ± 0.47 log MAR, 15.50 ± 2.87 mmHg, 27.34 ± 3.77 mm and 4.32 ± 0.45 mm, respectively. The average nuclear opalescence (NO) and nuclear colour (NC) scores were 3.61 ± 0.94 and 3.50 ± 0.91 (ranging from 1.00 to 6.90), respectively. The average AND and MND were 137.94 ± 17.01 and 230.01 ± 8.91, respectively. NC and NO scores both significantly correlated with the AND (rNC = 0.733, p = 0.000; rNO = 0.755, p = 0.000) and MND (rNC = 0.643, p = 0.000; rNO = 0.634, p = 0.000). In the training cohort, the area under the curve (AUC) of the model was 0.769 (P < 0.001, 95%CI 0.620–0.919), which had a good degree of differentiation (Fig. 2a). The calibration curve showed good agreement between predicted and actual probability.ConclusionThe nuclear density measurement on SD-OCT images can serve as an objective and reliable indicator for quantifying nuclear density.

Development and validation of a nomogram to predicting the efficacy of PD-1/PD-L1 inhibitors in patients with nasopharyngeal carcinoma.

With the treatment of nasopharyngeal carcinoma (NPC) by PD-1/PD-L1 inhibitors used widely in clinic, it becomes very necessary to anticipate whether patients would benefit from it. We aimed to develop a nomogram to evaluate the efficacy of anti-PD-1/PD-L1 in NPC patients. Totally 160 NPC patients were enrolled in the study. Patients were measured before the first PD-1/PD-L1 inhibitors treatment and after 8-12weeks of immunotherapy by radiological examinations to estimate the effect. The least absolute shrinkage and selection operator (LASSO) logistic regression was used to screen hematological markers and establish a predictive model. The nomogram was internally validated by bootstrap resampling and externally validated. Performance of the model was evaluated using concordance index, calibration curve, decision curve analysis and receiver operation characteristic curve. Patients involved were randomly split into training cohort ang validation cohort. Based on Lasso logistic regression, systemic immune-inflammation index (SII) and ALT to AST ratio (LSR) were selected to establish a predictive model. The C-index of training cohort and validating cohort was 0.745 and 0.760. The calibration curves and decision curves showed the precise predictive ability of this nomogram. The benefit of the model showed in decision curve was better than TNM stage. The area under the curve (AUC) value of training cohort and validation cohort was 0.745 and 0.878, respectively. The predictive model helped evaluating efficacy with high accuracy in NPC patients treated with PD-1/PD-L1 inhibitors.

Development and validation of a nomogram for predicting specific mortality risk: A study of competing risk model based on real endometrial cancer patients.

This study aimed to construct a competing risk prediction model for predicting specific mortality risks in endometrial cancer patients from the SEER database based on their demographic characteristics and tumor information. We collected relevant clinical data on patients with histologically confirmed endometrial cancer in the SEER database between 2010 and 2015. Univariate and multivariate competing risk models were used to analyze the risk factors for endometrial cancer-specific death, and a predictive nomogram was constructed. C-index and receiver operating characteristic curve (ROC) at different time points were used to verify the accuracy of the constructed nomogram. There were 26 109 eligible endometrial cancer patients in the training cohort and 11 189 in the validation cohort. Univariate and multivariate analyses revealed that Age, Marriage, Grade, Behav, FIGO, Size, Surgery, SurgOth, Radiation, ParaAortic_Nodes, Peritonea, N positive, DX_liver, and DX_lung were independent prognostic factors for specific mortality in endometrial cancer patients. Based on these factors, a nomogram was constructed. Internal validation showed that the nomogram had a good discriminative ability (C-index = 0.883 [95% confidence interval [CI]: 0.881-0.884]), and the 1-, 3-, and 5-year AUC values were 0.901, 0.886 and 0.874, respectively. External validation indicated similar results (C-index = 0.883 [95%CI: 0.882-0.883]), and the 1-, 3-, and 5- AUC values were 0.908, 0.885 and 0.870, respectively. We constructed a competing risk model to predict the specific mortality risk among endometrial cancer patients. This model has favorable accuracy and reliability and can provide a reference for the development and update of endometrial cancer prognostic risk assessment tools.

