MRI-based radiomics to predict neoadjuvant chemoradiotherapy outcomes in locally advanced rectal cancer: A multicenter study

Xiang, Yirong; Li, Shuai; Wang, Hongzhi; Song, Maxiaowei; Hu, Ke; Wang, Fengwei; Wang, Zhi; Niu, Zhiyong; Liu, Jin; Cai, Yong; Li, Yongheng; Zhu, Xianggao; Geng, Jie; Zhang, Yangzi; Teng, Huajing; Wang, Weihu

doi:10.1016/j.ctro.2022.11.009

Cited by 6 publications

(7 citation statements)

References 65 publications

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“…All included papers have been published since 2015, with an increase from 2019. The most studied organs are the prostate [3] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] (S-Table I), female pelvis [4] , [41] , [42] , [43] , [44] , [45] , [46] , [47] , [48] , [49] , [50] , [51] , [52] (S-Table II), and rectum [5] , [53] , [54] , [55] , [56] , [57] , [58] , [59] , [60] , [61] , [62] , [63] , [64] , [65] , [66] , [67] , [68] , [69] (S-Table III), while a lower number of publications were related to the liver [6] , [70] , [71] , [72] , [73] , [74] , [75] (S-Table IV), and a miscellaneous group of organs including kidney [76] , [77] , …”

Section: Resultsmentioning

confidence: 99%

Section: B Clinical Analysismentioning

confidence: 99%

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Normalization Strategies in Multi-Center Radiomics Abdominal MRI: Systematic Review and Meta-Analyses

Panić

Defeudis

Balestra

et al. 2023

IEEE Open J. Eng. Med. Biol.

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Goal: Artificial intelligence applied to medical image analysis has been extensively used to develop non-invasive diagnostic and prognostic signatures. However, these imaging biomarkers should be largely validated on multi-center datasets to prove their robustness before they can be introduced into clinical practice. The main challenge is represented by the great and unavoidable image variability which is usually addressed using different pre-processing techniques including spatial, intensity and feature normalization. The purpose of this study is to systematically summarize normalization methods and to evaluate their correlation with the radiomics model performances through meta-analyses. This review is carried out according to the PRISMA statement: 4777 papers were collected, but only 74 were included. Two meta-analyses were carried out according to two clinical aims: characterization and prediction of response. Findings of this review demonstrated that there are some commonly used normalization approaches, but not a commonly agreed pipeline that can allow to improve performance and to bridge the gap between bench and bedside.

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Section: Resultsmentioning

confidence: 99%

Section: B Clinical Analysismentioning

confidence: 99%

Normalization Strategies in Multi-Center Radiomics Abdominal MRI: Systematic Review and Meta-Analyses

Panić

Defeudis

Balestra

et al. 2023

IEEE Open J. Eng. Med. Biol.

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“…As treatment responses are closely related to OS, seven studies [ 44 – 50 ] have investigated the application of radiomics for constructing models to predict treatment responses to radical radiotherapy, chemotherapy or chemoradiotherapy. For example, one of these studies [ 44 ] used quantitative US (QUS) delta-radiomics to monitor the response of HNC to radical radiotherapy.…”

Section: Clinical Applications Based On Radiomicsmentioning

confidence: 99%

“…The trained models were then applied to an external validation set and afforded AUCs of 0.983 and 0.818, respectively, demonstrating their potential utility as references for devising cancer-treatment plans. Table 7 [ 44 – 50 ] summarizes recent radiomics-based treatment response studies.…”

Section: Clinical Applications Based On Radiomicsmentioning

confidence: 99%

“…The excellent results obtained in these studies demonstrate the high generalizability and robustness of radiomics, making it a viable tool for clinical applications. The six prospective studies we reviewed [ 35 , 38 , 43 , 48 – 50 ] showed acceptable performance of the radiomics model, with AUCs ranging from 0.688 to 0.871. Although the lowest AUC value of 0.688 [ 35 ] suggests that the model has limited classification performance, it still demonstrates predictive power.…”

Section: Clinical Applications Based On Radiomicsmentioning

confidence: 99%

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Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling

Zhang

Cheng

et al. 2023

Military Med Res

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Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’ anatomy. However, the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians. Moreover, some potentially useful quantitative information in medical images, especially that which is not visible to the naked eye, is often ignored during clinical practice. In contrast, radiomics performs high-throughput feature extraction from medical images, which enables quantitative analysis of medical images and prediction of various clinical endpoints. Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis, demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine. However, radiomics remains in a developmental phase as numerous technical challenges have yet to be solved, especially in feature engineering and statistical modeling. In this review, we introduce the current utility of radiomics by summarizing research on its application in the diagnosis, prognosis, and prediction of treatment responses in patients with cancer. We focus on machine learning approaches, for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling. Furthermore, we introduce the stability, reproducibility, and interpretability of features, and the generalizability and interpretability of models. Finally, we offer possible solutions to current challenges in radiomics research.

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Can Pretreatment MRI and Planning CT Radiomics Improve Prediction of Complete Pathological Response in Locally Advanced Rectal Cancer Following Neoadjuvant Treatment?

Ramireddy,

Sathya,

Sasidharan

et al. 2024

J Gastrointest Canc

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MRI-based radiomics to predict neoadjuvant chemoradiotherapy outcomes in locally advanced rectal cancer: A multicenter study

Cited by 6 publications

References 65 publications

Normalization Strategies in Multi-Center Radiomics Abdominal MRI: Systematic Review and Meta-Analyses

Normalization Strategies in Multi-Center Radiomics Abdominal MRI: Systematic Review and Meta-Analyses

Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling

Can Pretreatment MRI and Planning CT Radiomics Improve Prediction of Complete Pathological Response in Locally Advanced Rectal Cancer Following Neoadjuvant Treatment?

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