ArticleGastroenterology research and practice2019
Magnetic-Resonance-Imaging Texture Analysis Predicts Early Progression in Rectal Cancer Patients Undergoing Neoadjuvant Chemoradiation.
Article in Gastroenterology research and practice, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence with magnetic resonance imaging for prediction of pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer: A systematic review and meta-analysis.Frontiers in oncology · 2022Pooled it
- Response prediction of neoadjuvant chemoradiation therapy in locally advanced rectal cancer using CT-based fractal dimension analysis.European radiology · 2022Trial
- Personalized optimization of systematic prostate biopsy core number based on mpMRI radiomics features: a large-sample retrospective analysis.BMC cancer · 2025Article
- The Use of Radiomic Features to Predict Human Papillomavirus (HPV) Status in Head and Neck Tumors: A Review.Cureus · 2023Review
- Magnetic Resonance Imaging of the Gastrointestinal Tract: Current Role, Recent Advancements and Future Prospectives.Diagnostics (Basel, Switzerland) · 2023Review
- Gender Medicine in Clinical Radiology Practice.Journal of personalized medicine · 2023Review
- Review
- Article
- Role of CT texture analysis for predicting peritoneal metastases in patients with gastric cancer.La Radiologia medica · 2022Article
- Does restaging MRI radiomics analysis improve pathological complete response prediction in rectal cancer patients? A prognostic model development.La Radiologia medica · 2022Article
- Radiomic analysis to predict local response in locally advanced pancreatic cancer treated with stereotactic body radiation therapy.La Radiologia medica · 2022Article
- Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review.Diagnostics (Basel, Switzerland) · 2021Review
- Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment.Cancers · 2021Review
- Delta-radiomics and response to neoadjuvant treatment in locally advanced gastric cancer-a multicenter study of GIRCG (Italian Research Group for Gastric Cancer).Quantitative imaging in medicine and surgery · 2021Article
- A field strength independent MR radiomics model to predict pathological complete response in locally advanced rectal cancer.La Radiologia medica · 2021Article
- Endorectal Ultrasound and Magnetic Resonance Imaging for Rectal Cancer Staging: A Modern Multimodality Approach.Journal of clinical medicine · 2021Article
- MRI T2-weighted sequences-based texture analysis (TA) as a predictor of response to neoadjuvant chemo-radiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC).La Radiologia medica · 2020Article
- Review
- The value of multiparametric histogram features based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for the differential diagnosis of liver lesions.Annals of translational medicine · 2020Article
- MRI features and texture analysis for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy and tumor recurrence of locally advanced rectal cancer.European radiology · 2020Article
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14 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundWe hypothesized that texture analysis (TA) from the preoperative MRI can predict early disease progression (ePD), defined as the percentage of patients who relapsed or showed distant metastasis within three months from the radical surgery, in patients with locally advanced rectal cancer (LARC, stage II and III, AJCC) undergoing neoadjuvant chemoradiotherapy (C-RT).
methodsThis retrospective monoinstitutional cohort study included 49 consecutive patients in total with a newly diagnosed rectal cancer. All the patients underwent baseline abdominal MRI and CT scan of the chest and abdomen to exclude distant metastasis before C-RT. Texture parameters were extracted from MRI performed before C-RT (T1, DWI, and ADC sequences) using LifeX Software, a dedicated software for extracting texture parameters from radiological imaging. We divided the cohort in a training set of 34 patients and a validation set of 15 patients, and we tested the data sets for homogeneity, considering the clinical variables. Then we performed univariate and multivariate analysis, and a ROC curve was also generated.
resultsThirteen patients (26.5%) showed an ePD, three of whom with lung metastases and ten with liver relapse. The model was validated based on the prediction accuracy calculated in a previously unseen set of 15 patients. The prediction accuracy of the generated model was 82% (AUC = 0.853) in the training and 80% (AUC = 0.833) in the validation cohort. The only significant features at multivariate analysis was DWI GLCM Correlation (OR: 0.239,
conclusionOur results suggest that TA could be useful to identify patients that may develop early progression.
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