Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer

Meng, Xiaochun; Xia, Wei; Xie, Peiyi; Zhang, Rui; Li, Wenru; Wang, Mengmeng; Xiong, Fei; Liu, Yangchuan; Fan, Xinjuan; Xie, Yao; Wan, Xiang-Bo; Zhu, Kangshun; Shan, Hong; Wang, Lei; Gao, Xin

doi:10.1007/s00330-018-5763-x

Cited by 104 publications

(71 citation statements)

References 37 publications

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“…Kawada et al [13] found that 18 F-fluorodeoxyglucose PET/ CT was useful for prediction of KRAS status in metastatic colorectal cancer with a sensitivity, specificity, and accuracy of 68%, 74%, and 71.4%, respectively. Meng et al [30] showed that the radio-mics signatures based on multiparametric MR imaging predicted the KRAS gene mutation with an area under the curve (AUC) of 0.651. Cui et al [31] found that diffusion kurtosis imaging-derived histogram metrics from whole tumor volumes were associated with KRAS mutations.…”

Section: Discussionmentioning

confidence: 99%

Magnetic Resonance-Based Texture Analysis Differentiating KRAS Mutation Status in Rectal Cancer

Kim

Lee

et al. 2020

Cancer Res Treat

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Original ArticlePurpose Mutation of the Kirsten Ras (KRAS) oncogene is present in 30%-40% of colorectal cancers and has prognostic significance in rectal cancer. In this study, we examined the ability of radiomics features extracted from T2-weighted magnetic resonance (MR) images to differentiate between tumors with mutant KRAS and wild-type KRAS. Materials and MethodsSixty patients with primary rectal cancer (25 with mutant KRAS, 35 with wild-type KRAS) were retrospectively enrolled. Texture analysis was performed in all regions of interest on MR images, which were manually segmented by two independent radiologists. We identified potentially useful imaging features using the two-tailed t test and used them to build a discriminant model with a decision tree to estimate whether KRAS mutation had occurred. ResultsThree radiomic features were significantly associated with KRAS mutational status (p < 0.05). The mean (and standard deviation) skewness with gradient filter value was significantly higher in the mutant KRAS group than in the wild-type group (2.04±0.94 vs. 1.59±0.69). Higher standard deviations for medium texture (SSF3 and SSF4) were able to differentiate mutant KRAS (139.81±44.19 and 267.12±89.75, respectively) and wild-type KRAS (114.55±29.30 and 224.78±62.20). The final decision tree comprised three decision nodes and four terminal nodes, two of which designated KRAS mutation. The sensitivity, specificity, and accuracy of the decision tree was 84%, 80%, and 81.7%, respectively. ConclusionUsing MR-based texture analysis, we identified three imaging features that could differentiate mutant from wild-type KRAS. T2-weighted images could be used to predict KRAS mutation status preoperatively in patients with rectal cancer.

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Section: Discussionmentioning

confidence: 99%

Magnetic Resonance-Based Texture Analysis Differentiating KRAS Mutation Status in Rectal Cancer

Kim

Lee

et al. 2020

Cancer Res Treat

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“…Subjectivity and sampling error are proved to be potential problems in accurately evaluating MVI (5). A noninvasive imaging method which could accurately diagnosing MVI preoperatively would be help to better stratify HCC patients for clinical management (38). Extensive studies have shown that radiomics have great potential in predicting tumor biology and in improving implementation of precision medicine (18,23,28,29).…”

Section: Discussionmentioning

confidence: 99%

“…The common approach is to build a multi-feature parameter for radiomic analysis (44). Several studies have indicated that adding of mRMR can improve the performance of radiomic models (38,45,46). In our present study, the mRMR feature-ranking algorithms were added before the generation of radiomic signatures.…”

Section: Discussionmentioning

confidence: 99%

Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Initial Application of a Radiomic Algorithm Based on Grayscale Ultrasound Images

Liu

Xia

et al. 2020

Front. Oncol.

