High-grade Patterns Research Articles

The Trends, Clinicopathological Features, and Treatment Outcomes of Patients with Prostate Cancer in Lokoja, Nigeria

Abstract Background: Prostate cancer (PCa) was the most common noncutaneous cancer among Nigerian men in 2020. Despite this high incidence, documented rates may be an underestimation. Objectives: This study aimed to determine the hospital incidence rate, trends, and characterise the clinicopathologic features, and treatment outcomes of patients with PCa in our institution. Materials and Methods: A 5-year retrospective review of patients managed for PCa at the Federal Teaching Hospital, Lokoja (FTHL), Nigeria, from 2016 to 2020. Medical abstraction was done on clinicopathologic features, treatment received, serum prostate specific antigen (PSA) at diagnosis, 3, and 7 months of androgen deprivation therapy (ADT). Data obtained were analysed using SPSS 25, and trends analysis of the annual incidence rate and number of patients with PCa in the period was done using joinpoint regression modelling. Results: Of 89 patients, 83 had complete medical records. The average annual hospital incidence rate was 174/100,000, and the mean age at diagnosis was 70.4 years. Locally advanced disease was found in 81.9% of patients, and tumours were ISUP grade groups 4 and 5 in 54.2% of patients. ADT was done in 62.6% of the patients, while 28.9% of the patients defaulted after diagnosis. The mean serum PSA was 71.9 ng/mL (7.7–156) at diagnosis and was 11.1 ng/mL (0.1–102) and 15.3 ng/mL at 3 and 7 months of ADT, respectively. Only 40.6% of the treated patients had a serum PSA ≤ 4 ng/mL at 7 months of ADT. Conclusions: Patients with PCa in FTHL, Nigeria, have mostly locally advanced disease at diagnosis. Over half have a high-grade pattern and fail to achieve a serum PSA ≤ 4 ng/mL after 7 months of ADT.

Enhanced CT-Based Intratumoral and Peritumoral Radiomics Nomograms Predict High-Grade Patterns of Invasive Lung Adenocarcinoma

Rationale and ObjectivesExtraction of intratumoral and peritumoral radiomics features combined with clinical factors to establish nomograms to predict high-grade patterns (micropapillary and solid) of invasive adenocarcinoma of the lung (IAC). Materials and MethodsA retrospective study was conducted on 463 patients with pathologically confirmed IAC. Patients were randomized in a 7:3 ratio into a training cohort (n = 324) and a testing cohort (n = 139). A total of 2,154 CT-based radiomic features were extracted from each of the four regions: gross tumor volume (GTV) and gross peritumoral tumor volume (GPTV3, GPTV6, GPTV9) containing peri-tumor regions of 3 mm, 6 mm, and 9 mm. A radiomics nomogram was constructed based on the optimal radiomics model and clinically independent predictors. ResultsThe GPTV3 radiomics model showed better predictive performance in the testing group compared to the GTV (0.840), GPTV6 (0.843) and GPTV9 (0.734) models, with an AUC value of 0.889 in the testing group. In the clinical model, tumor density and the presence of a spiculation sign were identified as independent predictors. The nomogram, which combined these independent predictors with the GPTV3-Radscore, proved to be clinically useful.窗体顶端 ConclusionThe GPTV3 radiomics model was superior to the GTV, GPTV6 and GPTV9 radiomics models in predicting high-grade patterns (HGP) of IAC. In addition, nomograms based on GPTV3 radiomics features and clinically independent predictors can further improve the prediction efficiency.

Identification of a gene expression signature of vascular invasion and recurrence in stage I lung adenocarcinoma via bulk and spatial transcriptomics.

