Classification Of Adenocarcinoma Research Articles

Morphologic Features of Invasion in Lung Adenocarcinoma: Diagnostic Pitfalls.

Reproducibility of pulmonary invasive adenocarcinoma diagnosis is poor when applying the World Health Organization (WHO) classification. In this article, we aimed first to explain by 3-dimensional morphology why simple pattern recognition induces pitfalls for the assessment of invasion as applied in the current WHO classification of pulmonary adenocarcinomas. The underlying iatrogenic-induced morphologic alterations in collapsed adenocarcinoma in situ overlap with criteria for invasive adenocarcinoma. Pitfalls in seemingly acinar and papillary carcinoma are addressed with additional cytokeratin 7 and elastin stains. In addition, we provide more stringent criteria for a better reproducible and likely generalizable classification.

Classification of Lung Adenocarcinoma Using Convolutional Neural Networks: A Bioinformatics Approach

Classification of Lung Adenocarcinoma Using Convolutional Neural Networks: A Bioinformatics Approach

Molecular classification and intratumoral heterogeneity of gastric adenocarcinoma.

Gastric cancers frequently harbor striking histological complexity and diversity between lesions as well as within single lesions, known as inter- and intratumoral heterogeneity, respectively. The latest World Health Organization Classification of Tumors designated more than 30 histological subtypes for gastric epithelial tumors, assigning 12 subtypes for gastric adenocarcinoma (GAD). Meanwhile, recent advances in genome-wide analyses have provided molecular aspects to the histological classification of GAD, and consequently revealed different molecular traits underlying these histological subtypes. Moreover, accumulating knowledge of comprehensive molecular profiles has led to establishing molecular classifications of GAD, which are often associated with clinical biomarkers for therapeutics and prognosis. However, most of our knowledge of GAD molecular profiles is based on inter-tumoral heterogeneity, and the molecular profiles underlying intratumoral heterogeneity are yet to be determined. In this review, recently established molecular classifications of GAD are introduced in the aspect of pathological diagnosis and are discussed in the context of intratumoral heterogeneity.

The Silva pattern-based classification for HPV-associated endocervical adenocarcinoma: A single institution concordance study of trainees and gynecologic pathologists

The Silva pattern-based classification of HPV-associated endocervical adenocarcinoma has become an integral part of the histologic assessment of these tumors. Unfortunately, the Silva system reproducibility has had mixed results in past studies, and clinical practice still favors the FIGO stage assessment in directing therapeutic interventions for patients. In our study, we aimed to assess our institution’s concordance including not only gynecologic pathologists, but also pathology trainees through a series of 69 cases. The grouped total kappa concordance from all participants was 0.439 (Moderate), with an overall trainee kappa of 0.417 (moderate) and an overall pathologist kappa of 0.460 (moderate). Perfect concordance among all 10 study participants was seen in 8/69 cases (11.6 %), corresponding to 5/22 Pattern A cases (22.7 %), 0/16 Pattern B cases (0 %), and 3/31 Pattern C cases (9.7 %), with similar findings between trainees and pathologists when compared within their own cohorts. Recurrence was identified in 2 Pattern A cases, indicating a potential issue with limited excisional specimens which may not fully appreciate the true biologic aggressiveness of the lesions.

Molecular Classification and Pathogenesis of Pancreatic Adenocarcinoma and Targeted Therapies: A Review.

Pancreatic adenocarcinoma (PDAC) is disease with a 5-year survival of only 12%. Many patients with PDAC present with late-stage disease and even early-stage disease can often be characterized by an aggressive tumor biology. Standard therapy for metastatic PDAC consists mainly of chemotherapy regimens like FOLFIRINOX, FOLFOX, or gemcitabine and nab-paclitaxel. Research has focused on sequencing PDAC tumors to understand better the mutational landscape and transcriptomics of PDAC with the goal to develop targeted therapies. Targeted therapies may potentially minimize the toxic risks of chemotherapy and provide a long-term survival benefit. We herein review the underlying molecular pathogenesis of PDAC, as well as the classification schema created from current sequencing data, and recent updates related to targeted therapy for PDAC.

