Radiogenomics for predicting p53 status, PD-L1 expression, and prognosis with machine learning in pancreatic cancer

Iwatate, Yosuke; Hoshino, Isamu; Yokota, Hajime; Ishige, Fumitaka; Itami, Makiko; Mori, Yoshihide; Chiba, Shuichi; Arimitsu, Hidehito; Yanagibashi, Hiroo; Nagase, Hiroki; Takayama, Wataru

doi:10.1038/s41416-020-0997-1

Cited by 67 publications

(65 citation statements)

References 41 publications

(58 reference statements)

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“…Tumor proportion scores (TPS), combined positive score (CPS) and H-score were all used as evaluation criteria in previous research. Cases with PD-L1-stained cells ranging from 1% to 10% of the total tumor cells were considered PD-L1positive in pancreatic cancer, 36 and Yoon et al applied CT radiomics for predicting PD-L1 expression and defined PD-L1 positive as ≥50% (TPS) with any intensity in NSCLC. 22 Some research ignored the intensity of staining to a certain extent, which influenced on the PD-L1 predictive accuracy and response to immunotherapy.…”

Section: Discussionmentioning

confidence: 99%

<p>Pretreatment CT-Based Radiomics Signature as a Potential Imaging Biomarker for Predicting the Expression of PD-L1 and CD8+TILs in ESCC</p>

Wen¹,

Yang²,

Zhu³

et al. 2020

OTT

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Background: The present study constructed and validated models to predict PD-L1 and CD8+TILs expression levels in esophageal squamous cell carcinoma (ESCC) patients using radiomics features and clinical factors. Patients and Methods: This retrospective study randomly assigned 220 ESCC patients to a discovery dataset (n= 160) and validation dataset (n= 60). A total of 462 radiomics features were extracted from the segmentation of regions of interest (ROIs) based on pretreatment CT images of each patient. The LASSO algorithm was applied to reduce the dimensionality of the data and select features. A multivariable logistic regression analysis was adopted to build radiomics signatures. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive accuracy of these models. Results: There was no significant difference between the training and validation datasets for any clinical factors in patients with ESCC. The PD-L1 expression level correlated with the differentiation degree (p= 0.011) and tumor stage (p= 0.032). Smoking status (p= 0.043) and differentiation degree (p= 0.025) were associated with CD8+TILs expression levels. The radiomics signatures achieved good performance in predicting PD-L1 and CD8+TILs with AUCs= 0.784 and 0.764, respectively. The combined model showed a favorable predictive ability compared to radiomics signatures or clinical factors alone and improved the AUCs from 0.669 to 0.871 for PD-L1 and from 0.672 to 0.832 for CD8+TILs. These results were verified in the validation dataset with the AUCs of 0.817 and 0.795, respectively. Conclusion: CT-based radiomics features have a potential value for classifying patients according to PD-L1 and CD8+TILs expression levels. The combination of clinical factors and radiomics signatures significantly improved the predictive performance in ESCC.

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Section: Discussionmentioning

confidence: 99%

<p>Pretreatment CT-Based Radiomics Signature as a Potential Imaging Biomarker for Predicting the Expression of PD-L1 and CD8+TILs in ESCC</p>

Wen¹,

Yang²,

Zhu³

et al. 2020

OTT

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“…Si Shi et al (28) found a correlation of PET-imaging features with TP53 status in terms of metabolic tumour burden. Yosuke et al had reported a model for predicting TP53 mutations in pancreatic cancer from CT images using machine learning (29), and its AUC value was 0.795. But CT had no high contrast resolution that can reflect indistinguishable lesions in PDAC.…”

Section: Discussionmentioning

confidence: 99%

Differentiating TP53 Mutation Status in Pancreatic Ductal Adenocarcinoma Using Multiparametric MRI-Derived Radiomics

Gao

Chen

et al. 2021

Front. Oncol.

