Pre-Treatment Computed Tomography Radiomics for Predicting the Response to Neoadjuvant Chemoradiation in Locally Advanced Rectal Cancer: A Retrospective Study

Mao, Yun; Pei, Qian; Fu, Yan; Liu, Haipeng; Chen, Changyong; Li, Haiping; Gong, Guanghui; Yin, Hang; Pang, Peipei; Lin, Huashan; Xu, Biaoxiang; Zai, Hongyan; Yi, Xiaoping; Chen, Bihong T.

doi:10.3389/fonc.2022.850774

Cited by 10 publications

(10 citation statements)

References 50 publications

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“…The datasets included have both contrast and non-contrast enhanced CTs; with patients who underwent TNT having received contrast enhanced CT, while others got non-contrast enhanced CTs. However, we did not correct this since the impact of contrast agents on radiomics is not completely understood (31). The ideal situation would be to de ne the extent of the tumor on T2W high resolution MRI (32).…”

Section: Discussionmentioning

confidence: 99%

Can pre-treatment MRI and CT radiomics improve prediction of complete pathological response in locally advanced rectal cancer following neoadjuvant treatment?

Reddy,

et al. 2023

Preprint

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Objective(s): The treatment response to neoadjuvant chemoradiation (nCRT) differs largely in individuals treated for rectal cancer. In this study we investigated the role of radiomics to predict the pathological response in locally advanced rectal cancers at different treatment time points. 1) prior to start of any treatment using baseline T2-weighted MRI (T2W-MR) 2) at the start of radiation treatment using planning CT. Methods Patients who received nCRT followed by surgery between June 2017 to December 2019 were included in the study. Based on the histopathological tumour response grading (TRG) criteria, 58 patients with TRG 1 were classified as complete responders (pCR) and the rest as incomplete responders (IR). The gross tumour volume was defined by the radiation oncologists. Following resampling, 100 and 103 Pyradiomic features were extracted from T2W-MR and planning CT images, respectively. Synthetic minority oversampling technique (SMOTE) was applied to correct for class imbalance. Four Machine Learning classifiers were used to build clinical, radiomics and merged models. Model performance was evaluated on a held-out validation dataset following repeated stratified 3-fold cross validation using area-under-the-receiver-operator-characteristic curves (AUC) with bootstrap 95% confidence intervals. Results 150 patients were included. Clinical models performed better (AUC = 0.68) than the radiomics models (AUC = 0.62). Overall, the clinical + T2W-MR merged model performed the best (AUC = 0.72) in predicting the pathological response prior to therapy. Clinical + Planning CT merged models could only achieve highest AUC = 0.66. Conclusion Combining clinical and radiomics from baseline T2W-MR improves the prediction of pathological response in rectal cancer. Validation in larger cohorts is warranted before they can guide clinical decisions; especially in watch and wait strategies.

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Section: Discussionmentioning

confidence: 99%

Can pre-treatment MRI and CT radiomics improve prediction of complete pathological response in locally advanced rectal cancer following neoadjuvant treatment?

Reddy,

et al. 2023

Preprint

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“…Our study utilized the VASARI standard and combined it with contrast-enhanced 3D-T1-MPRAGE radiomics for analysis, which should be superior to using VASARI alone because radiomics analysis should be more objective, accurate, and reliable as a quantitative method. As a non-invasive diagnostic method, radiomic features extracted from images reflect cellular behaviors in the intratumoral microenvironment, which correlates with the prognosis of the tumor ( 44 – 46 ). Heterogeneity, an important parameter of the clinicopathological characteristics of gliomas, is associated with the degree of malignant behavior ( 47 ).…”

Section: Discussionmentioning

confidence: 99%

“…reliable as a quantitative method. As a non-invasive diagnostic method, radiomic features extracted from images reflect cellular behaviors in the intratumoral microenvironment, which correlates with the prognosis of the tumor (44)(45)(46). Heterogeneity, an important parameter of the clinicopathological characteristics of gliomas, is associated with the degree of malignant behavior (47).…”

