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Prediction of Tumor Cellularity in Resectable PDAC from Preoperative Computed Tomography Imaging.

ABSTRACT: PDAC remains a tumor entity with poor prognosis and a 5-year survival rate below 10%. Recent research has revealed invasive biomarkers, such as distinct molecular subtypes, predictive for therapy response and patient survival. Non-invasive prediction of individual patient outcome however remains an unresolved task. Discrete cellularity regions of PDAC resection specimen (n = 43) were analyzed by routine histopathological work up. Regional tumor cellularity and CT-derived Hounsfield Units (HU, n = 66) as well as iodine concentrations were regionally matched. One-way ANOVA and pairwise t-tests were performed to assess the relationship between different cellularity level in conventional, virtual monoenergetic 40 keV (monoE 40 keV) and iodine map reconstructions. A statistically significant negative correlation between regional tumor cellularity in histopathology and CT-derived HU from corresponding image regions was identified. Radiological differentiation was best possible in monoE 40 keV CT images. However, HU values differed significantly in conventional reconstructions as well, indicating the possibility of a broad clinical application of this finding. In this study we establish a novel method for CT-based prediction of tumor cellularity for in-vivo tumor characterization in PDAC patients.

SUBMITTER: Jungmann F

PROVIDER: S-EPMC8123300 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Prediction of Tumor Cellularity in Resectable PDAC from Preoperative Computed Tomography Imaging.

Jungmann Friederike F Kaissis Georgios A GA Ziegelmayer Sebastian S Harder Felix F Schilling Clara C Yen Hsi-Yu HY Steiger Katja K Weichert Wilko W Schirren Rebekka R Demir Ishan Ekin IE Friess Helmut H Makowski Markus R MR Braren Rickmer F RF Lohöfer Fabian K FK

Cancers 20210425 9

<h4>Background</h4>PDAC remains a tumor entity with poor prognosis and a 5-year survival rate below 10%. Recent research has revealed invasive biomarkers, such as distinct molecular subtypes, predictive for therapy response and patient survival. Non-invasive prediction of individual patient outcome however remains an unresolved task.<h4>Methods</h4>Discrete cellularity regions of PDAC resection specimen (<i>n</i> = 43) were analyzed by routine histopathological work up. Regional tumor cellularit ...[more]

PMID: 33922981

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Project description:Effective preoperative regimens and biomarkers for pancreatic ductal adenocarcinoma (PDAC) are lacking. We prospectively evaluated fluorouracil, leucovorin, irinotecan, and oxaliplatin (FOLFIRINOX)-based treatment and imaging-based biomarkers for borderline resectable PDAC. Eligible patients had treatment-naïve, histology-confirmed PDAC and one or more high-risk features: mesenteric vessel involvement, CA 19-9 level of 500 mg/dL or greater, and indeterminate metastatic lesions. Patients received modified FOLFIRINOX and chemoradiation before anticipated pancreatectomy. Tumors were classified on baseline computed tomography as high delta (well-defined interface with parenchyma) or low delta (ill-defined interface). We designated computed tomography interface response after therapy as type I (remained or became well defined) or type II (became ill defined). The study had 80% power to differentiate a 60% from 40% resection rate (α = .10). Overall survival (OS) and progression-free survival (PFS) were estimated using the Kaplan-Meier method, and subgroups were compared using log-rank tests. Thirty-three patients initiated therapy; 45% underwent pancreatectomy. The median OS was 24 months (95% CI, 16.2 to 29.6 months). For patients who did and did not undergo pancreatectomy, the median OS was 42 months (95% CI, 17.7 months to not estimable) and 14 months (95% CI, 9.0 to 24.8 months), respectively. Patients with high-delta tumors had lower 3-year PFS (4% v 40%) and 3-year OS rates (20% v 60%) than those with low-delta tumors (both P < .05). Patients with type II interface responses had lower 3-year PFS (0% v 29%) and 3-year OS rates (16% v 47%) than those with type I responses (both P < .001). Preoperative FOLFIRINOX followed by chemoradiation for high-risk borderline resectable PDAC was associated with a resection rate of 45% and median OS of approximately 2 years. Our imaging-based biomarker validation indicates that personalized treatment may be achieved using these biomarkers at baseline and post-treatment.