Spread Through Air Spaces in Residual Tumor Classification for Clinical IA Lung Adenocarcinoma

BackgroundWe aimed to validate the prognostic implication of uncertain resection, R(un), proposed by International Association for the Study of Lung Cancer (IASLC) and evaluate the prognostic value of spread through air spaces (STAS) in reclassifying the R classification among patients with lung adenocarcinoma after segmentectomy. MethodsWe enrolled 1007 patients who underwent segmentectomy for c-stage IA lung adenocarcinoma between 2014 and 2017. Recurrence-free survival (RFS) and overall survival (OS) were compared to evaluate the prognostic value of IASLC-R(un) and STAS. Whether STAS would skip into complementary lobectomy was evaluated in a prospective cohort. ResultsThe current IASLC-R(un) failed to significantly stratify the RFS (P = .078) in segmentectomy, and STAS was a stronger risk factor of poor prognosis for both RFS and OS (P < .001). Moreover, the presence of STAS was associated with increased locoregional recurrence in patients undergoing segmentectomy (P < .001) but not in those treated with lobectomy (P = .187), indicating that only STAS-positive segmentectomy was consistent with the concept of R(un) in relapse pattern. After reclassifying STAS-positive segmentectomy into the R(un) category, the proposed R(un) showed an improvement in prognosis stratification. In addition, 2 of 30 patients (6.2%) in the prospective cohort who underwent initial segmentectomy and complementary lobectomy had STAS clusters in the complementary lobectomy specimens. ConclusionsUnfavorable prognosis, relapse patterns consistent with R(un), and pathologic verification that saltatory spread of STAS observed in complementary lobectomy specimens supported reclassifying STAS-positive segmentectomy as R(un). STAS is a critical concern for the surgical completeness evaluation after segmentectomy.

Transformaer-based model for lung adenocarcinoma subtypes.

Lung cancer has the highest morbidity and mortality rate among all types of cancer. Histological subtypes serve as crucial markers for the development of lung cancer and possess significant clinical values for cancer diagnosis, prognosis, and prediction of treatment responses. However, existing studies only dichotomize normal and cancerous tissues, failing to capture the unique characteristics of tissue sectionsand cancertypes. Therefore, we have pioneered the classification of lung adenocarcinoma (LAD) cancer tissues into five subtypes (acinar, lepidic, micropapillary, papillary, and solid) based on sectiondata in whole-slide image sections. In addition, a novel model called HybridNet was designed to improve the classificationperformance. HybridNet primarily consists of two interactive streams: a Transformer and a convolutional neural network (CNN). The Transformer stream captures rich global representations using a self-attention mechanism, while the CNN stream extracts local semantic features to optimize image details. Specifically, during the dual-stream parallelism, the feature maps of the Transformer stream as weights are weighted and summed with those of the CNN stream backbone; at the end of the parallelism, the respective final features are concatenated to obtain more discriminative semanticinformation. Experimental results on a private dataset of LAD showed that HybridNet achieved 95.12% classification accuracy, and the accuracy of five histological subtypes (acinar, lepidic, micropapillary, papillary, and solid) reached 94.5%, 97.1%, 94%, 91%, and 99% respectively; the experimental results on the public BreakHis dataset show that HybridNet achieves the best results in three evaluation metrics: accuracy, recall and F1-score, with 92.40%, 90.63%, and 91.43%,respectively. The process of classifying LAD into five subtypes assists pathologists in selecting appropriate treatments and enables them to predict tumor mutation burden (TMB) and analyze the spatial distribution of immune checkpoint proteins based on this and other clinical data. In addition, the proposed HybridNet fuses CNN and Transformer information several times and is able to improve the accuracy of subtype classification, and also shows satisfactory performance on public datasets with some generalizationability.

A novel classification of small bowel adenocarcinoma based on a hidden genome classifier: A multi-institutional study.