Abnormal coagulation after hepatectomy in patients with normal preoperative coagulation function

BackgroundTo explore the risk factors for postoperative abnormal coagulation (PAC) and establish a predictive model for patients with normal preoperative coagulation function who underwent hepatectomy.Materials and MethodsA total of 661 patients with normal preoperative coagulation function who underwent hepatectomy between January 2015 and December 2021 at the First Affiliated Hospital of Sun Yat-sen University were divided into two groups: the postoperative abnormal coagulation group (PAC group, n = 362) and the normal coagulation group (non-PAC group, n = 299). Univariate and multivariate logistic analyses were used to identify the risk factors for PAC.ResultsThe incidence of PAC in 661 patients who underwent hepatectomy was 54.8% (362/661). The least absolute shrinkage and selection operator (LASSO) method was used for multivariate logistic regression analysis. The preoperative international normalized ratio (INR), intraoperative succinyl gelatin infusion and major hepatectomy were found to be independent risk factors for PAC. A nomogram for predicting the PAC after hepatectomy was constructed. The model presented a receiver operating characteristic (ROC) curve of 0.742 (95% confidence interval (CI): 0.697–0.786) in the training cohort. The validation set demonstrated a promising ROC of 0.711 (95% CI: 0.639–0.783), and the calibration curve closely approximated the true incidence. Decision curve analysis (DCA) was performed to assess the clinical usefulness of the predictive model. The risk of PAC increased when the preoperative international normalized ratio (INR) was greater than 1.025 and the volume of intraoperative succinyl gelatin infusion was greater than 1500 ml.ConclusionThe PAC is closely related to the preoperative INR, intraoperative succinyl gelatin infusion and major hepatectomy. A three-factor prediction model was successfully established for predicting the PAC after hepatectomy.

A novel multidisciplinary machine learning approach based on clinical, imaging, colonoscopy, and pathology features for distinguishing intestinal tuberculosis from Crohn's disease.

Differentiating intestinal tuberculosis (ITB) from Crohn's disease (CD) remains a diagnostic dilemma. Misdiagnosis carries potential grave implications. We aim to establish a multidisciplinary-based model using machine learning approach for distinguishing ITB from CD. Eighty-two patients including 25 patients with ITB and 57 patients with CD were retrospectively recruited (54 in training cohort and 28 in testing cohort). The region of interest (ROI) for the lesion was delineated on magnetic resonance enterography (MRE) and colonoscopy images. Radiomic features were extracted by least absolute shrinkage and selection operator regression. Pathological feature was extracted automatically by deep-learning method. Clinical features were filtered by logistic regression analysis. Diagnostic performance was evaluated by receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Delong's test was applied to compare the efficiency between the multidisciplinary-based model and the other four single-disciplinary-based models. The radiomics model based on MRE features yielded an AUC of 0.87 (95% confidence interval [CI] 0.68-0.96) on the test data set, which was similar to the clinical model (AUC, 0.90 [95% CI 0.71-0.98]) and higher than the colonoscopy radiomics model (AUC, 0.68 [95% CI 0.48-0.84]) and pathology deep-learning model (AUC, 0.70 [95% CI 0.49-0.85]). Multidisciplinary model, integrating 3 clinical, 21 MRE radiomic, 5 colonoscopy radiomic, and 4 pathology deep-learning features, could significantly improve the diagnostic performance (AUC of 0.94, 95% CI 0.78-1.00) on the bases of single-disciplinary-based models. DCA confirmed the clinical utility. Multidisciplinary-based model integrating clinical, MRE, colonoscopy, and pathology features was useful in distinguishing ITB from CD.

A Prognostic Model Based on the Log Odds Ratio of Positive Lymph Nodes Predicts Prognosis of Patients with Rectal Cancer.

