Literature DB >> 33890153

Preoperative prediction of postsurgical outcomes in mass-forming intrahepatic cholangiocarcinoma based on clinical, radiologic, and radiomics features.

Hyo Jung Park1, Bumwoo Park2, Seo Young Park3, Sang Hyun Choi1, Hyungjin Rhee4, Ji Hoon Park5, Eun-Suk Cho6, Suk-Keu Yeom7, Sumi Park8, Mi-Suk Park9, Seung Soo Lee10.   

Abstract

OBJECTIVES: Current prognostic systems for intrahepatic cholangiocarcinoma (IHCC) rely on surgical pathology data and are not applicable to a preoperative setting. We aimed to develop and validate preoperative models to predict postsurgical outcomes in mass-forming IHCC patients based on clinical, radiologic, and radiomics features.
METHODS: This multicenter retrospective cohort study included patients who underwent curative-intent resection for mass-forming IHCC. In the development cohort (single institution data), three preoperative multivariable Cox models for predicting recurrence-free survival (RFS) were constructed, including the clinical-radiologic, radiomics, and clinical-radiologic-radiomics (CRR) models based on clinical and CT findings, CT-radiomics features, and a combination of both, respectively. Model performance was evaluated in the test cohort (data from five institutions) using Harrell's C-index and compared with postoperative prognostic systems.
RESULTS: A total of 345 patients (233, development cohort; 112, test cohort) were evaluated. The clinical-radiologic model included five independent CT predictors (infiltrative contour, multiplicity, periductal infiltration, extrahepatic organ invasion, and suspicious metastatic lymph node) and showed similar performance in predicting RFS to the radiomics model (C-index, 0.65 vs. 0.68; p = 0.43 in the test cohort). The CRR model showed significantly improved performance (C-index, 0.71; p = 0.01) than the clinical-radiologic model and demonstrated similar performance to the postoperative prognostic systems in predicting RFS (C-index, 0.71-0.73 vs. 0.70-0.73; p ≥ 0.40) and overall survival (C-index, 0.68-0.71 vs. 0.64-0.74; p ≥ 0.27) in the test cohort.
CONCLUSIONS: A model integrating clinical, CT, and radiomics information may be useful for the preoperative assessment of postsurgical outcomes in patients with mass-forming IHCC. KEY POINTS: • The radiomics analysis had incremental value in predicting recurrence-free survival of patients with intrahepatic mass-forming cholangiocarcinoma. • The clinical-radiologic-radiomics model demonstrated similar performance to the postoperatively available prognostic systems (including 8th AJCC system) in predicting recurrence-free survival and overall survival. • The clinical-radiologic-radiomics model may be useful for the preoperative assessment of postsurgical outcomes in patients with mass-forming intrahepatic cholangiocarcinoma.

Entities:  

Keywords:  Cholangiocarcinoma; Image processing, computer-assisted; Multidetector computed tomography; Precision medicine; Prognosis

Year:  2021        PMID: 33890153     DOI: 10.1007/s00330-021-07926-6

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  1 in total

Review 1.  Cholangiocarcinoma: epidemiology, mechanisms of carcinogenesis and prevention.

Authors:  D M Parkin; H Ohshima; P Srivatanakul; V Vatanasapt
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  1993 Nov-Dec       Impact factor: 4.254

  1 in total
  7 in total

1.  Integrative Analysis of Intrahepatic Cholangiocarcinoma Subtypes for Improved Patient Stratification: Clinical, Pathological, and Radiological Considerations.

Authors:  Tiemo S Gerber; Lukas Müller; Fabian Bartsch; Lisa-Katharina Gröger; Mario Schindeldecker; Dirk A Ridder; Benjamin Goeppert; Markus Möhler; Christoph Dueber; Hauke Lang; Wilfried Roth; Roman Kloeckner; Beate K Straub
Journal:  Cancers (Basel)       Date:  2022-06-28       Impact factor: 6.575

2.  CT-Based Radiomics Analysis for Noninvasive Prediction of Perineural Invasion of Perihilar Cholangiocarcinoma.

Authors:  Peng-Chao Zhan; Pei-Jie Lyu; Zhen Li; Xing Liu; Hui-Xia Wang; Na-Na Liu; Yuyuan Zhang; Wenpeng Huang; Yan Chen; Jian-Bo Gao
Journal:  Front Oncol       Date:  2022-06-20       Impact factor: 5.738

Review 3.  Radiomics in medical imaging: pitfalls and challenges in clinical management.

Authors:  Roberta Fusco; Vincenza Granata; Giulia Grazzini; Silvia Pradella; Alessandra Borgheresi; Alessandra Bruno; Pierpaolo Palumbo; Federico Bruno; Roberta Grassi; Andrea Giovagnoni; Roberto Grassi; Vittorio Miele; Antonio Barile
Journal:  Jpn J Radiol       Date:  2022-03-28       Impact factor: 2.701

Review 4.  Radiomics of Biliary Tumors: A Systematic Review of Current Evidence.

Authors:  Francesco Fiz; Visala S Jayakody Arachchige; Matteo Gionso; Ilaria Pecorella; Apoorva Selvam; Dakota Russell Wheeler; Martina Sollini; Luca Viganò
Journal:  Diagnostics (Basel)       Date:  2022-03-28

5.  An update on radiomics techniques in primary liver cancers.

Authors:  Vincenza Granata; Roberta Fusco; Sergio Venazio Setola; Igino Simonetti; Diletta Cozzi; Giulia Grazzini; Francesca Grassi; Andrea Belli; Vittorio Miele; Francesco Izzo; Antonella Petrillo
Journal:  Infect Agent Cancer       Date:  2022-03-04       Impact factor: 2.965

6.  A Novel Clinical-Radiomics Model Based on Sarcopenia and Radiomics for Predicting the Prognosis of Intrahepatic Cholangiocarcinoma After Radical Hepatectomy.

Authors:  Liming Deng; Bo Chen; Chenyi Zhan; Haitao Yu; Jiuyi Zheng; Wenming Bao; Tuo Deng; Chongming Zheng; Lijun Wu; Yunjun Yang; Zhengping Yu; Yi Wang; Gang Chen
Journal:  Front Oncol       Date:  2021-11-19       Impact factor: 6.244

7.  Conventional, functional and radiomics assessment for intrahepatic cholangiocarcinoma.

Authors:  Vincenza Granata; Roberta Fusco; Andrea Belli; Valentina Borzillo; Pierpaolo Palumbo; Federico Bruno; Roberta Grassi; Alessandro Ottaiano; Guglielmo Nasti; Vincenzo Pilone; Antonella Petrillo; Francesco Izzo
Journal:  Infect Agent Cancer       Date:  2022-03-28       Impact factor: 2.965

  7 in total

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