724 Background: Small bowel adenocarcinoma (SBA) is a rare malignancy with poor prognosis. Data describing the clinical and pathologic characteristics of this disease are sparse, and few studies examine the genetic footprint of SBAs within the continuum of the gastrointestinal tract. Methods: All patients with small bowel adenocarcinoma for whom primary tumor tissue was available from 1993 to 2021 at six institutions were included. A hidden genome classifier (HGC) was developed based on genomic features from 286 gastroesophageal cancers (foregut) and 286 colorectal cancers (hindgut) using targeted tumor sequencing. The SBA samples were run through the HGC to obtain the predicted probability of either foregut or hindgut lineage. For patients submitted to curative intent resection, overall survival (OS) was calculated from 90 days post resection until date of last follow up. Cox regression was used to examine factors associated with OS and to construct the multivariable model. Results: Of a total of 243 patients with SBA, 60% were male. The most common anatomic locations were duodenum (53%), jejunum (28%), and ileum (12%). HGC prediction of foregut lineage was higher for duodenal (55%) and intestinal-type periampullary (64%) tumors, while prediction of hindgut lineage was higher for ileal (68%) and jejunal (54%) tumors. Patients were then divided into three HGC prediction groups based on the lowest-, highest-, and inter-quartile ranges: foregut (n=61), characterized by gene amplification of CDK12, CD3, EGFR, and KRAS and cytoband segmentation of chromosomes 8-p21.1 and 15-q26.3; hindgut (n=61), characterized by APC and KRAS mutations; and mixed-type (n=121) with elements of both. Among those undergoing curative-intent surgery (n=160), median OS was 73 months. On multivariable analysis, HGC prediction group (hindgut vs mixed-type, HR 3.22, 95%CI 1.54-6.73), age (HR 1.03, 95%CI 1.01-1.05) and positive lymph node status (HR 1.96, 95%CI 1.02-3.76) were associated with decreased survival (Table). Neither anatomic site nor the presence of other driver mutations was associated with survival. Conclusions: Small bowel adenocarcinomas display genomic heterogeneity. A novel hidden genome classifier that stratifies SBAs based on homology to foregut or hindgut genomic alterations may be superior to anatomic location for characterizing disease biology. Additional studies are needed to validate and further explore these findings. [Table: see text]

The value of CT shape quantification in predicting pathological classification of lung adenocarcinoma

ObjectiveTo evaluate whether quantification of lung GGN shape is useful in predicting pathological categorization of lung adenocarcinoma and guiding the clinic.Methods98 patients with primary lung adenocarcinoma were pathologically confirmed and CT was performed preoperatively, and all lesions were pathologically ≤ 30 mm in size. On CT images, we measured the maximum area of the lesion’s cross-section (MA). The longest diameter of the tumor (LD) was marked with points A and B, and the perpendicular diameter (PD) was marked with points C and D, which was the longest diameter perpendicular to AB. and D, which was the longest diameter perpendicular to AB. We took angles A and B as big angle A (BiA) and small angle A (SmA). We measured the MA, LD, and PD, and for analysis we derived the LD/PD ratio and the BiA/SmA ratio. The data were analysed using the chi-square test, t-test, ROC analysis, and binary logistic regression analysis.ResultsPrecursor glandular lesions (PGL) and microinvasive adenocarcinoma (MIA) were distinguished from invasive adenocarcinoma (IAC) by the BiA/SmA ratio and LD, two independent factors (p = 0.007, p = 0.018). Lung adenocarcinoma pathological categorization was indicated by the BiA/SmA ratio of 1.35 and the LD of 11.56 mm with sensitivity of 81.36% and 71.79%, respectively; specificity of 71.79% and 74.36%, respectively; and AUC of 0.8357 (95% CI: 0.7558–0.9157, p < 0.001), 0.8666 (95% CI: 0.7866–0.9465, p < 0.001), respectively. In predicting the pathological categorization of lung adenocarcinoma, the area under the ROC curve of the BiA/SmA ratio combined with LD was 0.9231 (95% CI: 0.8700-0.9762, p < 0.001), with a sensitivity of 81.36% and a specificity of 89.74%.ConclusionsQuantification of lung GGN morphology by the BiA/SmA ratio combined with LD could be helpful in predicting pathological classification of lung adenocarcinoma.

Development and validation of a nomogram for Siewert II esophagogastric junction adenocarcinoma: a retrospective analysis.

