https://openworks.mdanderson.org/sumexp21/1008/thumbnail.jp

De, Brian, MD

Fuentes, David, PhD

Javle, Milind, MD

Koay, Eugene J., MD, PhD

Van Wieren, Arie, BS

English

OpenWorks @ MD Anderson

BackgroundIntrahepatic cholangiocarcinoma (iCCA) is an uncommon, aggressive bile duct cancer. Two-thirds of iCCA tumors are surgically unresectable at diagnosis and prognosis for these patients is poor, with median survival time of 2.5-7.5 months in absence of treatment.1 More than half of unresectable iCCA patients die from tumor-related liver failure (TRLF), a result of the tumor invading and disrupting the nearby parenchyma, vasculature, or bile ducts.2Ablative radiotherapy (ART) has been shown to effectively mitigate TRLF and improve survival, but fewer than 1 in 6 iCCA patients likely to develop TRLF receive this treatment.4 This stems from the difficulty of prospectively identifying this subgroup of patients. To enable automated identification of iCCA patients likely to develop TRLF, we are developing a model applying the predictive ability of deep convolutional neural networks (CNNs) to an institutional database and pretreatment computed tomography (CT) scans for patients with unresectable iCCA. We predict that intratumoral and peritumoral imaging features of iCCA along with clinicopathologic features can be used by a CNN to accurately predict TRLF.ConclusionsOur preliminary data indicate a range of outcomes for patients and variables that may be useful to develop a CNN to predict TRLF. Looking forward, we will expand on our efforts to annotate patient data as well as manually segment CT scans of these patients for training of the CNN models. References1) Park et al.  Gut LIver. 2009;3(4):298-3052) Koay et al.  Hepatobiliary Surg Nutr. 2017;6(2):105-1163) Banales et al.  Nat Rev Gastroenterol Hepatol. 2020;17(9):557-5884) Tao et al.  J Clin Oncol. 2016;34(3):219-226Predicting Tumor-Related Liver Failure in Unresectable Intrahepatic Cholangiocarcinoma Patients through the Development of an Imaging-Based Deep Learning Neural Network ModelArie Van Wieren BS, Brian De MD1, David Fuentes PhD2, Milind Javle MD3, and Eugene J. Koay MD PhD11Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA2Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA3Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USAResultsCurrently, we have analyzed 175 patients who underwent radiotherapy for unresectable iCCA at MD Anderson from 2002-2021. TRLF was found to be the cause of death in 13 of 83 patients with a known cause of death.MethodsWe will construct a large institutional database that includes manually segmented CT scans, molecular data, and treatment data to train, validate, and test a CNN that can accurately predict whether a patient will develop TRLF. To construct an institutional database for training of the CNN, we will review records of 700+ patients with iCCA.Figure 3. Workflow displaying how segmented liver protocol CT images and clinical, pathologic, and molecular data will be utilized by a CNN to provide risk of TRLF at 12 months.Figure 2. Receiver operating characteristic curve of logistic prediction model for TRLF using pretreatment clinical characteristics. Clinical characteristics alone are inadequate to predict TRLF.Figure 1. iCCA is a malignancy that arises in the periphery of the second-order bile ducts.3Median RangeAge (years) 66 25-89Tumor Size (cm) 7.3 1.7-18.4Radiotherapy dose (Gy) 67.5 33-100Fractions 15 33-100BED10 (Gy) 98 44-144Overall Survival (months from diagnosis) 23.9 1.9-163Overall Survival (months from radiotherapy) 13.7 0.6-153.5Table 1. Median age, tumor size, radiotherapy treatment conditions, and overall survival.American Joint Committee on Cancer 8th Edition StageStage I 12%Stage II 22%Stage III 37%Stage IV 29%GenePercentage of Patients with MutationIDH1 24%TP53 21%ARID1A 21%FGFR2 13%BAP1 12%IDH2 12%PIK3CA 11%Table 2. Percentage of patients by American Joint Committee on Cancer 8th Edition Stage at start of radiotherapy. Table 3. Overall Survival at one year following radiotherapy.Tumor location in liverPercentage of PatientsCentrally 71%Peripherally 29%Table 5. Most common mutations in iCCA patients with molecular testing.Table 4. Percentage of patients by tumor location.Overall Survival [95% CI] at 1 year following radiotherapyAll patients (n=175)73% [65-81%]

Predicting Tumor Related Liver Failure in Unresectable Intrahepatic Cholangiocarcinoma Patients through the Development of an Imaging Based Deep Learning Neural Network Model

https://openworks.mdanderson.org/cgi/viewcontent.cgi?article=1008&context=sumexp21

Predicting Tumor Related Liver Failure in Unresectable Intrahepatic Cholangiocarcinoma Patients through the Development of an Imaging Based Deep Learning Neural Network Model

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