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A novel MRI-based radiomics for preoperative prediction of lymphovascular invasion in rectal cancer.
Abdom Radiol (NY)
January 2025
Department of Radiology, Taizhou Municipal Hospital, Taizhou, Zhejiang, China.
Background: To develop and validate a clinical-radiomics model for preoperative prediction of lymphovascular invasion (LVI) in rectal cancer.
Methods: This retrospective study included data from 239 patients with pathologically confirmed rectal adenocarcinoma from two centers, all of whom underwent MRI examinations. Cases from the first center (n = 189) were randomly divided into a training set and an internal validation set at a 7:3 ratio, while cases from the second center (n = 50) constituted the external validation set.
[A Review of progresses in research on delayed resistance to EGFR-TKI by Traditional Chinese medicine via inhibiting cancer stem cells properties].
Xi Bao Yu Fen Zi Mian Yi Xue Za Zhi
January 2025
Department of Integrated Traditional Chinese and Western Medicine, Shandong First Medical University Affiliated Cancer Hospital, Jinan 250117, China. *Corresponding author, E-mail:
It has been popular and challenging to undertake researches on the delay of acquired resistance of epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKI). As key cells for tumor initiation, cancer stem cells (CSC) play an important role in the process of resistance to EGFR-TKI. Although preliminary studies found that traditional Chinese medicine (TCM) could inhibit CSC properties and delay EGFR-TKI resistance, the specific molecular mechanism remains unclear.
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- Deep learning methods show strong potential for predicting lung cancer risk from CT scans, but there's a need for more comprehensive comparisons and validations of these models in real-world settings.
- The study reviews 21 state-of-the-art deep learning models, analyzing their performance using CT scans from a subset of the National Lung Screening Trial, with a focus on malignant versus benign classification.
- Results reveal that 3D deep learning models generally outperformed 2D models, with the best 3D model achieving an AUROC of 0.86 compared to 0.79 for the best 2D model, emphasizing the need to choose appropriate pretrained datasets and model types for effective lung cancer risk prediction.
Can parenchymal volume analysis replace nuclear renal scans for split renal function before and after partial nephrectomy with warm ischemia?
Urol Oncol
January 2025
Department of Urology, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China; State Key Laboratory of Oncology in Southern China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; State Key Laboratory of Oncology in Southern China, Guangzhou, P. R. China. Electronic address:
Article Synopsis
- The study evaluates parenchymal volume analysis (PVA) as a potential alternative to nuclear renal scan (NRS) for assessing split renal function (SRF) before and after partial nephrectomy (PN) with warm ischemia.
- Preoperatively, PVA showed a strong correlation with NRS findings, indicating its reliability for SRF assessment (49.4% vs. 50.0%, P = .501).
- Results suggest that while PVA is consistent with NRS preoperatively, the efficacy of PVA remains uncertain for post-operative SRF evaluation after PN, indicating a need for further investigation.
A Radiomic-Clinical Model of Contrast-Enhanced Mammography for Breast Cancer Biopsy Outcome Prediction.
Acad Radiol
January 2025
Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213 (C.L., S.W.); Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213 (D.A., M.Z., J.S., S.W.); Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15213 (S.W.); Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA 15213 (S.W.). Electronic address:
Rationale And Objectives: In the USA over 1 million breast biopsies are performed annually. Approximately 9.6% diagnostic exams were given Breast Imaging Reporting and Data System (BI-RADS) ≥4A, most of which are 4A/4B.
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