This study aimed to compare the prognostic value of rectal cancer by comparing different lymph node staging systems, and a nomogram was constructed based on superior lymph node staging. Overall, 8700 patients with rectal cancer was obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. The area under the curve (AUC), the C index, and the Akaike informativeness criteria (AIC) were used to examine the predict ability of various lymph node staging methods. Prognostic indicators were assessed using univariate and multivariate COX regression, and further correlation nomograms were created after the data were randomly split into training and validation cohorts. To evaluate the effectiveness of the model, the C index, calibration curves, decision curves (DCA), and receiver operating characteristic curve (ROC) were used. We ran Kaplan-Meier survival analyses to look for variations in risk classification. While compared to the N-stage positive lymph node ratio (LNR), the log odds ratio of positive lymph nodes (LODDS) had the highest predictive effectiveness. Multifactorial COX regression analyses were used to create nomograms for overall survival (OS) and cancer-specific survival (CSS). The C indices of OS and CSS for this model were considerably higher than those for TNM staging in the training cohort. The created nomograms demonstrated good efficacy based on ROC, rectification, and decision curves. Kaplan-Meier survival analysis revealed notable variations in patient survival across various patient strata. Compared to AJCC staging, the LODDS-based nomograms have a more accurate predictive effectiveness in predicting OS and CSS in patients with rectal cancer.

Classification of multiple primary lung cancer in patients with multifocal lung cancer: assessment of a machine learning approach using multidimensional genomic data

BackgroundMultiple primary lung cancer (MPLC) is an increasingly well-known clinical phenomenon. However, its molecular characterizations are poorly understood, and still lacks of effective method to distinguish it from intrapulmonary metastasis (IM). Herein, we propose an identification model based on molecular multidimensional analysis in order to accurately optimize treatment.MethodsA total of 112 Chinese lung cancers harboring at least two tumors (n = 270) were enrolled. We retrospectively selected 74 patients with 121 tumor pairs and randomly divided the tumor pairs into a training cohort and a test cohort in a 7:3 ratio. A novel model was established in training cohort, optimized for MPLC identification using comprehensive genomic profiling analyzed by a broad panel with 808 cancer-related genes, and evaluated in the test cohort and a prospective validation cohort of 38 patients with 112 tumors.ResultsWe found differences in molecular characterizations between the two diseases and rigorously selected the characterizations to build an identification model. We evaluated the performance of the classifier using the test cohort data and observed an 89.5% percent agreement (PA) for MPLC and a 100.0% percent agreement for IM. The model showed an excellent area under the curve (AUC) of 0.947 and a 91.3% overall accuracy. Similarly, the assay achieved a considerable performance in the independent validation set with an AUC of 0.938 and an MPLC predictive value of 100%. More importantly, the MPLC predictive value of the classification achieved 100% in both the test set and validation cohort. Compared to our previous mutation-based method, the classifier showed better κ consistencies with clinical classification among all 112 patients (0.84 vs. 0.65, p &amp;lt;.01).ConclusionThese data provide novel evidence of MPLC-specific genomic characteristics and demonstrate that our one-step molecular classifier can accurately classify multifocal lung tumors as MPLC or IM, which suggested that broad panel NGS may be a useful tool for assisting with differential diagnoses.

PSMA-positive prostatic volume prediction with deep learning based on T2-weighted MRI.

High PSMA expression might be correlated with structural characteristics such as growth patterns on histopathology, not recognized by the human eye on MRI images. Deep structural image analysis might be able to detect such differences and therefore predict if a lesion would be PSMA positive. Therefore, we aimed to train a neural network based on PSMA PET/MRI scans to predict increased prostatic PSMA uptake based onthe axial T2-weighted sequence alone. All patients undergoing simultaneous PSMA PET/MRI for PCa staging or biopsy guidance between April 2016 and December 2020 at our institution were selected. To increase the specificity of our model, the prostatic beds on PSMA PET scans were dichotomized in positive and negative regions using an SUV threshold greater than 4 to generate a PSMA PET map. Then, a C-ENet was trained on the T2 images of the training cohort to generate a predictive prostatic PSMA PET map. One hundred and fifty-four PSMA PET/MRI scans were available (133 [68Ga]Ga-PSMA-11 and 21 [18F]PSMA-1007). Significant cancer was present in 127 of them. The whole dataset was divided into a training cohort (n = 124) and a test cohort (n = 30). The C-ENet was able to predict the PSMA PET map with a dice similarity coefficient of 69.5 ± 15.6%. Increased prostatic PSMA uptake on PET might be estimated based on T2 MRI alone. Further investigation with larger cohorts and external validation is needed to assess whether PSMA uptake can be predicted accurately enough to help in the interpretation of mpMRI.

