Role of MRI‑based radiomics in locally advanced rectal cancer (Review)

Zhang, Siyu; Yu, Mengchao; Chen, Dan; Li, Peidong; Tang, Bin; Li, Jie

doi:10.3892/or.2021.8245

Cited by 19 publications

(14 citation statements)

References 103 publications

(100 reference statements)

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“…In addition, as studies on radionomic analysis to improve the prediction of pCR have recently been reported, further imaging methods may help select high-risk patients. 20 21 22 …”

Section: Discussionmentioning

confidence: 99%

Benefits of Adjuvant Chemotherapy for Clinical T3-4N0 Rectal Cancer After Preoperative Chemoradiotherapy

et al. 2023

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While the guidelines for adjuvant chemotherapy (AC) for colon cancer are relatively standardized, those for early rectal cancer are still lacking. We therefore evaluated the role of AC in clinical stage II rectal cancer treatment after preoperative chemoradiotherapy (CRT). Patients diagnosed with early rectal cancer (defined by clinical stage T3/4, N0) who completed CRT followed by surgery were enrolled in this retrospective study. To evaluate the role of AC, we analyzed the risk of recurrence and survival based on clinicopathologic parameters and adjuvant chemotherapy. Of the 112 patients, 11 patients (9.8%) experienced recurrence and five patients (4.8%) died. In a multivariate analysis, circumferential resection margin involvement (CRM+) on magnetic resonance imaging at diagnosis, CRM involvement following neoadjuvant therapy (ypCRM+), tumor regression grade (≤G1) and no-AC were considered poor prognostic factors for recurrence free survival (RFS). In addition, ypCRM+ and no-AC were associated with poor overall survival (OS) in the multivariate analysis. AC including 5-FU monotherapy demonstrated the benefits of reduced recurrence and prolonged survival in clinical stage II rectal cancer, even in pathologic stage following neoadjuvant therapy (ypStage) 0-I. Further prospective studies are needed to verify the benefit of each regimen of AC and the development of a method that can accurately predict CRM status before surgery, and a vigorous treatment that can induce CRM non-involvement (CRM-) should be considered even in early stages of rectal cancer.

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“…In addition, as studies on radionomic analysis to improve the prediction of pCR have recently been reported, further imaging methods may help select high-risk patients. 20 21 22 …”

Section: Discussionmentioning

confidence: 99%

Benefits of Adjuvant Chemotherapy for Clinical T3-4N0 Rectal Cancer After Preoperative Chemoradiotherapy

et al. 2023

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“…Those features are mathematical descriptions of the visual properties of an image [20][21][22]. For instance, numerical values are included, such as actual pixel intensities, edge strengths, shape descriptors, and regional variation or texture of pixel value [23], which can provide accurate and reliable evidence to aid in clinical decision-making [24,25]. For instance, a review study conducted by Lohmann et al illustrated the relationship between imaging features and clinicopathological manifestations of gliomas, in which the value of the features in diagnosing gliomas was demonstrated.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Glioma Grade by Tumor Heterogeneity Radiomic Analysis Based on Multiparametric MRI

Deng

Zhu

2023

Int J Comput Intell Syst

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Predicting glioma grade plays a pivotal role in treatment and prognosis. However, several current methods for grading depend on the characteristics of the whole tumor. Predicting grade by analyzing tumor subregions has not been thoroughly investigated, which aims to improve the prediction performance. To predict glioma grade via analysis of tumor heterogeneity with features extracted from tumor subregions, it is mainly divided into four magnetic resonance imaging (MRI) sequences, including T2-weighted (T2), fluid-attenuated inversion recovery (FLAIR), pre-gadolinium T1-weighted (T1), and post-gadolinium T1-weighted methods. This study included the data of 97 patients with glioblastomas and 42 patients with low-grade gliomas before surgery. Three subregions, including enhanced tumor (ET), non-enhanced tumor, and peritumoral edema, were obtained based on segmentation labels generated by the GLISTRBoost algorithm. One hundred radiomic features were extracted from each subregion. Feature selection was performed using the cross-validated recursive feature elimination with a support vector machine (SVM) algorithm. SVM classifiers with grid search were established to predict glioma grade based on unparametric and multiparametric MRI. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the classifiers, and the performance of the subregions was compared with the results of the whole tumor. In uniparametric analysis, the features from the ET subregion yielded a higher AUC value of 0.8697, 0.8474, and 0.8474 than those of the whole tumor of FLAIR, T1, and T2. In multiparametric analysis, the ET subregion achieved the best performance (AUC = 0.8755), which was higher than the uniparametric results. Radiomic features from the tumor subregion can potentially be used as clinical markers to improve the predictive accuracy of glioma grades.

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“…Once radiomics will be included in the clinical practice, it could help in improving the management of patients with LARC, providing tailored treatments, avoiding unnecessary toxicity to patient predicted unlikely to respond, and consequently anticipating the radical treatment or switching to an intensified treatment when necessary. Recent studies have explored the potential to predict long-term survival of patients with LARC [11], to discriminate different stages of rectal cancer [12], and to predict response to neoadjuvant chemoradiotherapy to identify patients eligible for less invasive treatments [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study

et al. 2022

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Background Pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer (LARC) is achieved in 15–30% of cases. Our aim was to implement and externally validate a magnetic resonance imaging (MRI)-based radiomics pipeline to predict response to treatment and to investigate the impact of manual and automatic segmentations on the radiomics models. Methods Ninety-five patients with stage II/III LARC who underwent multiparametric MRI before chemoradiotherapy and surgical treatment were enrolled from three institutions. Patients were classified as responders if tumour regression grade was 1 or 2 and nonresponders otherwise. Sixty-seven patients composed the construction dataset, while 28 the external validation. Tumour volumes were manually and automatically segmented using a U-net algorithm. Three approaches for feature selection were tested and combined with four machine learning classifiers. Results Using manual segmentation, the best result reached an accuracy of 68% on the validation set, with sensitivity 60%, specificity 77%, negative predictive value (NPV) 63%, and positive predictive value (PPV) 75%. The automatic segmentation achieved an accuracy of 75% on the validation set, with sensitivity 80%, specificity 69%, and both NPV and PPV 75%. Sensitivity and NPV on the validation set were significantly higher (p = 0.047) for the automatic versus manual segmentation. Conclusion Our study showed that radiomics models can pave the way to help clinicians in the prediction of tumour response to chemoradiotherapy of LARC and to personalise per-patient treatment. The results from the external validation dataset are promising for further research into radiomics approaches using both manual and automatic segmentations.

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Role of MRI‑based radiomics in locally advanced rectal cancer (Review)

Cited by 19 publications

References 103 publications

Benefits of Adjuvant Chemotherapy for Clinical T3-4N0 Rectal Cancer After Preoperative Chemoradiotherapy

Benefits of Adjuvant Chemotherapy for Clinical T3-4N0 Rectal Cancer After Preoperative Chemoradiotherapy

Prediction of Glioma Grade by Tumor Heterogeneity Radiomic Analysis Based on Multiparametric MRI

MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study

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