Brain metastasis magnetic resonance imaging-based deep learning for predicting epidermal growth factor receptor () mutation and subtypes in metastatic non-small cell lung cancer.

Background: The preoperative identification of epidermal growth factor receptor () mutations and subtypes based on magnetic resonance imaging (MRI) of brain metastases (BM) is necessary to facilitate individualized therapy. This study aimed to develop a deep learning model to preoperatively detect mutations and identify the location of mutations in patients with non-small cell lung cancer (NSCLC) and BM.

Methods: We included 160 and 72 patients who underwent contrast-enhanced T1-weighted (T1w-CE) and T2-weighted (T2W) MRI at Liaoning Cancer Hospital and Institute (center 1) and Shengjing Hospital of China Medical University (center 2) to form a training cohort and an external validation cohort, respectively.

Improved Prediction of Epidermal Growth Factor Receptor Status by Combined Radiomics of Primary Nonsmall-Cell Lung Cancer and Distant Metastasis.

Objectives: This study aimed to investigate radiomics based on primary nonsmall-cell lung cancer (NSCLC) and distant metastases to predict epidermal growth factor receptor (EGFR) mutation status.

Methods: A total of 290 patients (mean age, 58.21 ± 9.

Intra- and peritumoral radiomics for predicting early recurrence in patients with high-grade serous ovarian cancer.

Purpose: To explore values of intra- and peritumoral CT-based radiomics for predicting recurrence in high-grade serous ovarian cancer (HGSOC) patients.

Methods: This study enrolled 110 HGSOC patients from our hospital between Aug 2017 and Apr 2021. All patients underwent contrast-enhanced CT scans before treatment.

LWMA-Net: Light-weighted morphology attention learning for human embryo grading.

Visual inspection of embryo morphology is routinely used in embryo assessment and selection. However, due to the complexity of morphologies and large inter- and intra-observer variances among embryologists, manual evaluations remain to be subjective and time-consuming. Thus, we proposed a light-weighted morphology attention learning network (LWMA-Net) for automatic assistance on embryo grading.