Cholangiocarcinoma combined with biliary obstruction: an exosomal circRNA signature for diagnosis and early recurrence monitoring

Cholangiocarcinoma (CCA) is a highly malignant biliary tract cancer with currently suboptimal diagnostic and prognostic approaches. We present a novel system to monitor CCA using exosomal circular RNA (circRNA) via serum and biliary liquid biopsies. A pilot cohort consisting of patients with CCA-induced biliary obstruction (CCA-BO, n = 5) and benign biliary obstruction (BBO, n = 5) was used to identify CCA-derived exosomal circRNAs through microarray analysis. This was followed by a discovery cohort (n = 20) to further reveal a CCA-specific circRNA complex (hsa-circ-0000367, hsa-circ-0021647, and hsa-circ-0000288) in both bile and serum exosomes. In vitro and in vivo studies revealed the three circRNAs as promoters of CCA invasiveness. Diagnostic and prognostic models were established and verified by two independent cohorts (training cohort, n = 184; validation cohort, n = 105). An interpreter-free diagnostic model disclosed the diagnostic power of biliary exosomal circRNA signature (Bile-DS, AUROC = 0.947, RR = 6.05) and serum exosomal circRNA signature (Serum-DS, AUROC = 0.861, RR = 4.04) compared with conventional CA19-9 (AUROC = 0.759, RR = 2.08). A prognostic model of CCA undergoing curative-intent surgery was established by calculating early recurrence score, verified with bile samples (Bile-ERS, C-index=0.783) and serum samples (Serum-ERS, C-index = 0.782). These models, combined with other prognostic factors revealed by COX-PH model, enabled the establishment of nomograms for recurrence monitoring of CCA. Our study demonstrates that the exosomal triple-circRNA panel identified in both bile and serum samples serves as a novel diagnostic and prognostic tool for the clinical management of CCA.

Abstract PO5-24-10: Clinical significance and immune landscape analyses of the immune related genes based prognostic signature for HER2 positive breast cancer

Abstract Objective: To date, significant breakthrough has yet to be achieved in immunotherapy for HER2-positive breast cancer (HPBC). In this study, we aimed to construct a risk model for predicting prognosis and therapy response and evaluating the tumor immune landscape in HPBC patients. Methods: Differentially expressed immune-related genes (IRGs) were used as input to weighted correlation network analysis (WGCNA) to identify modules of highly correlated gene. An optimal prognostic TPBC-IRGs signature were constructed by least absolute shrinkage and selection operator (LASSO) cox regression. Survival analysis and ROC curves were analyzed to identify the predictive value in a training cohort and external validation cohorts. With three GEO datasets and samples from our center, the anti-HER2 therapeutic efficacy in HPBC patients was predicted through this prognostic model. Additionally, the immunotherapy outcome was forecasted using IMvigor210 dataset. We also investigated the connection between high and low-risk populations and immune checkpoints and analyzed immune infiltration levels of these populations with several algorithms. Ultimately, we develop a combined model and nomogram by integrating the risk score with clinical factors. Results: 11 key genes associated with overall survival (OS) in the blue module were selected for further analysis. A 3-gene signature (CX3CL1, RARB, TANK) based prognostic risk model was developed. The Kaplan-Meier survival curve revealed that patients with high-risk score had shorter OS compared with the low-risk group. The AUC of the training cohort at 1-, 3- and 5-year were 0.718, 0.784 and 0.767. The performance of the risk model was validated with GSE25066 and GSE55348. It’s showed that the risk scores of HPBC who were resistant to trastuzumab were significantly higher than those of patients who were sensitive to trastuzumab. The results indicated that the levels of immune checkpoints expression (such as PD-1, CTLA4 and TIGIT) and immune infiltration were higher in low-risk group than in high-risk group. The combined model showed better prognostic prediction accuracy compared to the clinical model or gene signature alone (1-year AUC=0.910;3-year AUC=0.832;5-year AUC=0.882). Conclusion: The immune gene-based risk profile and combined prognostic model in this study may hold promise for clinical use in predicting the prognosis and treatment efficacy of HPBC patients. figure3. Construction of HPBC-IRGs signature based prognostic model. Citation Format: Xiaofen Li, Wenfen Fu, Yushuai Yu, Jie Zhang, Chuangui Song. Clinical significance and immune landscape analyses of the immune related genes based prognostic signature for HER2 positive 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 PO5-24-10.

