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). STUDY TYPE: Cross-sectional retrospective cohort study. POPULATION: The study included 206 BC patients, with 136 in the training set [97 LVI(-) and 39 LVI(+) cases; median age: 51.5 years] and 70 in the test set [52 LVI(-) and 18 LVI(+) cases; median age: 48 years]. FIELD STRENGTH/SEQUENCE: 1.5 T/T1-weighted images, fat-suppressed T2-weighted images, diffusion-weighted imaging (DWI), and DCE-MRI. ASSESSMENT: The MRI-MF model was developed with conventional MR features using logistic analyses. The Radiomic feature extraction process involved collecting data from categorized DCE-MRI datasets, specifically the first and second post-contrast images (A1 and A2). Next, a DL model was implemented to determine LVI. Finally, we established a joint diagnosis model by combining the MRI-MF, Radiomics, and DL approaches. STATISTICAL TESTS: Diagnostic performance was compared using receiver operating characteristic curve analysis, confusion matrix, and decision curve analysis. RESULTS: Rim sign and peritumoral edema features were used to develop the MRI-MF model, while six Radiomics signature from the A1 and A2 images were used for the Radiomics model. The joint model (MRI-MF + Radiomics + DL models) achieved the highest accuracy (area under the curve [AUC] = 0.857), being significantly superior to the MRI-MF (AUC = 0.724), Radiomics (AUC = 0.736), or DL (AUC = 0.740) model. Furthermore, it also outperformed the pairwise combination models: Radiomics + MRI-MF (AUC = 0.796), DL + MRI-MF (AUC = 0.796), or DL + Radiomics (AUC = 0.826). DATA CONCLUSION: The joint model incorporating MRI-MF, Radiomics, and DL approaches can effectively determine the LVI status in patients with BC before surgery. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

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. The diagnostic performance of the different models was evaluated using receiver operating characteristic curve analysis RESULTS: The pGGNs were classified into grade 1 (117), 2 (34), and 3 (0), according to the IASLC grading system. In all four radiomic models, pGGNs of grade 2 had significantly higher radiomic scores than those of grade 1 (P < 0.05). The AUC of the GTV and GTV + 5/10/15-mm PTV were 0.869, 0.910, 0.951, and 0.872 in the training cohort and 0.700, 0.715, 0.745, and 0.724 in the validation cohort, respectively. CONCLUSIONS: The radiomic features we extracted from the GTV and PTV of pGGNs could effectively be used to differentiate grade-1 and grade-2 tumors. In particular, the radiomic features from the PTV increased the efficiency of the diagnostic model, with GTV + 10 mm PTV exhibiting the highest efficacy.

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: T1-2N+, T3-4N-, and T3-4N+. Survival curves were evaluated using the Kaplan-Meier method along with the log-rank test. We compared survival curves for NART, ART, and non-RT in the three categories. To further determine histological types impacting RT-associated survival, we proposed new categories by combining the tumor, node, and metastasis (TNM) stage with Lauren's classification. Results: ART afforded a significant survival benefit in patients with T1-2N+ and T3-4N+ tumors. In addition, NART conferred a survival advantage in patients with T3-4N+ and T3-4 exhibiting the intestinal type. Notably, ART and NART were both valuable in patients with T3-4N+, although no significant differences between treatment regimens were noted. Conclusions: Both NART and ART can prolong the survival of patients with stage II-III GEJA. Nevertheless, the selection of NART or ART requires a concrete analysis based on the patient's condition.