Microscopic vascular invasion (VI) is predictive of recurrence and benefit from lobectomy in stage I lung adenocarcinoma (LUAD) but is difficult to assess in resection specimens and cannot be accurately predicted prior to surgery. Thus, new biomarkers are needed to identify this aggressive subset of stage I LUAD tumors. To assess molecular and microenvironment features associated with angioinvasive LUAD we profiled 162 resected stage I tumors with and without VI by RNA-seq and explored spatial patterns of gene expression in a subset of 15 samples by high-resolution spatial transcriptomics (stRNA-seq). Despite the small size of invaded blood vessels, we identified a gene expression signature of VI from the bulk RNA-seq discovery cohort (n=103) and found that it was associated with VI foci, desmoplastic stroma, and high-grade patterns in our stRNA-seq data. We observed a stronger association with high-grade patterns from VI+ compared with VI- tumors. Using the discovery cohort, we developed a transcriptomic predictor of VI, that in an independent validation cohort (n=60) was associated with VI (AUROC=0.86; p=5.42×10-6) and predictive of recurrence-free survival (HR=1.98; p=0.024), even in VI- LUAD (HR=2.76; p=0.003). To determine our VI predictor's robustness to intra-tumor heterogeneity we used RNA-seq data from multi-region sampling of stage I LUAD cases in TRACERx, where the predictor scores showed high correlation (R=0.87, p<2.2×10-16) between two randomly sampled regions of the same tumor. Our study suggests that VI-associated gene expression changes are detectable beyond the site of intravasation and can be used to predict the presence of VI. This may enable the prediction of angioinvasive LUAD from biopsy specimens, allowing for more tailored medical and surgical management of stage I LUAD.

Novel Insights Into the International Association for the Study of Lung Cancer Grading System for Lung Adenocarcinoma

The new grading system for lung adenocarcinoma proposed by the International Association for the Study of Lung Cancer (IASLC) defines prognostic subgroups on the basis of histologic patterns observed on surgical specimens. This study sought to provide novel insights into the IASLC grading system, with particular focus on recurrence-specific survival (RSS) and lung cancer–specific survival among patients with stage I adenocarcinoma. Under the IASLC grading system, tumors were classified as grade 1 (lepidic predominant with <20% high-grade patterns [micropapillary, solid, and complex glandular]), grade 2 (acinar or papillary predominant with <20% high-grade patterns), or grade 3 (≥20% high-grade patterns). Kaplan-Meier survival estimates, pathologic features, and genomic profiles were investigated for patients whose disease was reclassified into a higher grade under the IASLC grading system on the basis of the hypothesis that they would strongly resemble patients with predominant high-grade tumors. Overall, 423 (29%) of 1443 patients with grade 1 or 2 tumors classified based on the predominant pattern–based grading system had their tumors upgraded to grade 3 based on the IASLC grading system. The RSS curves for patients with upgraded tumors were significantly different from those for patients with grade 1 or 2 tumors (log-rank P < .001) but not from those for patients with predominant high-grade patterns (P = .3). Patients with upgraded tumors had a similar incidence of visceral pleural invasion and spread of tumor through air spaces as patients with predominant high-grade patterns. In multivariable models, the IASLC grading system remained significantly associated with RSS and lung cancer–specific survival after adjustment for aggressive pathologic features such as visceral pleural invasion and spread of tumor through air spaces. The IASLC grading system outperforms the predominant pattern–based grading system and appropriately reclassifies tumors into higher grades with worse prognosis, even after other pathologic features of aggressiveness are considered.

Who benefit from adjuvant chemotherapy in stage I lung adenocarcinoma? A multi-dimensional model for candidate selection