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Objectives: To establish a radiomic algorithm based on grayscale ultrasound images and to make preoperative predictions of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients. Methods: In this retrospective study, 322 cases of histopathologically confirmed HCC lesions were included. The classifications based on preoperative grayscale ultrasound images were performed in two stages: (1) classifier #1, MVI-negative and MVI-positive cases; (2) classifier #2, MVI-positive cases were further classified as M1 or M2 cases. The gross-tumoral region (GTR) and peri-tumoral region (PTR) signatures were combined to generate gross-and peri-tumoral region (GPTR) radiomic signatures. The optimal radiomic signatures were further incorporated with vital clinical information. Multivariable logistic regression was used to build radiomic models. Results: Finally, 1,595 radiomic features were extracted from each HCC lesion. At the classifier #1 stage, the radiomic signatures based on features of GTR, PTR, and GPTR showed area under the curve (AUC) values of 0.708 (95% CI, 0.603-0.812), 0.710 (95% CI, 0.609-0.811), and 0.726 (95% CI, 0.625-0.827), respectively. Upon incorporation of vital clinical information, the AUC of the GPTR radiomic algorithm was 0.744 (95% CI, 0.646-0.841). At the classifier #2 stage, the AUC of the GTR radiomic signature was 0.806 (95% CI, 0.667-0.944). Conclusions: Our radiomic algorithm based on grayscale ultrasound images has potential value to facilitate preoperative prediction of MVI in HCC patients. The GTR radiomic signature may be helpful for further discriminating between M1 and M2 levels among MVI-positive patients.

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“…Radiomics features are thought to represent information about tumor genotypes and phenotypes. For example, radiomics signatures have been successfully used to predict histological grade (27)(28)(29) and KRAS mutation status (29,30) in colorectal cancer. Unlike biopsy specimens, radiomics features are derived from the whole tumor.…”

Section: Discussionmentioning

confidence: 99%

Combining Radiomics and Blood Test Biomarkers to Predict the Response of Locally Advanced Rectal Cancer to Chemoradiation

Jeon

Song

Chie

et al. 2020

In Vivo

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Background/Aim: A noninvasive method for predicting a patient's response to neoadjuvant chemoradiotherapy (nCRT) for locally advanced rectal cancer would be useful because this would help determine the subsequent treatment strategy. Two types of noninvasive biomarkers have previously been studied, based on radiomics and based on blood test parameters. We hypothesized that a combination of both types would provide a better predictive power, and this has not previously been investigated. Patients and Methods: Data from 135 patients with locally advanced rectal cancer who underwent nCRT were retrospectively allocated into training and validation cohorts in a 2:1 ratio. Sixty-five radiomics features were extracted from tumors segmented on T2-weighted magnetic resonance images. An elastic net was applied to generate four models for discerning the patients with good responses to nCRT based on radiomics features (model R), blood biomarkers (model B), both (model RB), and a linear combination of models R and B (model R+B). Results: Among 65 radiomics features, 17 were selected as robust features for model development. The AUC values of model R, model B, model RB, and model R+B achieved 0.751, 0.627, 0.785, and 0.711 in the training cohort (n=90), and 0.705, 0.603, 0.679, and 0.705 in validation cohort (n=45), respectively. In the entire cohort, models RB and R+B demonstrated a significantly better performance than model B but not R. There was no correlation between the scores of models R and B (p=0.76). Radiomics features had a greater influence than blood biomarkers on models RB and R+B. Conclusion: A non-redundancy between radiomics features and blood-based biomarkers was observed. Furthermore, radiomics features are more valuable in terms of predicting response to nCRT. The importance of combining noninvasive biomarkers in future investigations is highlighted. Neoadjuvant chemoradiotherapy (nCRT) is the standard treatment for locally advanced rectal cancer (LARC). The patient's response to nCRT is associated with disease outcome (1) and is often used to determine the subsequent 2955 This article is freely accessible online.

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Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer

Cited by 104 publications

References 37 publications

Magnetic Resonance-Based Texture Analysis Differentiating KRAS Mutation Status in Rectal Cancer

Magnetic Resonance-Based Texture Analysis Differentiating KRAS Mutation Status in Rectal Cancer

Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Initial Application of a Radiomic Algorithm Based on Grayscale Ultrasound Images

Combining Radiomics and Blood Test Biomarkers to Predict the Response of Locally Advanced Rectal Cancer to Chemoradiation

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