Radiomic features of pretreatment MRI could identify T stage in patients with rectal cancer: Preliminary findings

Sun, Yiqun; Hu, Panpan; Wang, Jiazhou; Shen, Lijun; Xia, Fan; Gan, Qing; Hu, Wenyu; Zhang, Zhen; Xin, Chao; Weijun, Peng; Tong, Tong; Gu, Yajia

doi:10.1002/jmri.26309

Cited by 21 publications

(16 citation statements)

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“…MR imaging can reduce the impact of image noise, which can affect texture analysis because of the higher contrast resolution and contrast-to-noise ratio when compared with CT [7]. However, previous studies have shown that pretreatment T2-weighted MR imaging radiomics has the potential to characterize the histopathologic features of rectal cancer and predict the response to chemoradiotherapy, except for KRAS mutation [4,9,14,34]. Therefore, in this preliminary study, we investigated the potential association between T2-weighted MR imaging features and KRAS mutation status.…”

Section: Discussionmentioning

confidence: 99%

“…To the best of our knowledge, there have been no studies on whether or not the T2-weighted MR-based radiomics signature is specifically associated with KRAS mutation status in rectal cancer. In recent years, biomarkers detected on MR imaging have shown potential for identifying tumor characteristics and improving our ability to diagnose certain tumors, including rectal cancer [14]. The aim of this study was to investigate the potential of T2-weighted MR imaging features to identify KRAS mutation status in rectal cancer.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

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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“…In multiple other cancers, radiomics has been shown to capture quantitative imaging differences and underlying tumor characteristics that have been associated with disease response to therapy 12,15 . More recently, radiomic texture features have shown some success for assessing pathologic response and have also been associated with patient survival in rectal cancers in single‐center studies 16 …”

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confidence: 99%

Radiomic Features of Primary Rectal Cancers on Baseline T₂‐Weighted MRI Are Associated With Pathologic Complete Response to Neoadjuvant Chemoradiation: A Multisite Study

Antunes

Ofshteyn

Bera

et al. 2020

Magnetic Resonance Imaging

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Background Twenty‐five percent of rectal adenocarcinoma patients achieve pathologic complete response (pCR) to neoadjuvant chemoradiation and could avoid proctectomy. However, pretreatment clinical or imaging markers are lacking in predicting response to chemoradiation. Radiomic texture features from MRI have recently been associated with therapeutic response in other cancers. Purpose To construct a radiomics texture model based on pretreatment MRI for identifying patients who will achieve pCR to neoadjuvant chemoradiation in rectal cancer, including validation across multiple scanners and sites. Study Type Retrospective. Subjects In all, 104 rectal cancer patients staged with MRI prior to long‐course chemoradiation followed by proctectomy; curated from three institutions. Field Strength/Sequence 1.5T–3.0T, axial higher resolution T2‐weighted turbo spin echo sequence. Assessment Pathologic response was graded on postsurgical specimens. In total, 764 radiomic features were extracted from single‐slice sections of rectal tumors on processed pretreatment T2‐weighted MRI. Statistical Tests Three feature selection schemes were compared for identifying radiomic texture descriptors associated with pCR via a discovery cohort (one site, N = 60, cross‐validation). The top‐selected radiomic texture features were used to train and validate a random forest classifier model for pretreatment identification of pCR (two external sites, N = 44). Model performance was evaluated via area under the curve (AUC), accuracy, sensitivity, and specificity. Results Laws kernel responses and gradient organization features were most associated with pCR (P ≤ 0.01); as well as being commonly identified across all feature selection schemes. The radiomics model yielded a discovery AUC of 0.699 ± 0.076 and a hold‐out validation AUC of 0.712 with 70.5% accuracy (70.0% sensitivity, 70.6% specificity) in identifying pCR. Radiomic texture features were resilient to variations in magnetic field strength as well as being consistent between two different expert annotations. Univariate analysis revealed no significant associations of baseline clinicopathologic or MRI findings with pCR (P = 0.07–0.96). Data Conclusion Radiomic texture features from pretreatment MRIs may enable early identification of potential pCR to neoadjuvant chemoradiation, as well as generalize across sites. Level of Evidence 3 Technical Efficacy Stage 2