BackgroundDespite promising overall survival of stage I lung adenocarcinoma (LUAD) patients, 10-25 % of them still went through recurrence after surgery. [1] While it is still disputable whether adjuvant chemotherapy is necessary for stage I patients. [2] IASLC grading system for non-mucinous LUAD shows that minor high-grade patterns are significant indicator of poor prognosis. [3] Other risk factors, such as, pleura invasion, lympho-vascular invasion, STAS, etc. are also related to poor prognosis. [4-6] There still lack evidence whether IASLC grade itself or together with other risk factors can guide the use of adjuvant therapy in stage I patients. In this article, we tried to establish a multi-variable recurrence prediction model for stage I LUAD patients that is able to identify candidates of adjuvant chemotherapy. MethodsWe retrospectively collected patients who underwent lung surgery from 2018.8.1 to 2018.12.31 at our institution and diagnosed with lung adenocarcinoma pT1-2aN0M0 (stage I). Clinical data, manifestation on CT scan, pathologic features, driver gene mutations and follow-up information were collected. Cox proportional hazards regression analyses were performed utilizing the non-adjuvant cohort to predict disease free survival (DFS) and a nomogram was constructed and applied to the total cohort. Kaplan-Meier method was used to compare DFS between groups. Statistical analysis was conducted by R version 3.6.3. FindingsA total of 913 stage I LUAD patients were included in this study. Median follow-up time is 48.1 months.4-year and 5-year DFS are 92.9 % and 89.6 % for the total cohort. 65 patient experienced recurrence or death. 4-year DFS are 97.0 %,94.6 % and 76.2 %, and 5-year DFS are 95.5 %, 90.0 % and 74.1 % in IASLC Grade1, 2 and 3, respectively(p < 0.0001). High-risk patients defined by single risk factors, such as, IASLC grade 3, pleura invasion, STAS, less LN resected could not benefit from adjuvant therapy. A LASSO-COX regression model was built and patients are divided into high-risk and low-risk groups. In the high-risk group, patients underwent adjuvant chemotherapy have longer DFS than those who did not (p = 0.024), while in the low-risk group, patients underwent adjuvant chemotherapy have inferior DFS than those who did not (p < 0.001). InterpretationIASLC grading is a significant indicator of DFS, however it could not guide adjuvant therapy in our stage I LUAD cohort. Growth patterns and T indicators together with other risk factors could identify high-risk patients that are potential candidate of adjuvant therapy, including some stage IA LUAD patients.

Micropapillary and solid components as high-grade patterns in IASLC grading system of lung adenocarcinoma: Clinical implications and management

ObjectivesThe grading system proposed by the International Association for the Study of Lung Cancer is based on a combination of predominant histologic subtypes and the proportion of high-grade components with a cutoff of 20%. We aimed to examine the clinical implications of the grading system beyond the discrimination of patient prognosis, while assessing the biological differences among high-grade subtypes. MethodsWe retrospectively reviewed 648 consecutive patients with resected lung adenocarcinomas and examined their clinicopathologic, genotypic, and immunophenotypic features and treatment outcomes. Besides the differences among grades, the clinical impact of different high-grade components: micropapillary (MIP) and solid (SOL) patterns, was individually evaluated. ResultsSurvival outcomes were well-stratified according to the grading system. Grade 3 tumors exhibited aggressive clinicopathologic features, while being an independent prognostic factor in multivariable analysis. A small proportion (<20 %) of high-grade components in grade 2 had a negative prognostic impact. The prognostic difference bordering on the 20 % cutoff of the MIP proportion was validated; however, the proportion of SOL component did not affect prognosis. A survival benefit from adjuvant chemotherapy was observed in grade 3 tumors regardless of histologic subtype, but not in grade 1–2 tumors. The molecular and immunophenotypic features were different among grades, but still heterogeneous in grade 3, with MIP harboring frequent EGFR mutation and SOL exhibiting high PD-L1 expression. The treatment outcome after recurrence was worse in grade 3, but tumors with MIP pattern had an equivalent prognosis to that of grade 1–2 tumors, reflecting the high frequency of molecular targeted therapy. ConclusionsIn addition to stratifying patient prognosis, the current grading system could discriminate clinical course, therapeutic effects of adjuvant chemotherapy, and molecular and immunophenotypic features. Further stratification based on biological heterogeneity in grade 3 remains necessary to enhance the role of the grading system in guiding patient management.

Pulmonary Low Malignant Potential Adenocarcinoma: A Validation of the Proposed Criteria for This Novel Subtype.