Due to the complex histological type and anatomical structures, there has been considerable debate on the classification of adenocarcinoma of the esophagogastric junction (AEG), especially Siewert II AEG. Furthermore, neither the American Joint Committee on Cancer (AJCC) 7th tumor-node-metastasis (TNM) [esophageal adenocarcinoma (E) or gastric cancer (G)] nor the AJCC 8th TNM (E or G) accurately predicted the prognosis of patients with Siewert II AEG. This study aimed to investigate the factors influencing the survival and prognosis of patients with Siewert II AEG and establish a new and better prognostic predictive model. A retrospective study. Patients with Siewert II AEG, retrieved from the Surveillance, Epidemiology, and End Results (SEER) databases, were assigned to the training set. Patients retrieved from a single tertiary medical center were assigned to the external validation set. Significant variables were selected using univariate and multivariate Cox regression analyses to construct the nomogram. Nomogram models were assessed using the concordance index (C-index), a calibration plot, decision curve analysis (DCA), and external validation. Age, tumor grade, and size, as well as the T, N, and M stages, were included in the nomograms. For the SEER training set, the C-index of the nomogram was 0.683 (0.665-0.701). The C-index of the nomogram for the external validation set was 0.690 (0.653-0.727). The calibration curve showed good agreement between the nomogram estimations and actual observations in both the training and external validation sets. The DCA showed that the nomogram was clinically useful. The new predictive model showed significant accuracy in predicting the prognosis of Siewert II AEG.

A Deep Learning Framework for Mass-Forming Chronic Pancreatitis and Pancreatic Ductal Adenocarcinoma Classification Based on Magnetic Resonance Imaging

A Deep Learning Framework for Mass-Forming Chronic Pancreatitis and Pancreatic Ductal Adenocarcinoma Classification Based on Magnetic Resonance Imaging

Classification and immunotherapy assessment of lung adenocarcinoma based on coagulation-related genes.

Introduction: This study on lung adenocarcinoma (LUAD), a common lung cancer subtype with high mortality. Aims: This study focuses on how tumor cell interactions affect immunotherapy responsiveness. Methods: Using public databases, we used non-negative matrix factorization clustering method, ssGSEA, CIBERSORT algorithm, immunophenotype score, survival analysis, protein-protein interaction network method to analyze gene expression data and coagulation-related genes. Results: We dividedLUAD patients into three coagulation-related subgroups with varying immune characteristics and survival rates. A cluster ofthreepatients, having the highest immune infiltration and survival rate, also showed the most potential for immunotherapy. We identified fivekey genes influencing patient survival using a protein-protein interaction network. Conclusion: This research offers valuable insights for forecasting prognosis and immunotherapy responsiveness in LUAD patients, helping to inform clinical treatment strategies.

Solitary lung adenocarcinoma: follow-up CT, pathological-molecular characteristics, and surgical prognosis for different morphological classifications

ObjectiveTo investigate the dynamic changes during follow-up computed tomography (CT), histological subtypes, gene mutation status, and surgical prognosis for different morphological presentations of solitary lung adenocarcinomas (SLADC).Materials and methodsThis retrospective study compared dynamic tumor changes and volume doubling time (VDT) in 228 patients with SLADC (morphological types I–IV) who had intermittent growth during follow-ups. The correlation between the morphological classification and histological subtypes, gene mutation status, and surgical prognosis was evaluated.ResultsAmong the 228 patients, 66 (28.9%) were classified as type I, 123 (53.9%) as type II, 16 (7%) as type III, and 23 (10.1%) as type IV. Type I had the shortest VDT (254 days), followed by types IV (381 days) and III (501 days), and then type II (993 days) (p < 0.05 each). Type I had a greater proportion of solid/micropapillary-predominant pattern than type II, and the lepidic-predominant pattern was more common in type II and III than in type I (p < 0.05 each). Furthermore, type II and IV SLADCs were correlated with positive epidermal growth factor receptor mutation (p < 0.05 each). Lastly, the Kaplan–Meier curves showed that the disease-free survival was longest for patients with type II tumors, followed by those with type III and IV tumors, and then those with type I tumors (p < 0.001 each).ConclusionA good understanding of the natural progression and pathological-molecular characteristics of different morphological SLADC types can help make accurate diagnoses, develop individual treatment strategies, and predict patient outcomes.Critical relevance statementA good understanding of the natural progression and pathological-molecular characteristics of different morphological solitary lung adenocarcinoma types can help make accurate diagnoses, develop individual treatment strategies, and predict patient outcomes.Key points• Type I–IV solitary lung adenocarcinomas exhibit varying natural progression on serial CT scans.• Morphological classification of solitary lung adenocarcinomas predicts histological subtype, gene status, and surgical prognosis.• This classification of solitary lung adenocarcinomas may help improve diagnostic, therapeutic, and prognosticating abilities.Graphical

