Deep learning detected histological differences between invasive and non-invasive areas of early esophageal cancer.

The depth of invasion plays a critical role in predicting the prognosis of early esophageal cancer, but the reasons behind invasion and the changes occurring in invasive areas are still not well understood. This study aimed to explore the morphological differences between invasive and non-invasive areas in early esophageal cancer specimens that have undergone endoscopic submucosal dissection (ESD), using artificial intelligence (AI) to shed light on the underlying mechanisms. In this study, data from 75 patients with esophageal squamous cell carcinoma (ESCC) were analyzed and endoscopic assessments were conducted to determine submucosal (SM) invasion.

Mathematical modeling predicts optimal immune checkpoint inhibitor and radiotherapy combinations and timing of administration.

Radiotherapy (RT) combined with immune checkpoint inhibitor (ICI) therapy has attracted substantial attention due to its potential to improve outcomes for patients with several types of cancer. However, the optimal administration timepoints and drug combinations remain unclear because the mechanisms underlying RT-induced changes in immune checkpoint molecule expression and interaction with their ligand(s) remain unclear. Herein, we demonstrated the dynamics of lymphocyte-mediated molecular interactions in tissue samples from esophageal cancer patients throughout RT schedules.

Conversion surgery after gemcitabine and cisplatin plus durvalumab for advanced intrahepatic cholangiocarcinoma: A case report.

Background: The combination of immune checkpoint inhibitors and chemotherapy has shown promising results for the treatment of advanced biliary tract cancer (BTC). Based on the results of the TOPAZ-1 trial, a gemcitabine and cisplatin plus durvalumab (GCD) regimen was recently approved as first-line therapy for patients with advanced BTC. However, post-GCD conversion surgery has not been previously studied.

Spatially resolved gene expression profiling of tumor microenvironment reveals key steps of lung adenocarcinoma development.

The interaction of tumor cells and their microenvironment is thought to be a key factor in tumor development. We present spatial RNA profiles obtained from 30 lung adenocarcinoma patients at the non-invasive and later invasive stages. We use spatial transcriptome sequencing data in conjunction with in situ RNA profiling to conduct higher resolution analyses.

NETosis in pulmonary pleomorphic carcinoma.

Pulmonary pleomorphic carcinoma (PC) is a rare non-small-cell lung carcinoma (NSCLC) with a poor prognosis, characterized by tumor necrosis (TN). NETosis is a form of neutrophil-specific cell death, which is morphologically characterized by prominent neutrophil infiltration and cell detritus in the necrotic foci. Seventy-six patients with pulmonary PC who underwent complete resection were enrolled.

A novel histopathological feature of spatial tumor-stroma distribution predicts lung squamous cell carcinoma prognosis.

We used a mathematical approach to investigate the quantitative spatial profile of cancer cells and stroma in lung squamous cell carcinoma tissues and its clinical relevance. The study enrolled 132 patients with 3-5 cm peripheral lung squamous cell carcinoma, resected at the National Cancer Center Hospital East. We utilized machine learning to segment cancer cells and stroma on cytokeratin AE1/3 immunohistochemistry images.

Quantification of Gremlin 1 throughout the tumor stroma using whole slide imaging and its clinicopathological significance in gastric cancer.

Gremlin 1 (GREM1) is an antagonist of bone morphogenetic protein (BMP). GREM1 is expressed in the stromal cells of various carcinomas and promotes tumor progression by suppressing BMP signaling. We designed this study to establish an evaluation strategy for GREM1 expression, focusing on the tumor stroma, and to examine its clinicopathological significance in gastric cancer (GC) progression.

Predicting lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma: collaboration between artificial intelligence and pathologists.

Researchers have attempted to identify the factors involved in lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma (SCC). However, studies combining histopathological and clinicopathological information in prediction models are limited. We aimed to develop a highly accurate lymph node recurrence prediction model for clinical stage T1-2, N0 (cT1-2N0) tongue SCC by integrating histopathological artificial intelligence (AI) with clinicopathological information.

Clinicopathological differences between EGFR mutated and EGFR wild-type lung adenocarcinoma with papillary predominant pattern.

Objectives: We aimed to reveal the clinicopathological differences between epidermal growth factor receptor (EGFR)-mutated and wild-type (WT) lung adenocarcinoma (LUAD) focusing on the predominant subtype.

Methods: This study included 352 with EGFR mutation and 370 with WT patients in consecutive stage I LUAD classified by the predominant subtype, and their clinicopathological characteristics and prognosis were analyzed. Using the Cancer Genome Atlas Program (TCGA) cohort, we analyzed differences in gene expression between EGFR mutation and WT groups.

Extracting interpretable features for pathologists using weakly supervised learning to predict p16 expression in oropharyngeal cancer.

One drawback of existing artificial intelligence (AI)-based histopathological prediction models is the lack of interpretability. The objective of this study is to extract p16-positive oropharyngeal squamous cell carcinoma (OPSCC) features in a form that can be interpreted by pathologists using AI model. We constructed a model for predicting p16 expression using a dataset of whole-slide images from 114 OPSCC biopsy cases.

Survival outcome of patient with pT1N0 biliary tract cancer treated with surgery alone.

Introduction: Adjuvant chemotherapy (AC) with S-1 or capecitabine monotherapy is now the standard of care for resected biliary tract cancer (BTC) according to the Adjuvant S-1 for Cholangiocarcinoma Trial (ASCOT) and the BILCAP study. Patients selection criteria, especially regarding pT1N0 BTC, differed in both trials. We aimed to clarify the survival outcomes regarding resected pT1N0 BTC without AC.

The prognostic impact of a high number of peritumoral alveolar macrophages in neuroendocrine carcinoma in the lung.

Alveolar macrophages (AMs) are resident macrophages in the lungs; however, whether the number of AMs plays a role in the lung neuroendocrine tumor (NET) prognosis remains unclear. We counted the number of AMs located around the tumor (peritumoral alveolar macrophages [pAMs]) and the number of AMs located apart from the tumor (distant macrophages; dAMs). In 73 cases of neuroendocrine carcinoma (NEC: small cell lung carcinoma and large cell neuroendocrine carcinoma), the group that contained higher pAMs (≥86/μm ) revealed shorter recurrent-free survival (RFS) than those with lower pAMs (<86/μm ) (p = 0.

Common clinicopathological and immunological features of sarcomatoid carcinoma across organs: A histomorphology-based cross-organ study.

Sarcomatoid carcinoma (SC), which can occur in any organ, is a rare disease. To elucidate common characteristics of SC beyond organs, we evaluated clinicopathological and immunological features of SC defined by the single histological criterion beyond organs compared to randomly matched conventional carcinoma (non-SC) adjusted for the disease stage. Immunological features were assessed by multiplex immunohistochemistry, comparing immune cell density in tumor tissues and tumor programmed death-ligand 1 (PD-L1) expression.