The MDESM model, based on a feature disentanglement and multimodal fusion strategy, demonstrates the potential to effectively combine MRI and clinical data to achieve accurate PSM prediction and offers a promising tool for preoperative risk stratification and surgical planning in prostate cancer.
Introduction To develop and externally validate a preoperative CT imaging model integrating primary tumour and visceral adipose tissue (VAT) features for predicting recurrence in colorectal cancer (CRC). Materials and methods This retrospective multicentre study included 1,369 patients with CRC undergoing upfront curat...
Peng-Fei Wei, Lin-Hua Zhong, Yong-Ming Tan et al.· Frontiers in Molecular Biosc...· 0 citations
Objectives: Cervical cancer (CC) remains a leading cause of cancer-related morbidity and mortality among women worldwide, with postoperative recurrence posing a major challenge to long-term survival. Multimodal data integration, including imaging, textual, and clinical information, holds significant potential for enhan...
Yi-Yang Wu, Yao Ai, Yang-Yang Zhang et al.· Cancers· 0 citations
Objective
To achieve accurate prediction of early recurrence (ER) in locally advanced gastric cancer (LAGC) patients after neoadjuvant therapy (NAT) and surgery, we constructed a deep learning fusion model integrating preoperative computed tomography (CT) imaging and perioperative clinicopathological features.
Method...
Cai-Ming Weng, Q. Zhong, Li-Hong Chen et al.· Chinese journal of cancer re...· 0 citations
Several MRI-derived parameters, including capsular contact length, MRI-suspected extraprostatic extension, and urethral proximity, were associated with an increased likelihood of positive surgical margins, and incorporation of these metrics alongside clinical factors may improve risk stratification.
Christoph Niessen, Yannick Gromes, S. Engelmann et al.· Urologia internationalis· 0 citations
Breast cancer patients receiving neoadjuvant treatment may benefit from a presurgical assessment of complete response to avoid unnecessary surgeries. We aimed to develop a machine learning model that predicts pathological complete response (pCR) based on results from different image modalities as well as tumor and pati...
Eva Reisig, Lie Cai, Maria Müller et al.· BMC Cancer· 0 citations
It is suggested that ultrasound-based transfer learning provides promising non-invasive biomarkers for preoperative LVSI prediction in endometrial cancer, with the integration of ultrasound-derived indicators further improving performance and offering a pragmatic alternative to MRI.
R. Mancari, Erica Lasaracina, G. Cormio et al.· Scientific Reports· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.