Developed and validated a prognostic nomogram for recurrence-free survival after complete surgical resection of local primary gastrointestinal stromal tumors based on deep learning

Chen, Tao; Meng, Guozhu; Li, Yong; Feng, Xingyu; Xiong, Wei; Zhao, Xixi; Yang, Yali; Zhang, Cangui; Hu, Yanfeng; Chen, Hao; Lin, Tian; Zhao, Mingli; Líu, Hao; Yu, Jiang; Xu, Yu; Li, Guoxin

doi:10.1016/j.ebiom.2018.12.028

Cited by 43 publications

(35 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, in the study, data augmentation was used to increase the size of the training dataset and prevent overfitting. Besides, the ResNet with simple architecture and short time consumption was enough to learn the predictive features according to the satisfactory results in our study and the other researchers [26,27].…”

Section: Discussionsupporting

confidence: 64%

“…A ResNet is an effective exploration of a deep CNN, allowing the effective training of substantially deeper networks than those used previously while maintaining fast convergence times [14].With the presentation of residual blocks, many traditional problems associated with a CNN, such as gradient vanishing and accuracy degradation, are significantly improved [14]. A ResNet has been used increasingly due to its utility and simplicity in clinical applications [15,16].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Noninvasive KRAS mutation estimation in colorectal cancer using a deep learning method based on routine CT imaging

Liu

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: The detection of KRAS gene mutations in colorectal cancer (CRC) is key to the optimal design of individualized therapeutic strategies. The noninvasive prediction of the KRAS status in CRC is challenging. Deep learning (DL) in medical imaging has shown its high performance in diagnosis, classification, and prediction in recent years. In this paper, we investigated predictive performance by using a DL method with a residual neural network ( ResNet ) to estimate the KRAS mutation status in CRC patients based on routine pre-treatment contrast-enhanced CT imaging. Methods: We have collected a dataset consisting of 157 patients with pathology-confirmed CRC who were randomly divided into a training cohort (n = 117) and a validation cohort (n = 40). We developed an ResNet model that used portal venous phase CT images to estimate KRAS mutations in the axial, coronal, and sagittal directions of the training cohort and validated the model in the validation cohort. Several groups of expended ROI patches were generated for the ResNet model, to explore whether tissues around the tumor can contribute to cancer assessment. We also explored a radiomics model with the random forest classifier (RFC) to predict KRAS mutations and compared it with the DL model. Results: The ResNet model in the axial direction achieved the higher area under the curve (AUC) value (0.90) in the validation cohort and peaked at 0.93 with an input of “ROI and 20-pixel” surrounding area. In the training cohort, the AUC was 0.945 (sensitivity: 0.75; specificity: 0.94), and in the validation cohort, the AUC was0.818 (sensitivity: 0.70; specificity: 0.85). In comparison, the ResNet model showed better predictive ability . Conclusions: Our experiments reveal that the computerized assessment of the pre-treatment CT images of CRC patients using a DL model has the potential to precisely predict KRAS mutations. This new model has the potential to assist in noninvasive KRAS mutation estimation. Keywords: Colorectal Neoplasm, Mutation, Deep Learning

show abstract

Section: Discussionsupporting

confidence: 64%

Section: Introductionmentioning

confidence: 99%

Noninvasive KRAS mutation estimation in colorectal cancer using a deep learning method based on routine CT imaging

Liu

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Radiomics can extract hundreds of quantitative features from medical images and is promising in prediction the biological behavior on the onset of tumor. In a number of previous studies, radiomics has been implicated in the predictions of the biological behaviors in GISTs [19–21]. Two studies used radiomic features extracted from CE‐CT to build prediction models for predicting malignant potential with promising accuracy [19,20].…”

Section: Discussionmentioning

confidence: 99%

“…Recently Radiomics prediction model has gained attention in the diagnosis of cancers [17,18]. Previous studies have shown high accuracy of radiomics in the assessment of biological behavior of GISTs comprehensively, including malignant potential [19,20], mitotic rate [19], recurrence [21]. However, to the best of our knowledge, this is the first ever study that investigates whether radiomics can be used as a tool to assess Ki‐67 expression status in GISTs.…”

Section: Introductionmentioning

confidence: 99%

Personalized CT‐based radiomics nomogram preoperative predicting Ki‐67 expression in gastrointestinal stromal tumors: a multicenter development and validation cohort

