Literature DB >> 30657405

Impact of Variability in Portal Venous Phase Acquisition Timing in Tumor Density Measurement and Treatment Response Assessment: Metastatic Colorectal Cancer as a Paradigm.

Laurent Dercle1, Lin Lu1, Philip Lichtenstein1, Hao Yang1, Deling Wang1, Jianguo Zhu1, Feiyun Wu1, Hubert Piessevaux1, Lawrence H Schwartz1, Binsheng Zhao1.   

Abstract

PURPOSE: New response patterns to anticancer drugs have led tumor size-based response criteria to shift to also include density measurements. Choi criteria, for instance, categorize antiangiogenic therapy response as a decrease in tumor density > 15% at the portal venous phase (PVP). We studied the effect that PVP timing has on measurement of the density of liver metastases (LM) from colorectal cancer (CRC).
METHODS: Pretreatment PVP computed tomography images from 291 patients with LM-CRC from the CRYSTAL trial (Cetuximab Combined With Irinotecan in First-Line Therapy for Metastatic Colorectal Cancer; ClinicalTrials.gov identifier: NCT00154102) were included. Four radiologists independently scored the scans' timing according to a three-point scoring system: early, optimal, late PVP. Using this, we developed, by machine learning, a proprietary computer-aided quality-control algorithm to grade PVP timing. The reference standard was a computer-refined consensus. For each patient, we contoured target liver lesions and calculated their mean density.
RESULTS: Contrast-product administration data were not recorded in the digital imaging and communications in medicine headers for injection volume (94%), type (93%), and route (76%). The PVP timing was early, optimal, and late in 52, 194, and 45 patients, respectively. The mean (95% CI) accuracy of the radiologists for detection of optimal PVP timing was 81.7% (78.3 to 85.2) and was outperformed by the 88.6% (84.8 to 92.4) computer accuracy. The mean ± standard deviation of LM-CRC density was 68 ± 15 Hounsfield units (HU) overall and 59.5 ± 14.9 HU, 71.4 ± 14.1 HU, 62.4 ± 12.5 HU at early, optimal, and late PVP timing, respectively. LM-CRC density was thus decreased at nonoptimal PVP timing by 14.8%: 16.7% at early PVP ( P < .001) and 12.6% at late PVP ( P < .001).
CONCLUSION: Nonoptimal PVP timing should be identified because it significantly decreased tumor density by 14.8%. Our computer-aided quality-control system outperformed the accuracy, reproducibility, and speed of radiologists' visual scoring. PVP-timing scoring could improve the extraction of tumor quantitative imaging biomarkers and the monitoring of anticancer therapy efficacy at the patient and clinical trial levels.

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Year:  2017        PMID: 30657405      PMCID: PMC6874047          DOI: 10.1200/CCI.17.00108

Source DB:  PubMed          Journal:  JCO Clin Cancer Inform        ISSN: 2473-4276


  43 in total

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Journal:  Eur J Nucl Med Mol Imaging       Date:  2014-11-01       Impact factor: 9.236

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Authors:  Geoffrey R Oxnard; Binsheng Zhao; Camelia S Sima; Michelle S Ginsberg; Leonard P James; Robert A Lefkowitz; Pingzhen Guo; Mark G Kris; Lawrence H Schwartz; Gregory J Riely
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4.  Abscopal effect in a Hodgkin lymphoma patient treated by an anti-programmed death 1 antibody.

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Journal:  Eur J Cancer       Date:  2016-08-18       Impact factor: 9.162

5.  Fluorouracil, leucovorin, and irinotecan plus cetuximab treatment and RAS mutations in colorectal cancer.

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7.  K-ras mutations and benefit from cetuximab in advanced colorectal cancer.

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9.  CT evaluation of the response of gastrointestinal stromal tumors after imatinib mesylate treatment: a quantitative analysis correlated with FDG PET findings.

Authors:  Haesun Choi; Chuslip Charnsangavej; Silvana de Castro Faria; Eric P Tamm; Robert S Benjamin; Marcella M Johnson; Homer A Macapinlac; Donald A Podoloff
Journal:  AJR Am J Roentgenol       Date:  2004-12       Impact factor: 3.959

Review 10.  Liver metastases in the era of molecular targeted therapy: new faces of treatment response.

Authors:  Atul Bhanudas Shinagare; Jyothi P Jagannathan; Katherine M Krajewski; Nikhil H Ramaiya
Journal:  AJR Am J Roentgenol       Date:  2013-07       Impact factor: 3.959

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  6 in total

1.  Using a single abdominal computed tomography image to differentiate five contrast-enhancement phases: A machine-learning algorithm for radiomics-based precision medicine.

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Journal:  Eur J Radiol       Date:  2020-01-28       Impact factor: 4.531

2.  Radiomics Response Signature for Identification of Metastatic Colorectal Cancer Sensitive to Therapies Targeting EGFR Pathway.

Authors:  Laurent Dercle; Lin Lu; Lawrence H Schwartz; Min Qian; Sabine Tejpar; Peter Eggleton; Binsheng Zhao; Hubert Piessevaux
Journal:  J Natl Cancer Inst       Date:  2020-09-01       Impact factor: 13.506

3.  Automated Identification of Optimal Portal Venous Phase Timing with Convolutional Neural Networks.

Authors:  Jingchen Ma; Laurent Dercle; Philip Lichtenstein; Deling Wang; Aiping Chen; Jianguo Zhu; Hubert Piessevaux; Jun Zhao; Lawrence H Schwartz; Lin Lu; Binsheng Zhao
Journal:  Acad Radiol       Date:  2019-05-28       Impact factor: 5.482

4.  Reliability of Radiomic Features Across Multiple Abdominal CT Image Acquisition Settings: A Pilot Study Using ACR CT Phantom.

Authors:  Lin Lu; Yongguang Liang; Lawrence H Schwartz; Binsheng Zhao
Journal:  Tomography       Date:  2019-03

Review 5.  Understanding Sources of Variation to Improve the Reproducibility of Radiomics.

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Journal:  Front Oncol       Date:  2021-03-29       Impact factor: 6.244

6.  Identification of Non-Small Cell Lung Cancer Sensitive to Systemic Cancer Therapies Using Radiomics.

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Journal:  Clin Cancer Res       Date:  2020-03-20       Impact factor: 13.801

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