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Identifying the lung carcinoma subtype in small biopsy specimens is an important part of determining a suitable treatment plan but is often challenging without the help of special and/or immunohistochemical stains. Pathology image analysis that tackles this issue would be helpful for diagnoses and subtyping of lung carcinoma. In this study, we developed AI models to classify multinomial patterns of lung carcinoma; ADC, LCNEC, SCC, SCLC, and non-neoplastic lung tissue based on convolutional neural networks (CNN or ConvNet). Four CNNs that were pre-trained using transfer learning and one CNN built from scratch were used to classify patch images from pathology whole-slide images (WSIs). We first evaluated the diagnostic performance of each model in the test sets. The Xception model and the CNN built from scratch both achieved the highest performance with a macro average AUC of 0.90. The CNN built from scratch model obtained a macro average AUC of 0.97 on the dataset of four classes excluding LCNEC, and 0.95 on the dataset of three subtypes of lung carcinomas; NSCLC, SCLC, and non-tumor, respectively. Of particular note is that the relatively simple CNN built from scratch may be an approach for pathological image analysis.

Lung cancer is a leading cause of death worldwide. Currently, in differential diagnosis of lung cancer, accurate classification of cancer types (adenocarcinoma, squamous cell carcinoma, and small cell carcinoma) is required. However, improving the accuracy and stability of diagnosis is challenging. In this study, we developed an automated classification scheme for lung cancers presented in microscopic images using a deep convolutional neural network (DCNN), which is a major deep learning technique. The DCNN used for classification consists of three convolutional layers, three pooling layers, and two fully connected layers. In evaluation experiments conducted, the DCNN was trained using our original database with a graphics processing unit. Microscopic images were first cropped and resampled to obtain images with resolution of 256 × 256 pixels and, to prevent overfitting, collected images were augmented via rotation, flipping, and filtering. The probabilities of three types of cancers were estimated using the developed scheme and its classification accuracy was evaluated using threefold cross validation. In the results obtained, approximately 71% of the images were classified correctly, which is on par with the accuracy of cytotechnologists and pathologists. Thus, the developed scheme is useful for classification of lung cancers from microscopic images.

ObjectivesThis study aimed to evaluate the value of nodal grouping (NG), defined as the presence of at least three contiguous lymph nodes (LNs) within one LN region, in staging and management of patients with non-metastatic nasopharyngeal carcinoma (NPC).MethodsMR images were reviewed to evaluate LN variables, including NG. The Kaplan–Meier method and multivariate Cox regression models evaluated the association between the variables and survival. Harrell’s concordance index (C-index) was used to measure the performance of prognostic models. The outcome of induction chemotherapy (IC) in patients with and without NG was compared using matched-pair analysis.ResultsIn 1224 patients enrolled, NG was found to be an independent prognostic factor for overall survival (OS), progression-free survival (PFS), distant metastasis-free survival (DMFS), and regional recurrence-free survival. The hazard ratio and 95% confidence interval (CI) of NG for OS (3.86, 2.09–7.12) were higher than those of stage N2 (3.54, 1.89–6.70). On upgrading patients with NG from stages N1 to N2, the revised N staging yielded a higher C-index compared to the American Joint Committee on Cancer system in predicting PFS (0.664 vs. 0.658, p = 0.022) and DMFS (0.699 vs. 0.690, p = 0.005). Results of the matched-pair analysis revealed that for patients with NG in stages N1 and N2, IC was correlated with improved OS (p = 0.022), PFS (p = 0.007), and DMFS (p = 0.021).ConclusionsNG is a significant prognostic factor for patients with NPC. Patients with NG may be upgraded from stages N1 to N2. NG was also a marker for identifying patients who would benefit from IC.Key Points• Nodal grouping, defined as the presence of at least three contiguous LNs within one LN region on MRI, was identified as a significant prognostic factor.• In patients with nasopharyngeal carcinoma, nodal grouping may influence lymph node staging.• Nodal grouping was a marker for identifying patients who may benefit from induction chemotherapy.