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ObjectivesThis study assessed the preoperative prediction of TP53 status based on multiparametric magnetic resonance imaging (mpMRI) radiomics extracted from two-dimensional (2D) and 3D images.Methods57 patients with pancreatic cancer who underwent preoperative MRI were included. The diagnosis and TP53 gene test were based on resections. Of the 57 patients included 37 mutated TP53 genes and the remaining 20 had wild-type TP53 genes. Two radiologists performed manual tumour segmentation on seven different MRI image acquisition sequences per patient, including multi-phase [pre-contrast, late arterial phase (ap), portal venous phase, and delayed phase] dynamic contrast enhanced (DCE) T1-weighted imaging, T2-weighted imaging (T2WI), Diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC). PyRadiomics-package was used to generate 558 two-dimensional (2D) and 994 three-dimensional (3D) image features. Models were constructed by support vector machine (SVM) for differentiating TP53 status and DX score method were used for feature selection. The evaluation of the model performance included area under the curve (AUC), accuracy, calibration curves, and decision curve analysis.ResultsThe 3D ADC-ap-DWI-T2WI model with 11 selected features yielded the best performance for differentiating TP53 status, with accuracy = 0.91 and AUC = 0.96. The model showed the good calibration. The decision curve analysis indicated that the radiomics model had clinical utility.ConclusionsA non-invasive and quantitative mpMRI-based radiomics model can accurately predict TP53 mutation status in pancreatic cancer patients and contribute to the precision treatment.

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“…Recently, radiogenomics is and emerging field that integrates "radiomics" and "genomics", which may aid in the development of precision medicine. Iwatate et al (27) found that radiogenomics could predict p53 mutations and in turn the prognosis of PDAC patients. Pancreatic cancer remains one of the most lethal malignancies, radiomics may have the potential to address some problems but further validation in larger-scale, multicenter studies and in randomized control trials is required.…”

Section: Discussionmentioning

confidence: 99%

A Novel Validated Recurrence Stratification System Based on 18F-FDG PET/CT Radiomics to Guide Surveillance After Resection of Pancreatic Cancer

Wei

Song

et al. 2021

Front. Oncol.

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objectiveDespite the heterogeneous biology of pancreatic cancer, similar surveillance schemas have been used. Identifying the high recurrence risk population and conducting prompt intervention may improve prognosis and prolong overall survival.MethodsOne hundred fifty-six resectable pancreatic cancer patients who had undergone 18F-FDG PET/CT from January 2013 to December 2018 were retrospectively reviewed. The patients were categorized into a training cohort (n = 109) and a validation cohort (n = 47). LIFEx software was used to extract radiomic features from PET/CT. The risk stratification system was based on predictive factors for recurrence, and the index of prediction accuracy was used to reflect both the discrimination and calibration.ResultsOverall, seven risk factors comprising the rad-score and clinical variables that were significantly correlated with relapse were incorporated into the final risk stratification system. The 1-year recurrence-free survival differed significantly among the low-, intermediate-, and high-risk groups (85.5, 24.0, and 9.1%, respectively; p < 0.0001). The C-index of the risk stratification system in the development cohort was 0.890 (95% CI, 0.835–0.945).ConclusionThe 18F-FDG PET/CT-based radiomic features and clinicopathological factors demonstrated good performance in predicting recurrence after pancreatectomy in pancreatic cancer patients, providing a strong recommendation for an adequate adjuvant therapy course in all patients. The high-risk recurrence population should proceed with closer follow-up in a clinical setting.

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Radiogenomics for predicting p53 status, PD-L1 expression, and prognosis with machine learning in pancreatic cancer

Cited by 67 publications

References 41 publications

<p>Pretreatment CT-Based Radiomics Signature as a Potential Imaging Biomarker for Predicting the Expression of PD-L1 and CD8+TILs in ESCC</p>

<p>Pretreatment CT-Based Radiomics Signature as a Potential Imaging Biomarker for Predicting the Expression of PD-L1 and CD8+TILs in ESCC</p>

Differentiating TP53 Mutation Status in Pancreatic Ductal Adenocarcinoma Using Multiparametric MRI-Derived Radiomics

A Novel Validated Recurrence Stratification System Based on 18F-FDG PET/CT Radiomics to Guide Surveillance After Resection of Pancreatic Cancer

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