Adenocarcinoma (ADC) is the most common histologic type of lung cancer, including in situ (lepidic), minimally invasive, and invasive forms. While the former 2 types are associated with a favorable outcome, the latter includes tumors with variable behavior, often tumor stage-related. A recent study proposed strict morphologic criteria defining a new subgroup of resected stage I invasive ADC (16% of cases) with favorable outcomes (100% disease-specific survival), named "ADC of low malignant potential (LMP-ADC)." The following criteria were met: ≤3cm size, nonmucinous histotype, ≥15% lepidic growth, and the absence of the following: high-grade patterns, >1 mitosis/2mm 2 , necrosis, and vascular/pleural invasion. The aim of the present study was to validate the performance of such criteria to identify LMP-ADC in a series of 274 stage IA resected lung ADCs from a single institution. Thirty-four tumors (12.4%) met the proposed criteria for LMP-ADC, as confirmed by additional stains for mitotic figures, Ki67 index, and elastic fibers (helpful to assess alveolar wall invasion). Minor differences between the lepidic and invasive components were observed regarding cell atypia and proliferation. p53 was normally expressed by invasive tumor cells. Mutations occurred in known lung cancer genes (mostly KRAS and EGFR). Five patients (14.7%) developed disease progression and 2 of them (5.9%) died of the disease. In our series, the disease-specific survival was 94.1%. In conclusion, in resected invasive lung ADC, a subgroup presenting low-grade morphologic features and associated with favorable prognosis does exist. Morphologic criteria for LMP-ADC supported by ancillary techniques represent a valid tool to better define this novel subgroup and to refine the stratification of invasive lung ADC, possibly suggesting modified follow-up protocols, based on the observed indolent behavior in most cases.

Prognostic implications of CT-defined ground glass opacity in clinical stage I–IIA grade 3 invasive non-mucinous pulmonary adenocarcinoma

To investigate the prognostic impact of computed tomography (CT)-defined ground glass opacity (GGO) in patients with clinical stage I-IIA grade 3 invasive non-mucinous pulmonary adenocarcinoma (INPA). The present study retrospectively enrolled 187 patients diagnosed with stage I-IIA grade 3 INPA. Their clinicopathological, radiological, and genetic information was evaluated systematically, and a 5-year follow-up was conducted to monitor disease recurrence and mortality. Patients were stratified based on the presence of a GGO component, and the Cox proportional hazard model was employed to assess the influence of clinicopathological factors and genetic variables on tumour outcomes. Recurrence-free survival (RFS) and overall survival (OS) were estimated using the Kaplan-Meier method and compared using the log-rank test. Significant differences were observed in both OS and RFS based on the presence of a GGO component. The group with GGO exhibited superior OS (p=0.002) and RFS (p=0.029). Multivariate analysis revealed that the presence of a GGO component (hazard ratio [HR]=0.412, 95% confidence interval [CI]: 0.177-0.959, p=0.040), clinical T2 stage (HR=2.473, 95% CI: 1.498-4.083, p<0.001), pathological N2 stage (HR=3.049, 95% CI: 1.800-5.167, p<0.001), and mixed high-grade patterns (HR=2.392, 95% CI: 1.418-4.036, p=0.001) were predictors of RFS. The presence of a GGO component is strongly associated with a favourable prognosis in grade 3 INPA.

Analysis of predictive factors of unforeseen nodal metastases in resected clinical stage I NSCLC.

Despite notable advances made in preoperative staging, unexpected nodal metastases after surgery are still significantly detected. In this study we aim to analyze the upstaging rate in patients with clinical stage I NSCLC without evidence of nodal disease in the preoperative staging who underwent lobectomy and radical lymphadenectomy. Patients who underwent lobectomy and systematic lymphadenectomy for clinical stage I NSCLC were evaluated. Exclusion criteria included the neoadjuvant treatment, incomplete resection and no adherence to preoperative guidelines. A total of 297 patients were included in the study. 159 patients were female, and the median age was 68 (61 - 73). The variables that showed a significant correlation with the upstaging rate at the univariate analysis were the number of resected lymph nodes and micropapillar/solid adenocar-cinoma subtype. This result was confirmed in the multivariate analysis with a OR= 2.545 (95%CI 1.136-5.701; p=0.02) for the number of resected lymph nodes and a OR=2.717 (95%CI 1.256-5.875; p=0.01) for the high-grade pattern of adenocarcinoma. Our results showed that in a homogeneous cohort of patients with clinical stage I NSCLC, the number of resected lymph nodes and the histological subtype of adenocarcinoma can significantly be associated with nodal metastasis.

