Delta Radiomic Analysis of Mesorectum to Predict Treatment Response and Prognosis in Locally Advanced Rectal Cancer

Chiloiro, G.; Cusumano, D.; Romano, Angela; Boldrini, Luca; Nicoli, G.; Votta, Claudio; Tran, Huong Elena; Barbaro, Brunella; Carano, Davide; Valentini, Vincenzo; Gambacorta, Maria Antonietta

doi:10.3390/cancers15123082

Cited by 6 publications

(2 citation statements)

References 53 publications

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“…Moreover, Chiloiro G et al. found that the best performing two-year DFS prediction model was developed on the basis of tumor volume as well as mesorectal features ( 33 ).…”

Section: Discussionmentioning

confidence: 99%

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Prediction of pathologic complete response to neoadjuvant chemoradiation in locally advanced rectal cancer

Zhong,

Zeng,

Zhang

et al. 2024

Front. Oncol.

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PurposeTo investigate the predictive factors of pathologic complete response (pCR) in locally advanced rectal cancer (LARC) patients who had been treated with neoadjuvant chemoradiation (nCRT).Methods and materialsFor this retrospective study, 53 LARC patients (37 males and 16 females; age range 25 to 79 years) were selected. Clinical characteristics, baseline mrTNM staging, MR gross tumor volumes (GTV), and pCR were evaluated. The diagnostic accuracy of GTV for predicting pCR was calculated.ResultsAmong 53 LARC patients, 15 patients achieved pCR (28.3%), while 38 patients achieved non-pCR. Only three (5.7%) out of 53 patients did not downstage after nCRT. GTV and tumor differentiation were the significant prognostic parameters for predicting pCR. A tumor volume threshold of 21.1 cm3 was determined as a predictor for pCR, with a sensitivity of 84% and specificity of 47%. In addition, GTV was associated with mrN stage, circumferential resection margin (CRM) status, extramural vascular invasion (EMVI) status, and pretreatment serum CEA level.ConclusionTumor volume and tumor differentiation have significant predictive values in preoperative assessment of pCR among LARC patients. These findings aid clinicians to discriminate those patients who may likely benefit from preoperative regimens and to make optimal treatment plans.

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“…Moreover, Chiloiro G et al. found that the best performing two-year DFS prediction model was developed on the basis of tumor volume as well as mesorectal features ( 33 ).…”

Section: Discussionmentioning

confidence: 99%

“…year DFS prediction model was developed on the basis of tumor volume as well as mesorectal features(33).…”

mentioning

confidence: 99%

Prediction of pathologic complete response to neoadjuvant chemoradiation in locally advanced rectal cancer

Zhong,

Zeng,

Zhang

et al. 2024

Front. Oncol.

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Image-based artificial intelligence for the prediction of pathological complete response to neoadjuvant chemoradiotherapy in patients with rectal cancer: a systematic review and meta-analysis

Shen,

Jin,

Chen

et al. 2024

Radiol med

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Delta radiomics: an updated systematic review

Nardone,

Reginelli,

Rubini

et al. 2024

Radiol med

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Background Radiomics can provide quantitative features from medical imaging that can be correlated with various biological features and diverse clinical endpoints. Delta radiomics, on the other hand, consists in the analysis of feature variation at different acquisition time points, usually before and after therapy. The aim of this study was to provide a systematic review of the different delta radiomics approaches. Methods Eligible articles were searched in Embase, Pubmed, and ScienceDirect using a search string that included free text and/or Medical Subject Headings (MeSH) with 3 key search terms: 'radiomics,' 'texture,' and 'delta.' Studies were analyzed using QUADAS-2 and the RQS tool. Results Forty-eight studies were finally included. The studies were divided into preclinical/methodological (5 studies, 10.4%); rectal cancer (6 studies, 12.5%); lung cancer (12 studies, 25%); sarcoma (5 studies, 10.4%); prostate cancer (3 studies, 6.3%), head and neck cancer (6 studies, 12.5%); gastrointestinal malignancies excluding rectum (7 studies, 14.6%) and other disease sites (4 studies, 8.3%). The median RQS of all studies was 25% (mean 21% ± 12%), with 13 studies (30.2%) achieving a quality score < 10% and 22 studies (51.2%) < 25%. Conclusions Delta radiomics shows potential benefit for several clinical endpoints in oncology, such asdifferential diagnosis, prognosis and prediction of treatment response, evaluation of side effects. Nevertheless, the studies included in this systematic review suffer from the bias of overall low methodological rigor, so that the conclusions are currently heterogeneous, not robust and hardly replicable. Further research with prospective and multicenter studies is needed for the clinical validation of delta radiomics approaches.

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Delta Radiomic Analysis of Mesorectum to Predict Treatment Response and Prognosis in Locally Advanced Rectal Cancer

Cited by 6 publications

References 53 publications

Prediction of pathologic complete response to neoadjuvant chemoradiation in locally advanced rectal cancer

Prediction of pathologic complete response to neoadjuvant chemoradiation in locally advanced rectal cancer

Image-based artificial intelligence for the prediction of pathological complete response to neoadjuvant chemoradiotherapy in patients with rectal cancer: a systematic review and meta-analysis

Delta radiomics: an updated systematic review

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