Delta radiomics for rectal cancer response prediction using low field magnetic resonance guided radiotherapy: an external validation

Cusumano, D.; Boldrini, Luca; Yadav, Poonam; Gao, Yu; Musurunu, Bindu; Chiloiro, G.; Piras, Antonio; Lenkowicz, Jacopo; Placidi, L.; Romano, Angela; Luca, V. De; Votta, Claudio; Barbaro, Brunella; Gambacorta, Maria Antonietta; Bassetti, M.; Yang, Yingli; Indovina, Luca; Valentini, Vincenzo

doi:10.1016/j.ejmp.2021.03.038

Cited by 37 publications

(32 citation statements)

References 29 publications

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“…Interestingly, the largest majority of the published MRI radiomics studies takes into account histogram features (considered alone or in more advanced models based also on textural, shape, and filtered ones), supporting systematic investigations in this direction [ 28 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 ].…”

Section: Resultsmentioning

confidence: 76%

“…A first hypothesis generating experience identified two delta radiomics features (ΔL_least and Δglnu) as possible candidate predictors of a complete response (cCR) after rectal cancer neoadjuvant therapy using 0.35 T setup images [ 28 ]. The model was then externally validated using independent cohorts and achieved remarkable performances for both cCR and pCR (accuracy for cCR prediction: ΔLleast = 81% and Δglnu = 63%; accuracy for pCR prediction: ΔLleast 79% and Δglnu = 40%), confirming the interest in applying radiomics to hybrid MR images [ 150 ]. These observations may lead to a new generation of innovative trials, personalizing a patient’s treatment based on imaging-based prediction results (clinical trials NCT04815694).…”

Section: Resultsmentioning

confidence: 90%

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Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

Nardone

Boldrini

Grassi

et al. 2021

Cancers

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Introduction: Neoadjuvant radiotherapy is currently used mainly in locally advanced rectal cancer and sarcoma and in a subset of non-small cell lung cancer and esophageal cancer, whereas in other diseases it is under investigation. The evaluation of the efficacy of the induction strategy is made possible by performing imaging investigations before and after the neoadjuvant therapy and is usually challenging. In the last decade, texture analysis (TA) has been developed to help the radiologist to quantify and identify the parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye. The aim of this narrative is to review the impact of TA on the prediction of response to neoadjuvant radiotherapy and or chemoradiotherapy. Materials and Methods: Key references were derived from a PubMed query. Hand searching and ClinicalTrials.gov were also used. Results: This paper contains a narrative report and a critical discussion of radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma, and rectal cancer. Conclusions: Radiomics can shed a light on the setting of neoadjuvant therapies that can be used to tailor subsequent approaches or even to avoid surgery in the future. At the same, these results need to be validated in prospective and multicenter trials.

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Section: Resultsmentioning

confidence: 76%

Section: Resultsmentioning

confidence: 90%

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

Nardone

Boldrini

Grassi

et al. 2021

Cancers

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“…The key idea behind radiomics is that we can mine images by extracting image descriptors, called radiomic features, which can provide rich information about the tumour or healthy tissue and can be used to build predictive or prognostic models. This method allows quantitative analysis of different Image modalities and identification of patterns and correlations among voxels that can be of interest for improving diagnosis, prognosis and prediction of treatment outcomes [2][3][4]. Clinical outcomes can be therefore predicted employing radiomics features, potentially changing the treatment paradigm.…”

Section: Introductionmentioning

confidence: 99%

A Multicentre Evaluation of Dosiomics Features Reproducibility, Stability and Sensitivity

Placidi

Gioscio

Garibaldi

et al. 2021

Cancers

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Dosiomics is a texture analysis method to produce dose features that encode the spatial 3D distribution of radiotherapy dose. Dosiomic studies, in a multicentre setting, require assessing the features’ stability to dose calculation settings and the features’ capability in distinguishing different dose distributions. Dose distributions were generated by eight Italian centres on a shared image dataset acquired on a dedicated phantom. Treatment planning protocols, in terms of planning target volume coverage and dose–volume constraints to the organs at risk, were shared among the centres to produce comparable dose distributions for measuring reproducibility/stability and sensitivity of dosiomic features. In addition, coefficient of variation (CV) was employed to evaluate the dosiomic features’ variation. We extracted 38,160 features from 30 different dose distributions from six regions of interest, grouped by four features’ families. A selected group of features (CV < 3 for the reproducibility/stability studies, CV > 1 for the sensitivity studies) were identified to support future multicentre studies, assuring both stable features when dose distributions variation is minimal and sensitive features when dose distribution variations need to be clearly identified. Dosiomic is a promising tool that could support multicentre studies, especially for predictive models, and encode the spatial and statistical characteristics of the 3D dose distribution.

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“…As regards the textural features, three gray-level matrices were considered: run length (rlm), co-occurrence (cm), and size zone (szm) matrices. The complete list of the radiomic features extracted is reported in the Supplementary Materials, with similar experiences dealing with this topic ( 15 , 29 ).…”

Section: Methodsmentioning

confidence: 99%

Fractal-Based Radiomic Approach to Tailor the Chemotherapy Treatment in Rectal Cancer: A Generating Hypothesis Study

Dio¹,

Chiloiro²,

Cusumano³

et al. 2021

Front. Oncol.

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IntroductionThe aim of this study was to create a radiomic model able to calculate the probability of 5-year disease-free survival (5yDFS) when oxaliplatin (OXA) is or not administered in patients with locally advanced rectal cancer (LARC) and treated with neoadjuvant chemoradiotherapy (nCRT), allowing physicians to choose the best chemotherapy (CT) regimen.MethodsLARC patients with cT3–4 cN0 or cT1–4 cN1–2 were treated according to an nCRT protocol that included concomitant CT schedules with or without OXA and radiotherapy dose of 55 Gy in 25 fractions. Radiomic analysis was performed on the T2-weighted (T2-w) MR images acquired during the initial tumor staging. Statistical analysis was performed separately for the cohort of patients treated with and without OXA. The ability of every single radiomic feature in predicting 5yDFS as a univariate analysis was assessed using the Wilcoxon–Mann–Whitney (WMW) test or t-test. Two logistic models (one for each cohort) were calculated, and their performance was assessed using the area under the receiver operating characteristic (ROC) curve (AUC).ResultsA total of 176 image features belonging to four families (morphological, statistical, textural, and fractal) were calculated for each patient. At the univariate analysis, the only feature showing significance in predicting 5yDFS was the maximum fractal dimension of the subpopulation identified considering 30% and 50% as threshold levels (maxFD30–50). Once the models were developed using this feature, an AUC of 0.67 (0.57–0.77) and 0.75 (0.56–0.95) was obtained for patients treated with and without OXA, respectively. A maxFD30–50 >1.6 was correlated to a higher 5yDFS probability in patients treated with OXA.ConclusionThis study suggests that radiomic analysis of MR T2-w images can be used to define the optimal concomitant CT regimen for stage III LARC cancer patients. In particular, by providing an indication of the gross tumor volume (GTV) spatial heterogeneity at initial staging, maxFD30–50 seems to be able to predict the probability of 5yDFS. New studies including a larger cohort of patients and external validation sets are recommended to verify the results of this hypothesis-generating study.

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Delta radiomics for rectal cancer response prediction using low field magnetic resonance guided radiotherapy: an external validation

Cited by 37 publications

References 29 publications

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

A Multicentre Evaluation of Dosiomics Features Reproducibility, Stability and Sensitivity

Fractal-Based Radiomic Approach to Tailor the Chemotherapy Treatment in Rectal Cancer: A Generating Hypothesis Study

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