P1.27-09 Efficacy and Safety of Aumolertinib as Adjuvant Therapy in Resectable Stage I NSCLC with High-grade Patterns and EGFR Mutations (APPOINT)

P1.27-09 Efficacy and Safety of Aumolertinib as Adjuvant Therapy in Resectable Stage I NSCLC with High-grade Patterns and EGFR Mutations (APPOINT)

P53, CK20, and FGFR3 Overexpression is Associated with the Characteristics of Urothelial Cell Carcinoma of the Bladder

The aim of this study was to investigate the association between the overexpression of tumor protein (P53), cytokeratin 20 (CK20), fibroblast growth factor receptor 3 (FGFR3), biomarkers and the grading, prognosis, heterogeneity, and relapse tendency of urothelial cell carcinomas (UCCs) of the bladder. A cross-sectional study was conducted using 413 samples of Iranian patients diagnosed with UCC of the bladder. The tissue microarray technique was used to evaluate the patterns of tumor tissue. Two pathologists scored tissue staining using a semi-quantitative scoring system. The results showed that P53 was a predictor of a high-grade pattern (the area under the curve (AUC)=0.620) with a best cut-off value of 95.0 using the receiver operating characteristic (ROC) curve. CK20 was another predictor of a high-grade pattern (AUC=0.745) with a best cut-off value of 15. However, the overexpression of both biomarkers was not associated with a heterogeneous pattern and could not predict tumor-associated death or relapse. The heterogeneous (odds ratio (OR)=4.535, p-value=0.001) and non-papillary (OR= 6.363, p-value= 0.001) patterns were effective predictors of tumor recurrence among all baseline variables, including patient and tumor characteristics. FGFR3 was positive in all specimens and was not a valuable biomarker for differentiating patterns. None of the variables predicted tumor prognosis. The study findings indicate that the intensity and percentage of cell staining for P53 and CK20 in the UCC of the bladder can aid in differentiating the grading patterns. The tendency of tumor relapse can be predicted by demonstrating heterogeneous and non-papillary patterns.

Correlation of Programmed Death-Ligand 1 Expression With Lung Adenocarcinoma Histologic and Molecular Subgroups in Primary and Metastatic Sites

Programmed death-ligand 1 (PD-L1) expression in terms of the tumor proportion score (TPS) is the main predictive biomarker approved for immunotherapy against lung nonsmall cell carcinoma. Although some studies have explored the associations between histology and PD-L1 expression in pulmonary adenocarcinoma, they have been limited in sample size and/or extent of examined histologic variables, which may have resulted in conflicting information. In this observational retrospective study, we identified primary and metastatic lung adenocarcinoma cases in the span of 5 years and tabulated the detailed histopathologic features, including pathological stage, tumor growth pattern, tumor grade, lymphovascular and pleural invasion, molecular alterations, and the associated PD-L1 expression for each case. Statistical analyses were performed to detect associations between PD-L1 and these features. Among 1658 cases, 643 were primary tumor resections, 751 were primary tumor biopsies, and 264 were metastatic site biopsies or resections. Higher TPS significantly correlated with high-grade growth patterns, grade 3 tumors, higher T and N stage, presence of lymphovascular invasion, and presence of MET and TP53 alterations, whereas lower TPS correlated with lower-grade tumors and presence of EGFR alterations. There was no difference in PD-L1 expression in matched primary and metastases, although higher TPS was observed in metastatic tumors due to the presence of high-grade patterns in these specimens. TPS showed a strong association with a histologic pattern. Higher-grade tumors had higher TPS, which is also associated with more aggressive histologic features. Tumor grade should be kept in mind when selecting cases and blocks for PD-L1 testing.

Evolutionary characterization of lung adenocarcinoma morphology in TRACERx.

