ArticleLa Radiologia medica2018
Fractal-based radiomic approach to predict complete pathological response after chemo-radiotherapy in rectal cancer.
Article in La Radiologia medica, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
72 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Role of radiomics in predicting lymph node metastasis in gastric cancer: a systematic review.Frontiers in medicine · 2023Pooled it
- Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment.European journal of nuclear medicine and molecular imaging · 2021Pooled it
- Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.European journal of nuclear medicine and molecular imaging · 2019Pooled it
- Radiomic Analysis of MRI for Assessing Response to Neoadjuvant Chemoradiotherapy in Rectal Adenocarcinoma: A Systematic Review and Metaanalysis.Molecular imagingPooled it
- Predicting locally advanced rectal cancer response to neoadjuvant therapy withEuropean journal of nuclear medicine and molecular imaging · 2019Trial
- Diagnostic efficacy of dual-energy computed tomography-based fractal analysis for assessing extramural venous invasion/tumor deposits and peripheral nerve invasion in rectal cancer.Quantitative imaging in medicine and surgery · 2026Article
- Can Radiomics Predict Pathologic Complete Response After Neoadjuvant Chemoradiotherapy for Rectal Cancer? A Systematic Review and Meta-Analysis of Diagnostic-Accuracy Studies.Journal of personalized medicine · 2025Review
- Article
- Visualization analysis of research hotspots and trends in MRI-based artificial intelligence in rectal cancer.Heliyon · 2024Article
- MOREOVER: multiomics MR-guided radiotherapy optimization in locally advanced rectal cancer.Radiation oncology (London, England) · 2024Article
- Operationalizing and digitizing person-centered daily functioning: a case for functionomics.BMC medical informatics and decision making · 2024Article
- Simulated deep CT characterization of liver metastases with high-resolution filtered back projection reconstruction.Visual computing for industry, biomedicine, and art · 2024Article
- Radiomics-enhanced early regression index for predicting treatment response in rectal cancer: a multi-institutional 0.35 T MRI-guided radiotherapy study.La Radiologia medica · 2024Article
- The Application of Radiomics and AI to Molecular Imaging for Prostate Cancer.Journal of personalized medicine · 2024Review
- Prediction of locally advanced rectal cancer response to neoadjuvant chemoradiation therapy using volumetric multiparametric MRI-based radiomics.Abdominal radiology (New York) · 2024Article
- Artificial Intelligence-Based Management of Adult Chronic Myeloid Leukemia: Where Are We and Where Are We Going?Cancers · 2024Review
- The Role of Radiomics in Rectal Cancer.Journal of gastrointestinal cancer · 2023Review
- Article
- KIT 1 (Keep in Touch) Project-Televisits for Cancer Patients during Italian Lockdown for COVID-19 Pandemic: The Real-World Experience of Establishing a Telemedicine System.Healthcare (Basel, Switzerland) · 2023Article
- Delta Radiomic Analysis of Mesorectum to Predict Treatment Response and Prognosis in Locally Advanced Rectal Cancer.Cancers · 2023Article
12 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study was to propose a methodology to investigate the tumour heterogeneity and evaluate its ability to predict pathologically complete response (pCR) after chemo-radiotherapy (CRT) in locally advanced rectal cancer (LARC). This approach consisted in normalising the pixel intensities of the tumour and identifying the different sub-regions using an intensity-based thresholding. The spatial organisation of these subpopulations was quantified using the fractal dimension (FD). This approach was implemented in a radiomic workflow and applied to 198 T2-weighted pre-treatment magnetic resonance (MR) images of LARC patients. Three types of features were extracted from the gross tumour volume (GTV): morphological, statistical and fractal features. Feature selection was performed using the Wilcoxon test and a logistic regression model was calculated to predict the pCR probability after CRT. The model was elaborated considering the patients treated in two institutions: Fondazione Policlinico Universitario "Agostino Gemelli" of Rome (173 cases, training set) and University Medical Centre of Maastricht (25 cases, validation set). The results obtained showed that the fractal parameters of the subpopulations have the highest performance in predicting pCR. The predictive model elaborated had an area under the curve (AUC) equal to 0.77 ± 0.07. The model reliability was confirmed by the validation set (AUC = 0.79 ± 0.09). This study suggests that the fractal analysis can play an important role in radiomics, providing valuable information not only about the GTV structure, but also about its inner subpopulations.
Indexed as
Identifiers
29230678What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.