Background Since the definition of different histologic subtypes of urothelial carcinomas by the World Health Organization (WHO) 2004 classification, description of molecular features and clinical behavior of these variants has gained more attention. Methods We reviewed 205 tumor samples of patients with locally advanced bladder cancer mainly treated within the randomized AUO-AB05/95 trial with radical cystectomy and adjuvant cisplatin-based chemotherapy for histologic subtypes. 178 UC, 18 plasmacytoid (PUC) and 9 micropapillary (MPC) carcinomas of the bladder were identified. Kaplan Meier analysis and backward multivariate Cox’s proportional hazards regression analysis were performed to compare overall survival between the three histologic subtypes. Results Patients suffering from PUC have the worst clinical outcome regarding overall survival compared to conventional UC and MPC of the bladder that in turn seem have to best clinical outcome (27.4 months, 62.6 months, and 64.2 months, respectively; p=0.013 by Kaplan Meier analysis). Backward multivariate Cox´s proportional hazards regression analysis (adjusted to relevant clinicopathological parameters) showed a hazard ratio of 3.2 (p=0.045) for PUC in contrast to patients suffering from MPC. Conclusions Histopathological diagnosis of rare variants of urothelial carcinoma can identify patients with poor prognosis.

To assess immunohistochemical (IHC) stains differentially expressed between different types of lung cancer. We evaluated 16 different IHC stains in 209 prospectively included, surgically treated primary lung cancers, including 121 adenocarcinomas, 65 squamous cell carcinomas, 15 large-cell carcinomas, 5 adenosquamous carcinomas, 2 sarcomatoid carcinomas, and 1 small-cell carcinoma, using the tissue microarray technique. Cytokeratin 5 (CK5) and P63 were both positive in 10% or more of the cells in 97% of the squamous cell carcinomas, with the former being positive (<10% of the cells) in only 2 non-squamous cell carcinomas. Thyroid transcription factor 1 (TTF1) and napsin A were positive in 10% or more of the cells in 88% and 87% of the adenocarcinomas, respectively, with 94% of the adenocarcinomas being positive in at least 1 marker. Fifteen percent of the adenocarcinomas were positive for estrogen receptor. CK5, TTF1, and napsin A are sensitive markers for squamous cell carcinoma and adenocarcinoma of the lung.

Introduction The special types of breast cancer seem to have not only distinct morphological features but also distinct biological features. Materials and methods Women diagnosed with a first primary invasive breast cancer in the 2004–2005 period were identified through Tuscan Cancer Registry. Information on age, tumor size, lymph node status, histological type and grade, hormonal receptors, HER2 immunohistochemical expression were collected. Five subtypes were defined: luminal A, luminal B HER2+, luminal B HER2−, triple negative, and HER2 positive. The association between the histological type and molecular subgroups was assessed by a Fisher’s exact test, and a multinomial logistic regression model was used. Results Out of 1,487 patients, 34 % were luminal A subtype, 25 % luminal B HER2−, 11 % luminal B HER2+, 19 % triple negative, and 10.2 % HER2+; 58.5 % of cancers were ductal NOS types. With luminal A as reference, histological types distribution was significantly different between the subgroups. Mucinous, tubular, and cribriform histotypes were found among luminal A cancers more than in other subgroups; all medullary carcinomas were triple negative cancers. Pathological stage at diagnosis was more advanced, and histological grade was lower among subgroups other than luminal A. Conclusions Significant association between breast cancer histotypes and molecular subgroups was found.

Background/Objective Triple-negative breast cancer (TNBC) is a heterogeneous collection of breast tumors with numerous differences including morphological characteristics, genetic makeup, immune-cell infiltration, and response to systemic therapy. DNA methylation profiling is a robust tool to accurately identify disease-specific subtypes. We aimed to generate an epigenetic subclassification of TNBC tumors (epitypes) with utility for clinical decision-making. Methods Genome-wide DNA methylation profiles from TNBC patients generated in the Cancer Genome Atlas project were used to build machine learning-based epigenetic classifiers. Clinical and demographic variables, as well as gene expression and gene mutation data from the same cohort, were integrated to further refine the TNBC epitypes. Results This analysis indicated the existence of four TNBC epitypes, named as Epi-CL-A, Epi-CL-B, Epi-CL-C, and Epi-CL-D. Patients with Epi-CL-B tumors showed significantly shorter disease-free survival and overall survival [log rank; P  = 0.01; hazard ratio (HR) 3.89, 95% confidence interval (CI) 1.3–11.63 and P  = 0.003; HR 5.29, 95% CI 1.55–18.18, respectively]. Significant gene expression and mutation differences among the TNBC epitypes suggested alternative pathway activation that could be used as ancillary therapeutic targets. These epigenetic subtypes showed complementarity with the recently described TNBC transcriptomic subtypes. Conclusions TNBC epigenetic subtypes exhibit significant clinical and molecular differences. The links between genetic make-up, gene expression programs, and epigenetic subtypes open new avenues in the development of laboratory tests to more efficiently stratify TNBC patients, helping optimize tailored treatment approaches.