Deciphering the role of apoptosis signature on the immune dynamics and therapeutic prognosis in breast cancer: Implication for immunotherapy

Background: In breast cancer oncogenesis, the precise role of cell apoptosis holds untapped potential for prognostic and therapeutic insights. Thus, it is important to develop a model predicated for breast cancer patients’ prognosis and immunotherapy response based on apoptosis-related signature.Methods: Our approach involved leveraging a training dataset from The Cancer Genome Atlas (TCGA) to construct an apoptosis-related gene prognostic model. The model’s validity was then tested across several cohorts, including METABRIC, Sun Yat-sen Memorial Hospital Sun Yat-sen University (SYSMH), and IMvigor210, to ensure its applicability and robustness across different patient demographics and treatment scenarios. Furthermore, we utilized Quantitative Polymerase Chain Reaction (qPCR) analysis to explore the expression patterns of these model genes in breast cancer cell lines compared to immortalized mammary epithelial cell lines, aiming to confirm their differential expression and underline their significance in the context of breast cancer.Results: Through the development and validation of our prognostic model based on seven apoptosis-related genes, we have demonstrated its substantial predictive power for the survival outcomes of breast cancer patients. The model effectively stratified patients into high and low-risk categories, with high-risk patients showing significantly poorer overall survival in the training cohort and across all validation cohorts. Importantly, qPCR analysis confirmed that the genes constituting our model indeed exhibit differential expression in breast cancer cell lines when contrasted with immortalized mammary epithelial cell lines.Conclusion: Our study establishes a groundbreaking prognostic model using apoptosis-related genes to enhance the precision of breast cancer prognosis and treatment, particularly in predicting immunotherapy response.

Abstract PO1-07-07: Radiomics features for distinguishing true recurrence versus new primary tumor following breast-conserving treatment

Abstract Background Despite modern surgical and irradiation techniques, ipsilateral breast tumor recurrence (IBTR) accounts for 5-15% of all cancer recurrences in women treated with breast conservative treatment. Historically, the methods to define true recurrence (TR) and new primary tumor (NP) for IBTR mainly rely on clinical and pathological criteria, limiting the accuracy of the discerption and causing misclassification. This study aimed to develop a preoperative, noninvasive model for distinguishing IBTR by integrating clinicopathological review with paired dynamic contrast-enhanced breast magnetic resonance imaging (DCE-MRI) at diagnosis and following in-breast recurrence. MethodsWe retrospectively extracted radiomics features from MRI to develop a radiomic cohort of IBTR (n =46), among which all patients underwent paired preoperative DCE-MRI. This radiomic cohort was divided into a training cohort (n =27) and a validation cohort (n =19) with stratified random sampling. Classification of IBTR as TR or NP on the basis of tumor location, histologic subtype, estrogen receptor, and HER2 status was set as the gold standard. The least absolute shrinkage and selection operator (LASSO) regression and logistic regression were utilized to perform radiomics feature selection and model training, respectively. The clinical utility of the model was determined via decision curve analysis (DCA). Results We selected three radiomics features (first-order feature Kurtosis from the first postcontrast phase on IBTR DCE-MRI, GLCM feature Imc2 from the first postcontrast phase on IBTR DCE-MRI, and the delta feature Correlation from the precontrast phase of the change between IBTR and primary tumor DCE-MRI) to develop an IBTR-classification predicting radiomic model, which performed well in the validation cohort (AUC 0.867, 95% confidence interval (CI) 0.694-1). Further investigation for sensitivity (73.3%) and specificity (100%) verified a favorable concordance between the radiomic classification and the conventional standard, with a diagnostic accuracy of 79%. Conclusions: Our study demonstrated the feasibility of the radiomics model in predicting IBTR classification and provided preoperative information about the nature of “recurrence”. This might have important implications in surgical approaches and multidisciplinary care for IBTR. Further efforts are needed to improve the reproducibility of radiomics features and models in multiple centers. Citation Format: Feilin Qu, Guan-Hua Su, Jin-Hui Li, Chao You, Zhi-Ming Shao. Radiomics features for distinguishing true recurrence versus new primary tumor following breast-conserving 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 PO1-07-07.