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“…[8][9][10][11] As an extension of texture analysis, radiomics enable medical images to be converted into highdimensional, mineable, and quantitative features by using highthroughput extraction algorithms of these characterizations. 12,13 Previous studies on breast cancer, 14,15 colorectal cancer, 16 and lung cancer 17 have shown superior performance for characterizing or grading the tumors with the assistance of radiomics. For prostate neoplasms, additional values of radiomics features were also reported on PCa detection, aggressiveness assessment, and survival analysis in comparison with the conventional morphologic predictors.…”

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confidence: 99%

MRI‐Based Radiomics Signature for the Preoperative Prediction of Extracapsular Extension of Prostate Cancer

Xie

Wang

et al. 2019

Magnetic Resonance Imaging

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Background Radiomics approaches based on multiparametric MRI (mp‐MRI) have shown high accuracy in prostate cancer (PCa) management. However, there is a need to apply radiomics to the preoperative prediction of extracapsular extension (ECE). Purpose To develop and validate a radiomics signature to preoperatively predict the probability of ECE for patients with PCa, compared with the radiologists' interpretations. Study Type Retrospective. Population In total, 210 patients with pathology‐confirmed ECE status (101 positive, 109 negative) were enrolled. Field Strength/Sequence T2‐weighted imaging (T2WI), diffusion‐weighted imaging, and dynamic contrast‐enhanced imaging were performed on two 3.0T MR scanners. Assessment A radiomics signature was constructed to predict the probability of ECE prior to radical prostatectomy (RP). In all, 17 stable radiomics features of 1619 extracted features based on T2WI were selected. The same images were also evaluated by three radiologists. The predictive performance of the radiomics signature was validated and compared with radiologists' interpretations. Statistical Tests A radiomics signature was developed by a least absolute shrinkage and selection operator (LASSO) regression algorithm. Samples enrolled were randomly divided into two groups (143 for training and 67 for validation). Discrimination, calibration, and clinical usefulness were validated by analysis of the receiver operating characteristic (ROC) curve, calibration curve, and the decision curve, respectively. The predictive performance was then compared with visual assessments of three radiologists. Results The radiomics signature yielded an AUC of 0.902 and 0.883 in the training and validation cohort, respectively, and outperformed the visual assessment (AUC: 0.600–0.697) in the validation cohort. Pairwise comparisons demonstrated that the radiomics signature was more sensitive than the radiologists (75.00% vs. 46.88%–50.00%, all P < 0.05), but obtained comparable specificities (91.43% vs. (88.57%–94.29%); P ranged from 0.64–1.00). Data Conclusion A radiomics signature was developed and validated that outperformed the radiologists' visual assessments in predicting ECE status. Level of Evidence: 4 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:1914–1925.

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Radiomic features of pretreatment MRI could identify T stage in patients with rectal cancer: Preliminary findings

Cited by 21 publications

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

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

Radiomic Features of Primary Rectal Cancers on Baseline T₂‐Weighted MRI Are Associated With Pathologic Complete Response to Neoadjuvant Chemoradiation: A Multisite Study

MRI‐Based Radiomics Signature for the Preoperative Prediction of Extracapsular Extension of Prostate Cancer

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Radiomic features of pretreatment MRI could identify T stage in patients with rectal cancer: Preliminary findings

Cited by 21 publications

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

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

Radiomic Features of Primary Rectal Cancers on Baseline T2‐Weighted MRI Are Associated With Pathologic Complete Response to Neoadjuvant Chemoradiation: A Multisite Study

MRI‐Based Radiomics Signature for the Preoperative Prediction of Extracapsular Extension of Prostate Cancer

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Radiomic Features of Primary Rectal Cancers on Baseline T₂‐Weighted MRI Are Associated With Pathologic Complete Response to Neoadjuvant Chemoradiation: A Multisite Study