Molecular profile of gastric adenocarcinoma, relevant epidemiological factors - Systematic review and meta-analysis relating sex with Epstein-Barr virus and unstable microsatellites subtypes.

Gastric epithelial tumors exhibit morphological heterogeneity, diverse biological behaviors, and different oncopathological pathways. The Cancer Genome Atlas (TCGA) proposed a molecular classification of gastric adenocarcinomas based on genetic and molecular findings, which shows particular characteristics of diagnosis, prognosis, and indirectly, therapeutic alternatives. Within this classification, Epstein-Barr virus-positive (EBV+) and high microsatellite instability (MSI-H) subtypes stand out as subtypes that present a less aggressive biological behavior and a highly mutilated phenotype. This study conducted a systematic review with an emphasis on epidemiological and prognostic factors based on the molecular classification proposed by TCGA. A broad, comprehensive, and reproducible search with methodological rigor was conducted for study selection using the ROBINS-I and GRADEpro protocols and appropriate combinations of keywords. A total of 25 studies were selected: six with a complete classification similar to TCGA and 19 with a distinction between MSI-H and EBV+. The application of meta-analysis calculations reinforces the prevalence of positive Epstein-Barr adenocarcinomas in males and high microsatellite instability in females, with a high level of certainty of evidence and low risk of bias in the analyzed studies due to the rigorous methods used. The molecular classification proposed by TCGA shows limited dissemination, with MSI-H and EBV+ subtypes being the most researched, probably due to the benefit of the association with immunotherapies. However, the subclassification cannot be restricted to less than a quarter of the cases, and improvements in this aspect are urgent for the construction of knowledge on this important topic of global health.

Impact of circumferential resection margin on survival in ampullary cancer: retrospective analysis.

Ampullary carcinoma is a clinically variable entity. This study aimed to evaluate prognostic factors for the outcome of resected ampullary carcinoma patients with particular intent to analyse the influence of surgical radicality. Patients undergoing resection between 2002 and 2017 were analysed. Clinicopathological parameters, perioperative outcome and survival were examined. Risk factor analysis for postresection survival was performed. Resection margin status was evaluated according to the revised classification for pancreatic adenocarcinoma. A total of 234 patients were identified, 97.9 per cent (n = 229) underwent formal resection, while 2.1 per cent (n = 5) underwent ampullary resection. Histological subtypes were 46.6 per cent (n = 109) pancreatobiliary, 34.2 per cent (n = 80) intestinal, 11.5 per cent (n = 27) mixed, and 7.7 per cent (n = 18) undetermined. In the pancreatobiliary group, tumours were more advanced with more vascular resections, pT4 stage, G3 differentiation and pN+ status. Five-year overall survival was significantly different for pancreatobiliary compared to intestinal (51.7 per cent versus 72.8 per cent, P = 0.0087). In univariable analysis, age, pT4 stage, pN+, pancreatobiliary subtype and positive resection margin were significantly associated with worse overall survival. Long-term outcome was significantly better after true R0 resection (circumferential resection margin-, tumour clearance >1 mm) compared with circumferential resection margin+ (<1 mm) and R1 resections (5-year overall survival: 69.6 per cent, median overall survival 191 months versus 42.4 per cent and 53 months; P = 0.0017). Postresection survival of ampullary carcinoma patients is determined by histological subtype and surgical radicality. Intestinal differentiation is associated with less advanced tumour stages and better differentiation, which is reflected in a significantly better overall survival compared to pancreatobiliary differentiation. Despite this, true R0-resection is a prognostic key determinant in both entities, achieving 5-year survival in two-thirds of patients.