Lung adenocarcinomas (LUADs) display a broad histological spectrum from low-grade lepidic tumors through to mid-grade acinar and papillary and high-grade solid, cribriform and micropapillary tumors. How morphology reflects tumor evolution and disease progression is poorly understood. Whole-exome sequencing data generated from 805 primary tumor regions and 121 paired metastatic samples across 248 LUADs from the TRACERx 421 cohort, together with RNA-sequencing data from 463 primary tumor regions, were integrated with detailed whole-tumor and regional histopathological analysis. Tumors with predominantly high-grade patterns showed increased chromosomal complexity, with higher burden of loss of heterozygosity and subclonal somatic copy number alterations. Individual regions in predominantly high-grade pattern tumors exhibited higher proliferation and lower clonal diversity, potentially reflecting large recent subclonal expansions. Co-occurrence of truncal loss of chromosomes 3p and 3q was enriched in predominantly low-/mid-grade tumors, while purely undifferentiated solid-pattern tumors had a higher frequency of truncal arm or focal 3q gains and SMARCA4 gene alterations compared with mixed-pattern tumors with a solid component, suggesting distinct evolutionary trajectories. Clonal evolution analysis revealed that tumors tend to evolve toward higher-grade patterns. The presence of micropapillary pattern and 'tumor spread through air spaces' were associated with intrathoracic recurrence, in contrast to the presence of solid/cribriform patterns, necrosis and preoperative circulating tumor DNA detection, which were associated with extra-thoracic recurrence. These data provide insights into the relationship between LUAD morphology, the underlying evolutionary genomic landscape, and clinical and anatomical relapse risk.

Exploration of Morphological Features of Clear Cell Renal Cell Carcinoma With PBRM1, SETD2, BAP1, or KDM5C Mutations.

The last decade has seen great advances in genomic profiling and prognosis-associated factors of clear cell renal cell carcinoma (RCC), the most common entity in kidney cancer. Following VHL, PBRM1, SETD2, BAP1, and KDM5C have been validated as the most common co-occurring gene mutations in clear cell RCC by multicenter studies. However, the morphological features of clear cell RCC with co-occurring gene mutations remain unclear. In this study, we presented 20 clear cell RCCs that underwent next-generation sequencing, of which 1 tumor was reclassified as ELOC-mutated RCC. PBRM1, SETD2, BAP1, and KDM5C were the most common mutations, following VHL. Morphologically, clear cell RCC with PBRM1 or KDM5C mutation usually displayed a low-grade pattern. Cystic changes and hyalinized stroma were often observed. The Ki67 index was <10%. These observations indicated good prognosis. However, mutated SETD2 may increase the malignancy of clear cell RCC with PBRM1 mutation. Two clear cell RCCs with mutated PBRM1 and SETD2 developed local or distant metastases. Clear cell RCC with BAP1 mutations always had high-grade patterns, and rhabdoid differentiation was also observed, indicating that BAP1 mutation was associated with poor outcomes. Papillary architecture was often a feature of BAP1 mutation, which is uncommon in clear cell RCC. PDL1 was positive in only one tumor with BAP1 mutation, and the positivity rate was limited to 5%. B7H3 was negative in all tumors. Morphologic findings in this small cohort may suggest why PBRM1 mutation does not correlate with decreased survival, whereas BAP1 mutation usually predicts poor outcomes.

Major pathologic response exhibited distinct prognostic significance for lung adenocarcinoma post different modalities of neoadjuvant therapy.

For non-small cell lung cancer (NSCLC) patients receiving neoadjuvant therapy, the major pathologic response (MPR) is defined as the percentage of residual viable tumor cells (%RVT) in the tumor bed of no more than 10%. It has been proposed as a predictor of survival in neoadjuvant therapy-treated cohorts. Nonetheless, the significance of %RVT in the pathological assessment of lung adenocarcinoma cohorts remains undetermined. Overall, 152 lung adenocarcinoma patients were included in this retrospective study, among whom 67 received neoadjuvant targeted therapy and 85 received neoadjuvant chemotherapy. Clinicopathological characteristics, neoadjuvant treatment response, and survival status were investigated. The routinely adopted standard for MPR (%RVT ≤10%) failed to differentiate prognosis in the lung adenocarcinoma population. For the neoadjuvant chemotherapy cohort, the optimal %RVT cutoff value of RFS was 60%. However, this cutoff value was clinically insignificant in the neoadjuvant targeted therapy cohort. Hence, for these patients, we built a nomogram model including high-grade patterns and ypN stage to predict disease recurrence, demonstrating a high efficacy (a bootstrap-corrected C-index of 0.731). %RVT served as a strong indicator of the prognosis of lung adenocarcinoma in patients receiving neoadjuvant chemotherapy but not neoadjuvant targeted therapy. Residual high-grade pathological patterns might substitute MPR in prognostic evaluation of lung adenocarcinoma post-targeted therapy.