Purpose Our purposes were: 1) to estimate the prediction performance (PP) of cytology in identifying papillary thyroid carcinoma (PTC) subtypes; 2) to explore how the PTC subtypes distribute among the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) categories. Methods Nodules were included if both the histology with the PTC subtype report and the cytology report with the possible PTC subtype were available. The PP was calculated by making the proportion of True positives/False positives+false negatives. Results 309 cytologically “suspicious for malignancy” and “malignant” thyroid nodules with PTC histology were evaluated. ACR TI-RADS categorization for classical PTC was significantly different from non-classical PTC ( p- value 0.02). For the whole cohort the PP of cytologically classical cases was 0.74, while that of cytologically non classical cases was 0.41. ACR TI-RADS categorization was not significantly different for aggressive vs non-aggressive PTC subtypes ( p- value 0.1). When considering only aggressive or non-aggressive PTC subtypes, the PP of cytologically classical cases was respectively 0.86 and 0.87, while that of cytologically non classical cases was respectively 0.27 and 0.22. The PP of cytologically classical cases was 0.73 and 0.79, respectively for macroPTCs and microPTCs, while that of cytologically non classical cases was 0.55 and 0.33, respectively for macroPTCs and microPTCs. Conclusion Cytology examination reliably performed in predicting classical PTC versus non classical PTC subtypes. ACR TI-RADS categorization was significantly different among classical PTC versus non classical PTC subtypes.

Abstract Objectives Define if the presence of morphologic features of adenocarcinoma (ACA) in non–small cell lung carcinoma (NSCLC) on cytology specimens correlates with clinical and biologic features. Methods A total of 209 cases of NSCLC diagnosed on fine-needle aspiration in a 3-year period were included. Results After morphologic review, the cases were classified as ACA (n = 115), NSCLC favor ACA (n = 43), and NSCLC–not otherwise specified (NOS) (n = 18). Squamous cell (SCC) (n = 14) and NSCLC favor SCC (n = 19) were excluded from further analysis. Patients with EGFR-mutated tumors had longer overall survival than those with EGFR wild-type tumors (P = .01). When comparing cases with morphologic features of ACA, NSCLC favor ACA, and NSCLC-NOS, there were no differences in the presence or absence of tested mutations, clinical stage, or survival. Conclusion Patients diagnosed with pulmonary ACA, NSCLC favor ACA, or NSCLC-NOS in cytology specimens have similar clinical stage, survival, and molecular alterations.

The purpose of this study was to classify breast carcinomas based on variations in gene expression patterns derived from cDNA microarrays and to correlate tumor characteristics to clinical outcome. A total of 85 cDNA microarray experiments representing 78 cancers, three fibroadenomas, and four normal breast tissues were analyzed by hierarchical clustering. As reported previously, the cancers could be classified into a basal epithelial-like group, an ERBB2-overexpressing group and a normal breast-like group based on variations in gene expression. A novel finding was that the previously characterized luminal epithelial/estrogen receptor-positive group could be divided into at least two subgroups, each with a distinctive expression profile. These subtypes proved to be reasonably robust by clustering using two different gene sets: first, a set of 456 cDNA clones previously selected to reflect intrinsic properties of the tumors and, second, a gene set that highly correlated with patient outcome. Survival analyses on a subcohort of patients with locally advanced breast cancer uniformly treated in a prospective study showed significantly different outcomes for the patients belonging to the various groups, including a poor prognosis for the basal-like subtype and a significant difference in outcome for the two estrogen receptor-positive groups.