Independent Stage Classification for Gastroesophageal Junction Adenocarcinoma.

In gastroesophageal junction (GEJ) adenocarcinoma cases, a prognosis based on ypTNM staging could be affected by preoperative therapy. Patients with esophageal adenocarcinoma and gastric adenocarcinoma who underwent preoperative therapy followed by surgical resection from 2006 through 2017 were identified in the National Cancer Database. To enable stage-by-stage OS comparisons, tumors were classified into four gross ypTNM groups: ypT1/2, N-negative; ypT1/2, N-positive; ypT3/4, N-negative; and ypT3/4, N-positive. Prognostic factors were examined, and an OS prediction nomogram was developed for patients with abdominal/lower esophageal and gastric cardia adenocarcinoma, representing GEJ cancers. We examined 25,463 patient records. When compared by gross ypTNM group, the abdominal/lower esophageal and gastric cardia adenocarcinoma groups had similar OS rates, differing from those of other esophageal or gastric cancers. Cox regression analysis of patients with GEJ cancers showed that preoperative chemoradiotherapy was associated with shorter OS than preoperative chemotherapy after adjustment for the ypTNM group (hazard ratio 1.31, 95% CI 1.24-1.39, p < 0.001), likely owing to downstaging effects. The nomogram had a concordance index of 0.833 and a time-dependent area under the curve of 0.669. OS prediction in GEJ adenocarcinoma cases should include preoperative therapy regimens. Our OS prediction nomogram provided reasonable OS prediction for patients with GEJ adenocarcinoma, and future validation is needed.

Updates on lung adenocarcinoma: invasive size, grading andSTAS.

Advancements in the classification of lung adenocarcinoma have resulted in significant changes in pathological reporting. The eighth edition of the tumour-node-metastasis (TNM) staging guidelines calls for the use of invasive size in staging in place of total tumour size. This shift improves prognostic stratification and requires a more nuanced approach to tumour measurements in challenging situations. Similarly, the adoption of new grading criteria based on the predominant and highest-grade pattern proposed by the International Association for the Study of Lung Cancer (IASLC) shows improved prognostication, and therefore clinical utility, relative to previous grading systems. Spread through airspaces (STAS) is a form of tumour invasion involving tumour cells spreading through the airspaces, which has been highly researched in recent years. This review discusses updates in pathological T staging, adenocarcinoma grading and STAS and illustrates the utility and limitations of current concepts in lung adenocarcinoma.

Extent of regional lymphadenectomy and number-based nodal classification for non-ampullary duodenal adenocarcinoma

BackgroundThis study aimed to evaluate the adequate extent of regional lymphadenectomy according to tumor location and the impact of number-based nodal classification on survival in patients with non-ampullary duodenal adenocarcinoma (NADAC). MethodsA total of 85 patients with NADAC who underwent surgery were enrolled. The frequency of metastasis was calculated for each node group in the respective tumor locations for 63 patients who underwent lymphadenectomy for pT2-pT4 tumor. ResultsThe frequency of metastasis in the pancreaticoduodenal (nos. 13 and 17) and superior mesenteric artery (no. 14) nodes was high (16.7 %–52.3 %) regardless of tumor location. Metastasis in the perigastric (nos. 3 and 4d) and right celiac artery (no. 9) nodes was not uncommon (14.3 %–22.2 %) for tumors in the first portion. The frequency of metastasis in the pyloric (nos. 5 and 6) and the other peripancreaticoduodenal (nos. 8 and 12) nodes varied depending on tumor location but could not be ignored for staging. When these nodes were classified as regional nodes, the 5-year survival in patients with pN0, pN1 (1–2 positive nodes), and pN2 (≥3 positive nodes) were 82.9 %, 51.7 %, and 19.2 %, respectively (p < 0.001). pN classification independently predicted survival (pN1, p = 0.022; pN2, p < 0.001). ConclusionsNos. 5, 6, 8, 12, 13, 14, and 17 nodes in all advanced NADAC and nos. 3, 4d, and 9 nodes in advanced NADAC in the first portion should be considered as regional nodes for accurate staging. The number-based nodal classification allows good patients’ prognostic stratification.

Combined deep-learning MRI-based radiomic models for preoperative risk classification of endometrial endometrioid adenocarcinoma.