Zhang

Gao

Zhang

et al. 2020

Clinical & Translational Med

View full text Add to dashboard Cite

Background and AimTo develop and validate radiomic prediction models using contrast‐enhanced computed tomography (CE‐CT) to preoperatively predict Ki‐67 expression in gastrointestinal stromal tumors (GISTs). MethodA total of 339 GIST patients from four centers were categorized into the training, internal validation, and external validation cohort. By filtering unstable features, minimum redundancy, maximum relevance, Least Absolute Shrinkage and Selection Operator (LASSO) algorithm, a radiomic signature was built to predict the malignant potential of GISTs. Individual nomograms of Ki‐67 expression incorporating the radiomic signature or clinical factors were developed using the multivariate logistic model and evaluated regarding its calibration, discrimination, and clinical usefulness. ResultsThe radiomic signature, consisting of 6 radiomic features had AUC of 0.787 [95% confidence interval (CI) 0.632–0.801], 0.765 (95% CI 0.683–0.847), and 0.754 (95% CI 0.666–0.842) in the prediction of high Ki‐67 expression in the training, internal validation and external validation cohort, respectively. The radiomic nomogram including the radiomic signature and tumor size demonstrated significant calibration, and discrimination with AUC of 0.801 (95% CI 0.726–0.876), 0.828 (95% CI 0.681–0.974), and 0.784 (95% CI 0.701–0.868) in the training, internal validation and external validation cohort respectively. Based on the Decision curve analysis, the radiomics nomogram was found to be clinically significant and useful. ConclusionsThe radiomic signature from CE‐CT was significantly associated with Ki‐67 expression in GISTs. A nomogram consisted of radiomic signature, and tumor size had maximum accuracy in the prediction of Ki‐67 expression in GISTs. Results from our study provide vital insight to make important preoperative clinical decisions.

show abstract

Section: Introductionmentioning

confidence: 99%

Noninvasive KRAS mutation estimation in colorectal cancer using a deep learning method based on CT imaging

Liu

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: The detection of Kirsten rat sarcoma viral oncogene homolog ( KRAS )gene mutations in colorectal cancer (CRC) is key to the optimal design of individualized therapeutic strategies. The noninvasive prediction of the KRAS status in CRC is challenging. Deep learning (DL) in medical imaging has shown its high performance in diagnosis, classification, and prediction in recent years. In this paper, we investigated predictive performance by using a DL method with a residual neural network ( ResNet ) to estimate the KRAS mutation status in CRC patients based on pre-treatment contrast-enhanced CT imaging. Methods: We have collected a dataset consisting of 157 patients with pathology-confirmed CRC who were divided into a training cohort (n = 117) and a testing cohort (n = 40). We developed an ResNet model that used portal venous phase CT images to estimate KRAS mutations in the axial, coronal, and sagittal directions of the training cohort and evaluated the model in the testing cohort. Several groups of expended region of interest （ROI）patches were generated for the ResNet model，to explore whether tissues around the tumor can contribute to cancer assessment. We also explored a radiomics model with the random forest classifier (RFC) to predict KRAS mutations and compared it with the DL model. Results: The ResNet model in the axial direction achieved the higher area under the curve (AUC) value (0.90) in the testing cohort and peaked at 0.93 with an input of “ROI and 20-pixel” surrounding area. AUC of radiomics model in testing cohorts were 0.818. In comparison, the ResNet model showed better predictive ability . Conclusions: Our experiments reveal that the computerized assessment of the pre-treatment CT images of CRC patients using a DL model has the potential to precisely predict KRAS mutations. This new model has the potential to assist in noninvasive KRAS mutation estimation. Keywords: Colorectal Neoplasm, Mutation, Deep Learning

show abstract

Developed and validated a prognostic nomogram for recurrence-free survival after complete surgical resection of local primary gastrointestinal stromal tumors based on deep learning

Cited by 43 publications

References 40 publications

Noninvasive KRAS mutation estimation in colorectal cancer using a deep learning method based on routine CT imaging

Noninvasive KRAS mutation estimation in colorectal cancer using a deep learning method based on routine CT imaging

Personalized CT‐based radiomics nomogram preoperative predicting Ki‐67 expression in gastrointestinal stromal tumors: a multicenter development and validation cohort

Noninvasive KRAS mutation estimation in colorectal cancer using a deep learning method based on CT imaging

Contact Info

Product

Resources

About