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Project description:Background: Preoperative and postoperative evaluation of colorectal cancer (CRC) patients is crucial for subsequent treatment guidance. Our study aims to provide a timely and rapid assessment of the prognosis of CRC patients with deep learning according to non-invasive preoperative computed tomography (CT) and explore the underlying biological explanations. Methods: A total of 808 CRC patients with preoperative CT (development cohort: n = 426, validation cohort: n = 382) were enrolled in our study. We proposed a novel end-to-end Multi-Size Convolutional Neural Network (MSCNN) to predict the risk of CRC recurrence with CT images (CT signature). The prognostic performance of CT signature was evaluated by Kaplan-Meier curve. An integrated nomogram was constructed to improve the clinical utility of CT signature by combining with other clinicopathologic factors. Further visualization and correlation analysis for CT deep features with paired gene expression profiles were performed to reveal the molecular characteristics of CRC tumors learned by MSCNN in radiographic imaging. Results: The Kaplan-Meier analysis showed that CT signature was a significant prognostic factor for CRC disease-free survival (DFS) prediction [development cohort: hazard ratio (HR): 50.7, 95% CI: 28.4-90.6, p < 0.001; validation cohort: HR: 2.04, 95% CI: 1.44-2.89, p < 0.001]. Multivariable analysis confirmed the independence prognostic value of CT signature (development cohort: HR: 30.7, 95% CI: 19.8-69.3, p < 0.001; validation cohort: HR: 1.83, 95% CI: 1.19-2.83, p = 0.006). Dimension reduction and visualization of CT deep features demonstrated a high correlation with the prognosis of CRC patients. Functional pathway analysis further indicated that CRC patients with high CT signature presented down-regulation of several immunology pathways. Correlation analysis found that CT deep features were mainly associated with activation of metabolic and proliferative pathways. Conclusions: Our deep learning based preoperative CT signature can effectively predict prognosis of CRC patients. Integration analysis of multi-omic data revealed that some molecular characteristics of CRC tumor can be captured by deep learning in CT images.

Project description:BackgroundThe high recurrence rate after radical resection of pancreatic ductal adenocarcinoma (PDAC) leads to its poor prognosis. We aimed to develop a model to preoperatively predict the risk of recurrence based on computed tomography (CT) radiomics and multiple clinical parameters.MethodsDatasets were retrospectively collected and analysed of 220 PDAC patients who underwent contrast-enhanced computed tomography (CE-CT) and received radical resection at 3 institutions in China between 2013 and 2017, with 153 from one institution as a training set, the remaining 67 as a validation set. For each patient, CT radiomics features were extracted from intratumoral and peritumoral regions to establish intratumoral, peritumoral and combined radiomics models using artificial neural network (ANN) algorithm. By incorporating clinical factors, radiomics-clinical nomograms were finally built by multivariable logistic regression analysis to predict 1- and 2-year recurrence risk.FindingsThe developed radiomics model integrating intratumoral and peritumoral radiomics features was superior to the conventionally constructed model merely using intratumoral radiomics features. Further, radiomics-clinical nomograms outperformed other models in predicting 1-year recurrence with an area under the receiver operating characteristic curve (AUROC) of 0.916 (95%CI, 0.860-0.955) in the training set and 0.764 (95%CI, 0.644-0.859) in the validation set, and 2-year recurrence with an AUROC of 0.872 (95%CI: 0.809-0.921) in the training set and 0.773 (95%CI, 0.654-0.866) in the validation set.InterpretationThis study has developed and externally validated a radiomics-clinical nomogram integrating intra- and peritumoral CT radiomics signature as well as clinical factors to predict the recurrence risk of PDAC after radical resection, which will facilitate optimized and individualized treatment strategies.FundingThis work was supported by the National Key R&D Program of China [grant number: 2018YFE0114800], the General Program of National Natural Science Foundation of China [grant number: 81772562, 2017; 81871351, 2018], the Fundamental Research Funds for the Central Universities [grant number: 2021FZZX005-08], and Zhejiang Provincial Key Projects of Technology Research [grant number: WKJ-ZJ-2033].

Dataset Information

Prediction of Tumor Cellularity in Resectable PDAC from Preoperative Computed Tomography Imaging.

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Prediction of Tumor Cellularity in Resectable PDAC from Preoperative Computed Tomography Imaging.

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