Differences exist between high- and low-risk endometrial cancer (EC) in terms of whether lymph node dissection is performed. Factors such as tumor grade, myometrial invasion (MDI), and lymphovascular space invasion (LVSI) in the European Society for Medical Oncology (ESMO), European SocieTy for Radiotherapy & Oncology (ESTRO) and European Society of Gynaecological Oncology (ESGO) guidelines risk classification can often only be accurately assessed postoperatively. The aim of our study was to estimate the risk classification of patients with endometrial endometrioid adenocarcinoma before surgery and offer individualized treatment plans based on their risk classification. Clinical information and last preoperative pelvic magnetic resonance imaging (MRI) of patients with postoperative pathologically determined endometrial endometrioid adenocarcinoma were collected retrospectively. The region of interest (ROI) was subsequently plotted in T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) MRI scans, and the traditional radiomics features and deep-learning image features were extracted. A final radiomics nomogram model integrating traditional radiomics features, deep learning image features, and clinical information was constructed to distinguish between low- and high-risk patients (based on the 2020 ESMO-ESGO-ESTRO guidelines). The efficacy of the model was evaluated in the training and validation sets of the model. We finally included 168 patients from January 1, 2020 to July 29, 2021, of which 95 patients in 2021 were classified as the training set and 73 patients in 2020 were classified as the validation set. In the training set, the area under the curve (AUC) of the radiomics nomogram was 0.923 (95%CI: 0.865-0.980) and in the validation set, the AUC of the radiomics nomogram was 0.842 (95%CI: 0.762-0.923). The nomogram had better predictions than both the traditional radiomics model and the deep-learning radiomics model. MRI-based radiomics models can be useful for preoperative risk classification of patients with endometrial endometrioid adenocarcinoma.

Silva Classification System for HPV-Related EAC of Stage I ∼ IIIc1p Cervical Adenocarcinoma and Its Effect on Prognosis and Survival

The proportion of adenocarcinoma in cervical cancer gradually increased and presented a younger trend. The previous pathological classification of cervical adenocarcinoma is difficult to provide reference for clinical treatment. In recent years, Silva classification, a new pathologic system for cervical adenocarcinoma, has been confirmed to be suitable for HPV-associated adenocarcinoma (HPVA), and has shown certain clinical application value in subsequent studies. Therefore, this study will retrospectively analyze the distribution of Silva typing system in patients with HPVA under standard treatment mode and its relationship with prognosis and survival. From January 2010 to September 2021, 124 cervical adenocarcinoma patients with HPVA were retrospectively included, who underwent radical resection of cervical cancer. The HE staining sections of the patients were divided into SilvaA, SilvaB, and SilvaC types according to the Silva typing system. Kaplan-Meier calculation was used for single-factor analysis, and COX stepwise regression model was used for multi-factor analysis. Of the 124 patients with HPVA who could be graded according to the Silva system, 16 (12.9%, 16/124) were SilvaA, 27 (21.7%, 27/124) SilvaB, and 81 (65.4%, 81/124) SilvaC. In Silva classification, FIGO staging of Silva A and B was stage I. And FIGO staging of Silva C was more significantly later than the staging of Silva A and B. All lymph node metastases and paruterine infiltrates were found only in Silva C. In addition, the patients with Silva C large mass accounted for a higher proportion (41.7%). SilvaA type cervical adenocarcinoma patients were in a survival state by the end of follow-up. Among Silva B, 3 patients died due to tumor, and the 5-year OS rate were 91.3%. Among SilvaC, 15 patients died due to tumor, and the 5-year OS rate were 76.5%. FIGO stage and lymph node invasion were the influencing factors for survival and prognosis of Silva classification (P <0.05). FIGO stage, tumor size, lymph node invasion, and paralegal invasion were the influencing factors for survival and prognosis of SilvaC patients (P <0.05). Silva model classification system combined with clinicopathological features has certain clinical value for the prognostic guidance of HPVA patients. Among Silva classification, SilvaC had the worst prognosis. Late FIGO stage, lymph node metastasis, and paralegal infiltration are the influencing factors for survival and prognosis of SilvaC type.