ArticleCancers2023
Radiomics-Based Inter-Lesion Relation Network to Describe [
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed.
- Radiomic analysis of patient and interorgan heterogeneity in response to immunotherapies and BRAF-targeted therapy in metastatic melanoma.Journal for immunotherapy of cancer · 2025Article
- Predicting survival, neurotoxicity and response in B-cell lymphoma patients treated with CAR-T therapy using an imaging features-based model.EJNMMI research · 2024Article
- Radiomic-based prediction of lesion-specific systemic treatment response in metastatic disease.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2024Article
- Radiomic-Based Prediction of Lesion-Specific Systemic Treatment Response in Metastatic Disease.medRxiv : the preprint server for health sciences · 2024Article
- Radiomic-Based Approaches in the Multi-metastatic Setting: A Quantitative Review.medRxiv : the preprint server for health sciences · 2024Article
- Outsmarting Metastatic Prostate Cancer: Integration of Imaging, Liquid Biopsies and Biomarkers With Artificial Intelligence.Technology in cancer research & treatmentReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Advanced image analysis, including radiomics, has recently acquired recognition as a source of biomarkers, although there are some technical and methodological challenges to face for its application in the clinic. Among others, proper phenotyping of metastatic or systemic disease where multiple lesions coexist is an issue, since each lesion contributes to characterization of the disease. Therefore, the radiomic profile of each lesion should be modeled into a more complex architecture able to reproduce each "unit" (lesion) as a part of the "entire" (patient). This work aimed to characterize intra-tumor heterogeneity underpinning metastatic prostate cancer using an exhaustive innovative approach which consist of a i) feature transformation method to build an agnostic (i.e., irrespective of pre-existence knowledge, experience, and expertise) radiomic profile of lesions extracted from [
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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.