Prediction of high-grade patterns of stage IA lung invasive adenocarcinoma based on high-resolution CT features: a bicentric study.

This study aims to predict the high-grade pattern (HGP) of stage IA lung invasive adenocarcinoma (IAC) based on the high-resolution CT (HRCT) features. The clinical, pathological, and HRCT imaging data of 457 patients (from bicentric) with pathologically confirmed stage IA IAC (459 lesions in total) were retrospectively analyzed. The 459 lesions were classified into high-grade pattern (HGP) (n = 101) and non-high-grade pattern (n-HGP) (n = 358) groups depending on the presence of HGP (micropapillary and solid) in pathological results. The clinical and pathological data contained age, gender, smoking history, tumor stage, pathological type, and presence or absence of tumor spread through air spaces (STAS). CT features consisted of lesion location, size, density, shape, spiculation, lobulation, vacuole, air bronchogram, and pleural indentation. The independent predictors for HGP were screened by univariable and multivariable logistic regression analyses. The clinical, CT, and clinical-CT models were constructed according to the multivariable analysis results. The multivariate analysis suggested the independent predictors of HGP, encompassing tumor size (p = 0.001; OR = 1.090, 95% CI 1.035-1.148), density (p < 0.001; OR = 9.454, 95% CI 4.911-18.199), and lobulation (p = 0.002; OR = 2.722, 95% CI 1.438-5.154). The AUC values of clinical, CT, and clinical-CT models for predicting HGP were 0.641 (95% CI 0.583-0.699) (sensitivity = 69.3%, specificity = 79.2%), 0.851 (95% CI 0.806-0.896) (sensitivity = 79.2%, specificity = 79.6%), and 0.852 (95% CI 0.808-0.896) (sensitivity = 74.3%, specificity = 85.8%). The logistic regression model based on HRCT features has a good diagnostic performance for the high-grade pattern of stage IA IAC. • The AUC values of clinical, CT, and clinical-CT models for predicting high-grade patterns were 0.641 (95% CI 0.583-0.699), 0.851 (95% CI 0.806-0.896), and 0.852 (95% CI 0.808-0.896). • Tumor size, density, and lobulation were independent predictive markers for high-grade patterns. • The logistic regression model based on HRCT features has a good diagnostic performance for the high-grade patterns of invasive adenocarcinoma.

Development of a novel predictive model for lymph node metastasis in patients with endometrial endometrioid carcinoma

BackgroundGlobally, the burden of endometrial endometrioid carcinoma (EEC) increases annually. However, the histological grade of EEC remains unelucidated. We developed a novel model for predicting lymph node metastasis (LNM) in patients with endometrioid carcinoma (EC), which has not been well established.MethodsA total of 344 patients with EEC were classified into training (n = 226) and validation (n = 118) cohorts. To develop a nomogram to predict LNM, independent predictors were defined using univariate and multivariate regression analyses. The calibration curve, area under the decision curve analysis (DCA), and receiver operating characteristic curve were used to evaluate the performance of the nomogram.ResultsIndependent predictors of LNM in EC were identified in the univariate analysis, including mitosis; microcystic, elongated, and fragmented patterns; lymphovascular invasion (LVI); necrosis; and high-grade pattern. Mitosis, LVI, and high-grade pattern remained independent predictors of LNM in multivariate analysis. An LNM nomogram that was constructed by incorporating the five predictors showed reliable discrimination and calibration. DCA showed that the LNM nomogram scoring system had significant clinical application value. In addition, a high nomogram score (score > 150) was a significant prognosticator for survival in both LNM-positive and LNM-negative ECs.ConclusionsOur novel predictive model for LNM in patients with EC has the potential to assist surgeons in making optimal treatment decisions.