Abstract PO4-02-12: MDA iLobular Prognostic Tool: A Novel Approach for Risk Stratification in Invasive Lobular Carcinoma

Abstract Background: Invasive lobular carcinoma (ILC) is a distinct subtype of breast cancer that differs from the more common invasive ductal carcinoma (IDC) by its unique characteristics that influence prognosis. Recent studies have shown that ILC is associated with worse long-term outcomes compared to IDC. Risk stratification is essential in breast cancer for tailoring treatment plans to the individual patient’s needs, maximizing the chances of successful outcomes and minimizing unnecessary interventions. Although there are multiple tools available for breast cancer, there is currently not a risk stratification tool that is specific for ILC. The MDA iLobular prognostic tool is the first risk stratification tool that has been developed specifically for ILC patients. Methods: We retrospectively searched for patients treated at MD Anderson Cancer Center with a diagnosis of stage I-III ILC in our prospectively collected electronic database. The study focused on two primary endpoints: Overall Survival (OS) and Distant Recurrence Free Survival (DRFS). We utilized univariate and multivariate Cox Proportional Hazard (PH) regression models to assess the statistical significance of all variables. The univariate Cox analysis identified prognostic factors, which were further analyzed using backward and stepwise multivariate Cox proportional hazards regression analysis. This process helped identify statistically significant prognostic factors that were included in the final multivariate models. We estimated hazard ratios and 95% confidence intervals for each factor, considering a P-value of &amp;lt; 0.05 as statistically significant. To evaluate the performance of the fitted multivariate Cox PH regression models for OS and DRFS, we randomly divided two-thirds of the data points into a training dataset, while the remaining one-third constituted the test dataset. We assessed the discrimination capacity of each model using Harrell's C-index. Results: The study included a total of 4,216 female patients, with a median age of 56 years. The median pathological tumor size was 2 mm, and the median number of lymph nodes was one. Among these patients, 1,376 were pre-menopausal, while 2,837 were post-menopausal. The training cohort was a subset of 2,950 patients and the test cohort was a subset of 1,266 patients. After evaluating various prognostic models, we identified the model with the highest prognostic accuracy for OS and DRFS. This selected model demonstrated a Harrell's C-index of 0.704 for OS and 0.718 for DRFS on the training dataset and a Harrell's C-index of 0.702 for OS and 0.671 for DRFS on the test dataset. The model incorporated several covariates, including age at the time of diagnosis, number of lymph nodes, pathological tumor size (mm), ER status, tumor grade, ILC histology, and the presence or absence of concomitant LCIS (Table 1). Conclusion: In conclusion, the MDA iLobular prognostic tool represents a significant advancement as the first dedicated tool for risk stratifying ILC, providing valuable guidance for tailoring therapy to individual ILC patients based on their specific risk profiles. Table 1. Multivariate Cox proportional hazard model parameter estimates for OS and DRFS Citation Format: Jason Mouabbi, Sarah Pasyar, Roland Bassett, Akshara Singareeka Raghavendra, Amy Hassan, Rachel Layman, Debu Tripathy. MDA iLobular Prognostic Tool: A Novel Approach for Risk Stratification in Invasive Lobular Carcinoma [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 PO4-02-12.

