ABSTRACT
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.
ABSTRACT
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.