Validation of the novel International Association for the Study of Lung Cancer grading system and prognostic value of filigree micropapillary and discohesive growth pattern in invasive pulmonary adenocarcinoma

IntroductionThe Pathology Committee of the International Association for the Study of Lung Cancer (IASLC) proposed a new histological grading system based on the combination of predominant and high-grade patterns in 2020. Materials and MethodsPathological sections from 631 patients with stage I-III invasive lung adenocarcinoma were reviewed. We then determined the histological grade according to the new grading system and confirmed the pathological features that included the filigree micropapillary and discohesive growth pattern. Applying of the novel IASLC grading system in prognosis stratification was verified and the clinical significance of the pathological characteristics was explored. ResultsCox multivariable analysis revealed that in the stage I-III invasive lung adenocarcinoma, the IASLC grading system was significantly associated with disease-free survival (DFS) [hazard ratio (HR) = 1.419; 95 % confidence interval (CI): 1.040–1.937; P = 0.027] and overall survival (OS) (HR = 1.899; 95 % CI: 1.168–3.087; P = 0.010). In patients with IASLC Grades 1 and 2, the simultaneous presence of filigree micropapillary and discohesive growth pattern was significantly correlated with DFS (HR = 1.899; 95 % CI:1.168–3.087; P = 0.010). However, the filigree micropapillary and discohesive growth pattern did not affect the OS (HR = 2.786; P = 0.317). The competitive risk model revealed that in the stage I cohort, the simultaneous presence of filigree micropapillary and discohesive growth pattern was a risk factor for recurrence and metastasis [sub- distribution HR (SHR) = 1.987; 95 %CI: 1.122–3.518; P = 0.019]. ConclusionOur study verified that the new prognostic stratification system was an effective stratification tool. Filigree micropapillary and discohesive growth pattern may also be risk factors for DFS, postoperative recurrence and metastasis.

Establishment and validation of a radiological-radiomics model for predicting high-grade patterns of lung adenocarcinoma less than or equal to 3 cm.

ObjectiveWe aimed to develop a Radiological-Radiomics (R-R) based model for predicting the high-grade pattern (HGP) of lung adenocarcinoma and evaluate its predictive performance.MethodsThe clinical, pathological, and imaging data of 374 patients pathologically confirmed with lung adenocarcinoma (374 lesions in total) were retrospectively analyzed. The 374 lesions were assigned to HGP (n = 81) and non-high-grade pattern (n-HGP, n = 293) groups depending on the presence or absence of high-grade components in pathological findings. The least absolute shrinkage and selection operator (LASSO) method was utilized to screen features on the United Imaging artificial intelligence scientific research platform, and logistic regression models for predicting HGP were constructed, namely, Radiological model, Radiomics model, and R-R model. Also, receiver operating curve (ROC) curves were plotted on the platform, generating corresponding area under the curve (AUC), sensitivity, specificity, and accuracy. Using the platform, nomograms for R-R models were also provided, and calibration curves and decision curves were drawn to evaluate the performance and clinical utility of the model. The statistical differences in the performance of the models were compared by the DeLong test.ResultsThe R-R model for HGP prediction achieved an AUC value of 0.923 (95% CI: 0.891-0.948), a sensitivity of 87.0%, a specificity of 83.4%, and an accuracy of 84.2% in the training set. In the validation set, this model exhibited an AUC value of 0.920 (95% CI: 0.887-0.945), a sensitivity of 87.5%, a specificity of 83.3%, and an accuracy of 84.2%. The DeLong test demonstrated optimal performance of the R-R model among the three models, and decision curves validated the clinical utility of the R-R model.ConclusionIn this study, we developed a fusion model using radiomic features combined with radiological features to predict the high-grade pattern of lung adenocarcinoma, and this model shows excellent diagnostic performance. The R-R model can provide certain guidance for clinical diagnosis and surgical treatment plans, contributing to improving the prognosis of patients.

P2.09-01 MRI-based Radiomic Signature to Predict Pathologic High-grade Pattern in Lung Adenocarcinoma

While curative resection is the established treatment in N0 early lung adenocarcinoma patients, recurrence have been reported to be more than 30% and one of the risk factors can be attributed to micropapillary and solid pattern in histology, both being high-grade patterns associated with adverse outcome. Also, incorporation of high-grade pattern in lung adenocarcinoma grading system yields good correlation with prognosis. In this regard, our study focuses on developing the first prediction model to find quantitative MRI features of micropapillary or solid pattern (MPsol) in lung adenocarcinoma by radiomics approach.