Ensemble Machine Learning Classifiers Combining CT Radiomics and Clinical-Radiological Features for Preoperative Prediction of Pathological Invasiveness in Lung Adenocarcinoma Presenting as Part-Solid Nodules: A Multicenter Retrospective Study.

BackgroundLung adenocarcinomas manifesting as part-solid nodules (PSNs) represent a distinct clinical subtype where accurate preoperative determination of pathological invasiveness critically influences both prognosis and surgical decision-making. This multicenter study aims to develop an ensemble machine learning classifier that integrates computed tomography (CT) radiomic signatures with clinical-radiological features to enhance the preoperative prediction of invasive status.MethodsWe retrospectively analyzed 344 patients with pathologically confirmed lung adenocarcinoma presenting as PSNs across three medical centers.

Radiomics integration based on intratumoral and peritumoral computed tomography improves the diagnostic efficiency of invasiveness in patients with pure ground-glass nodules: a machine learning, cross-sectional, bicentric study.

Background: Radiomics has shown promise in the diagnosis and prognosis of lung cancer. Here, we investigated the performance of computed tomography-based radiomic features, extracted from gross tumor volume (GTV), peritumoral volume (PTV), and GTV + PTV (GPTV), for predicting the pathological invasiveness of pure ground-glass nodules present in lung adenocarcinoma.

Methods: This was a retrospective, cross-sectional, bicentric study with data collected from January 1, 2018, to June 1, 2022.

Assessment of intratumor heterogeneity for preoperatively predicting the invasiveness of pulmonary adenocarcinomas manifesting as pure ground-glass nodules.

Background: The increased use of low-dose computed tomography (CT) for lung cancer screening has improved the detection of ground-glass nodules. However, as the clinical utility of CT findings to predict the invasiveness of pure ground-glass nodules (pGGNs) is currently limited, differentiating pGGNs that indicate invasive adenocarcinoma (IAC) from those that represent other histological entities is challenging. We aimed to quantify intratumor heterogeneity of lung adenocarcinomas characterized by pGGNs on CT to assess its efficacy in predicting IACs before surgery.

CT Radiomic Nomogram Using Optimal Volume of Interest for Preoperatively Predicting Invasive Mucinous Adenocarcinomas in Patients with Incidental Pulmonary Nodules: A Multicenter, Large-Scale Study.

Introduction: This study evaluated the efficacy of radiomic analysis with optimal volumes of interest (VOIs) on computed tomography images to preoperatively differentiate invasive mucinous adenocarcinoma (IMA) from non-mucinous adenocarcinoma (non-IMA) in patients with incidental pulmonary nodules (IPNs).

Methods: This multicenter, large-scale retrospective study included 1383 patients with IPNs, 110 (8%) of whom were pathologically diagnosed with IMA postoperatively. Radiomic features were extracted from multi-scale VOI subgroups (VOI, VOI, VOI , and VOI ).

Radiomics Nomogram Based on Optimal Volume of Interest Derived from High-Resolution CT for Preoperative Prediction of IASLC Grading in Clinical IA Lung Adenocarcinomas: A Multi-Center, Large-Population Study.

The novel grading system developed by the International Association for the Study of Lung Cancer (IASLC) for clinical stage IA lung adenocarcinomas has demonstrated remarkable prognostic capabilities. Notably, tumors classified as grade 3 have been associated with poor prognostic outcomes, thereby playing a crucial role in the formulation of personalized surgical strategies. The objective of this study is to develop a radiomics nomogram that utilizes the optimal volume of interest (VOI) derived from high-resolution CT (HRCT) scans to accurately predict the presence of grade 3 tumors in patients with clinical IA lung adenocarcinomas.

Peritumoral radiomics increases the efficiency of classification of pure ground-glass lung nodules: a multicenter study.

Purpose: We aimed to evaluate the efficiency of computed tomography (CT) radiomic features extracted from gross tumor volume (GTV) and peritumoral volumes (PTV) of 5, 10, and 15 mm to identify the tumor grades corresponding to the new histological grading system proposed in 2020 by the Pathology Committee of the International Association for the Study of Lung Cancer (IASLC).

Methods: A total of 151 lung adenocarcinomas manifesting as pure ground-glass lung nodules (pGGNs) were included in this randomized multicenter retrospective study. Four radiomic models were constructed from GTV and GTV + 5/10/15-mm PTV, respectively, and compared.

Assessment of Lymphovascular Invasion in Breast Cancer Using a Combined MRI Morphological Features, Radiomics, and Deep Learning Approach Based on Dynamic Contrast-Enhanced MRI.

Background: Assessment of lymphovascular invasion (LVI) in breast cancer (BC) primarily relies on preoperative needle biopsy. There is an urgent need to develop a non-invasive assessment method.

Purpose: To develop an effective model to assess the LVI status in patients with BC using magnetic resonance imaging morphological features (MRI-MF), Radiomics, and deep learning (DL) approaches based on dynamic contrast-enhanced MRI (DCE-MRI).

Survival benefit after neoadjuvant or adjuvant radiotherapy for stage II-III gastroesophageal junction adenocarcinoma: A large population-based cohort study.

Objective: The standard treatment for stage II-III gastroesophageal junction adenocarcinoma (GEJA) remains controversial, and the role of radiotherapy (RT) in stage II-III GEJA is unclear. Herein, we aimed to evaluate the prognosis of different RT sequences and identify potential candidates to undergo neoadjuvant RT (NART) or adjuvant RT (ART).

Materials And Methods: In total, we enrolled 3,492 patients with resectable stage II-III GEJA from the Surveillance, Epidemiology, and End Results (SEER) database, subsequently assigned to three categories: TN, TN, and TN.