Abstract PO5-15-10: Plasticity marker FOXC1 expression accurately predicts efficacy of Adjuvant Tamoxifen + Chemotherapy in reducing all-cause mortality in ER+LN- Breast Cancer: Validation in the SCAN-B Prospective Study (NCT02306096)

Abstract INTRODUCTION: Despite the development and validation of multimarker gene panels like OncotypeDx®, Mammaprint® and EndoPredict®, an affordable and globally accessible biomarker approach that can predict increased risk of all-cause mortality in patients diagnosed with ER+LN- breast cancer, as well as the efficacy of adjuvant endocrine + chemotherapy in mitigating such elevated risk, continues to be an unmet medical need. Lack of such a pragmatic solution continues to be the cause of preventable recurrent/metastatic disease in patients diagnosed with ER+LN- breast cancer in resource-challenged settings around the world. We hypothesized that a predictive biomarker strategy that measures expression of the plasticity marker FOXC1, in combination with clinical parameters like tumor size (TS) and tumor grade (TG), may offer equivalent results to that of the above multimarker gene panels at a significantly lower economic cost without compromising accuracy of prediction results. METHODS: Pre-treatment tumor RNA data obtained from a training cohort (compendium of gene expression microarray datasets, n=2857) and a single large validation cohort (SCAN-B Prospective Multicenter Observational Study, n=3520) for patients diagnosed with ER+LN- breast cancer were analyzed for FOXC1 expression, tumor size (TS) and tumor grade (TG) and correlated with Recurrence-Free Survival (RFS) and Overall Survival (OS). Optimized biomarker cutoff values based on model area-under-curve were leave-one-out cross validated and Risk-of-Recurrence (ROR) prediction algorithm derived utilizing the (compendium) training dataset. The unmodified strategy was then validated in the independent (SCAN-B) validation dataset. RESULTS: A predetermined High ROR score calculated using FOXC1 expression, TS and TG (trained using the compendium dataset) predicted efficacy of adjuvant endocrine + chemotherapy over that of adjuvant endocrine therapy alone in ER+LN- patients in terms of statistically significant reduction in all-cause mortality at 8-years post-diagnosis in the SCAN-B validation dataset (4.66% vs 24.06%, n=326, OR=6.48, 95%CI [2.97-14.11], p&amp;lt; 0.0001). OncotypeDx® (5.7% vs 14.4%, n=762, OR=2.79, 95%CI [1.37-5.67], p=0.002), Mammaprint® (5.1% vs 18.4%, n=665, OR= 95%CI [2.04-8.43], p&amp;lt; 0.0001) and Endopredict® (4.8% vs 15.2%, n=923, OR=3.58 95%CI [1.78-7.21], p=0.0002) were also statistically significant predictors of the same. CONCLUSION: Pre-treatment tumor FOXC1 mRNA or protein expression (assessed using qRT-PCR or routine immunohistochemistry (IHC), respectively, when combined with TS and TG presents a unique and economical alternative solution to multimarker gene panel tests like OncotypeDx®, Mammaprint® or Endopredict®, for guiding therapy of patients diagnosed with ER+LN- breast cancer in resource challenged settings. Such an approach to identify elevated risk of recurrence in patients diagnosed with ER+LN- breast cancer and prevent the same by guiding adjuvant endocrine + chemotherapy decisions, could help to extend recurrence-free and overall survival. Such an approach merits testing in real world ER+LN- patient cohorts in resource-challenged settings to help support implementation of this FOXC1-driven predictive biomarker strategy in the clinic. Citation Format: Partha Ray, Tania Ray, Clive Taylor, Robert Hussa. Plasticity marker FOXC1 expression accurately predicts efficacy of Adjuvant Tamoxifen + Chemotherapy in reducing all-cause mortality in ER+LN- Breast Cancer: Validation in the SCAN-B Prospective Study (NCT02306096) [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 PO5-15-10.

Developing and validating multi-omics prediction models for late patient-reported dysphagia in head and neck radiotherapy.