Section: Discussionmentioning

confidence: 99%

The combination of radiomics features and VASARI standard to predict glioma grade

et al. 2023

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Background and PurposeRadiomics features and The Visually AcceSAble Rembrandt Images (VASARI) standard appear to be quantitative and qualitative evaluations utilized to determine glioma grade. This study developed a preoperative model to predict glioma grade and improve the efficacy of clinical strategies by combining these two assessment methods.Materials and MethodsPatients diagnosed with glioma between March 2017 and September 2018 who underwent surgery and histopathology were enrolled in this study. A total of 3840 radiomic features were calculated; however, using the least absolute shrinkage and selection operator (LASSO) method, only 16 features were chosen to generate a radiomic signature. Three predictive models were developed using radiomic features and VASARI standard. The performance and validity of models were evaluated using decision curve analysis and 10-fold nested cross-validation.ResultsOur study included 102 patients: 35 with low-grade glioma (LGG) and 67 with high-grade glioma (HGG). Model 1 utilized both radiomics and the VASARI standard, which included radiomic signatures, proportion of edema, and deep white matter invasion. Models 2 and 3 were constructed with radiomics or VASARI, respectively, with an area under the receiver operating characteristic curve (AUC) of 0.937 and 0.831, respectively, which was less than that of Model 1, with an AUC of 0.966.ConclusionThe combination of radiomics features and the VASARI standard is a robust model for predicting glioma grades.

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“…Recent studies in radiomics have highlighted the remarkable potential of CT-based radiomics analyses in predicting the response to CRT in LARC, as evidenced by numerous references [12][13][14][15][16][17][18][19][20]. The majority of these studies primarily focused on predicting the pCR status [12][13][14][15][16][17][18]. Despite these valuable contributions, the current body of research still faces several limitations that need to be addressed.…”

Section: Introductionmentioning

confidence: 99%

“…Incorporating relevant clinical factors in combination with radiomic features can provide a more comprehensive understanding of the predictive models, yielding more accurate and reliable results. Furthermore, it is paramount to explore a robust classifier to ensure the development of reliable predictive models [18]. Different machine learning algorithms should be evaluated to identify the most suitable and effective approach for predicting pCR in LARC based on radiomics scores.…”

Section: Introductionmentioning

confidence: 99%

Radiomics Signature Based on Support Vector Machines for the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

Li,

Chen,

Zhang

et al. 2023

Cancers

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The objective of this study was to evaluate the discriminative capabilities of radiomics signatures derived from three distinct machine learning algorithms and to identify a robust radiomics signature capable of predicting pathological complete response (pCR) after neoadjuvant chemoradiotherapy in patients diagnosed with locally advanced rectal cancer (LARC). In a retrospective study, 211 LARC patients were consecutively enrolled and divided into a training cohort (n = 148) and a validation cohort (n = 63). From pretreatment contrast-enhanced planning CT images, a total of 851 radiomics features were extracted. Feature selection and radiomics score (Radscore) construction were performed using three different machine learning methods: least absolute shrinkage and selection operator (LASSO), random forest (RF) and support vector machine (SVM). The SVM-derived Radscore demonstrated a strong correlation with the pCR status, yielding area under the receiver operating characteristic curves (AUCs) of 0.880 and 0.830 in the training and validation cohorts, respectively, outperforming the RF and LASSO methods. Based on this, a nomogram was developed by combining the SVM-based Radscore with clinical indicators to predict pCR after neoadjuvant chemoradiotherapy. The nomogram exhibited superior predictive power, achieving AUCs of 0.910 and 0.866 in the training and validation cohorts, respectively. Calibration curves and decision curve analyses confirmed its appropriateness. The SVM-based Radscore demonstrated promising performance in predicting pCR for LARC patients. The machine learning-driven nomogram, which integrates the Radscore and clinical indicators, represents a valuable tool for predicting pCR in LARC patients.

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Pre-Treatment Computed Tomography Radiomics for Predicting the Response to Neoadjuvant Chemoradiation in Locally Advanced Rectal Cancer: A Retrospective Study

Cited by 10 publications

References 50 publications

Can pre-treatment MRI and CT radiomics improve prediction of complete pathological response in locally advanced rectal cancer following neoadjuvant treatment?

Can pre-treatment MRI and CT radiomics improve prediction of complete pathological response in locally advanced rectal cancer following neoadjuvant treatment?

The combination of radiomics features and VASARI standard to predict glioma grade

Radiomics Signature Based on Support Vector Machines for the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

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