ArticleApplied bionics and biomechanics2021
Evaluation of the Risk of Recurrence in Patients with Local Advanced Rectal Tumours by Different Radiomic Analysis Approaches.
Article in Applied bionics and biomechanics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 13 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
13 citing papers in PubMed, 17 citations in OpenAlex.
- Quantitative Evaluation of Extramural Vascular Invasion of Rectal Cancer by Dynamic Contrast-Enhanced Magnetic Resonance Imaging.Contrast media & molecular imaging · 2022Trial
- Retracted: Evaluation of the Risk of Recurrence in Patients with Local Advanced Rectal Tumours by Different Radiomic Analysis Approaches.Applied bionics and biomechanics · 2023Article
- Predictive performance of radiomic models based on features extracted from pretrained deep networks.Insights into imaging · 2022Article
- Computed tomography-based radiomics nomogram for the preoperative prediction of perineural invasion in colorectal cancer: a multicentre study.Abdominal radiology (New York) · 2022Article
- Artificial Intelligence Technique of Synthesis and Characterizations for Measurement of Optical Particles in Medical Devices.Applied bionics and biomechanics · 2022Article
- Applying Dynamic Systems to Social Media by Using Controlling Stability.Computational intelligence and neuroscience · 2022Article
- The Practices of Solid Waste Utility and Thriving Conditions of Logistics (a Case of Tepi Town): A Study to Treat the Healthy Environment.Journal of environmental and public health · 2022Article
- Perception of the Impact of Artificial Intelligence in the Decision-Making Processes of Public Healthcare Professionals.Journal of environmental and public health · 2022Article
- Application of Radiomics in Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: Strategies and Challenges.Journal of oncology · 2022Review
- Hand Gesture of Recognition Pattern Analysis by Image Treatment Techniques.Computational and mathematical methods in medicine · 2022Article
- Comparative Study of Artificial Intelligence Techniques for the Diagnosis of Chronic Nerve Diseases.Computational and mathematical methods in medicine · 2022Article
- A Novel of New 7D Hyperchaotic System with Self-Excited Attractors and Its Hybrid Synchronization.Computational intelligence and neuroscience · 2021Article
- Implementation of Machine Learning Models for the Prevention of Kidney Diseases (CKD) or Their Derivatives.Computational intelligence and neuroscience · 2021Article
Corrections and comments
- Retraction · 2023-11-29Compromised Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The word radiomics, like all domains of type omics, assumes the existence of a large amount of data. Using artificial intelligence, in particular, different machine learning techniques, is a necessary step for better data exploitation. Classically, researchers in this field of radiomics have used conventional machine learning techniques (random forest, for example). More recently, deep learning, a subdomain of machine learning, has emerged. Its applications are increasing, and the results obtained so far have demonstrated their remarkable effectiveness. Several previous studies have explored the potential applications of radiomics in colorectal cancer. These potential applications can be grouped into several categories like evaluation of the reproducibility of texture data, prediction of response to treatment, prediction of the occurrence of metastases, and prediction of survival. Few studies, however, have explored the potential of radiomics in predicting recurrence-free survival. In this study, we evaluated and compared six conventional learning models and a deep learning model, based on MRI textural analysis of patients with locally advanced rectal tumours, correlated with the risk of recidivism; in traditional learning, we compared 2D image analysis models vs. 3D image analysis models, models based on a textural analysis of the tumour versus models taking into account the peritumoural environment in addition to the tumour itself. In deep learning, we built a 16-layer convolutional neural network model, driven by a 2D MRI image database comprising both the native images and the bounding box corresponding to each image.
Identifiers
What 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.