To investigate models developed using radiomic and dosiomic (multi-omics) features from planning and treatment imaging for late patient-reported dysphagia in head and neck radiotherapy.&#xD;&#xD;Materials and methods: Training (n=64) and testing (n=23) cohorts of head and neck cancer patients treated with curative intent chemo-radiotherapy with a follow-up time greater than 12 months were retrospectively examined. Patients completed the MD Anderson Dysphagia Inventory and a composite score ≤60 was interpreted as patient-reported dysphagia. A chart review collected baseline dysphagia and clinical factors. Multi-omic features were extracted from planning and last synthetic CT images using the pharyngeal constrictor muscle contours as a region of interest. Late patient-reported dysphagia models were developed using a random forest backbone, with feature selection and up-sampling methods to account for the imbalanced data. Models were developed and validated for multi-omic feature combinations for both timepoints. &#xD;&#xD;Results: A clinical and radiomic feature model developed using the planning CT achieved good performance (validation: sensitivity=80±27% / balanced accuracy=71±23%, testing: sensitivity =80±10% / balanced accuracy=73±11%). The synthetic CT models did not show improvement over the plan CT multi-omics models, with poor reliability of the radiomic features on these images. Dosiomic features extracted from the synthetic CT showed promise in predicting late patient-reported dysphagia.&#xD;&#xD;Conclusion: Multi-omics models can predict late patient-reported dysphagia in head and neck radiotherapy patients. Synthetic CT dosiomic features show promise in developing successful models to account for changes in delivered dose distribution. Multi-center or prospective studies are required prior to clinical implementation of these models.&#xD.

A novel model of artificial intelligence based automated image analysis of CT urography to identify bladder cancer in patients investigated for macroscopic hematuria.

To evaluate whether artificial intelligence (AI) based automatic image analysis utilising convolutional neural networks (CNNs) can be used to evaluate computed tomography urography (CTU) for the presence of urinary bladder cancer (UBC) in patients with macroscopic hematuria. Our study included patients who had undergone evaluation for macroscopic hematuria. A CNN-based AI model was trained and validated on the CTUs included in the study on a dedicated research platform (Recomia.org). Sensitivity and specificity were calculated to assess the performance of the AI model. Cystoscopy findings were used as the reference method. The training cohort comprised a total of 530 patients. Following the optimisation process, we developed the last version of our AI model. Subsequently, we utilised the model in the validation cohort which included an additional 400 patients (including 239 patients with UBC). The AI model had a sensitivity of 0.83 (95% confidence intervals [CI], 0.76-0.89), specificity of 0.76 (95% CI 0.67-0.84), and a negative predictive value (NPV) of 0.97 (95% CI 0.95-0.98). The majority of tumours in the false negative group (n = 24) were solitary (67%) and smaller than 1 cm (50%), with the majority of patients having cTaG1-2 (71%). We developed and tested an AI model for automatic image analysis of CTUs to detect UBC in patients with macroscopic hematuria. This model showed promising results with a high detection rate and excessive NPV. Further developments could lead to a decreased need for invasive investigations and prioritising patients with serious tumours.

Late-xerostomia prediction model based on 18F-FDG PET image biomarkers of the main salivary glands

Background and purposeRecently, a comprehensive xerostomia prediction model was published, based on baseline xerostomia, mean dose to parotid glands (PG) and submandibular glands (SMG). Previously, PET imaging biomarkers (IBMs) of PG were shown to improve xerostomia prediction. Therefore, this study aimed to explore the potential improvement of the additional PET-IBMs from both PG and SMG to the recent comprehensive xerostomia prediction model (i.e., the reference model). Materials and methodsTotally, 540 head and neck cancer patients were split into training and validation cohorts. PET-IBMs from the PG and SMG, were selected using bootstrapped forward selection based on the reference model. The IBMs from both the PG and SMG with the highest selection frequency were added to the reference model, resulting in a PG-IBM model and a SMG-IBM model which were combined into a composite model. Model performance was assessed using the area under the curve (AUC). Likelihood ratio test compared the predictive performance between the reference model and models including IBMs. ResultsThe final selected PET-IBMs were 90th percentile of the PG SUV and total energy of the SMG SUV. The additional two PET-IBMs in the composite model improved the predictive performance of the reference model significantly. The AUC of the reference model and the composite model were 0.67 and 0.69 in the training cohort, and 0.71 and 0.73 in the validation cohort, respectively. ConclusionThe composite model including two additional PET-IBMs from PG and SMG improved the predictive performance of the reference xerostomia model significantly, facilitating a more personalized prediction approach.