Skip to main content

11.10.2019 | Hepatobiliary Tumors

Prediction of Histopathologic Growth Patterns of Colorectal Liver Metastases with a Noninvasive Imaging Method

Annals of Surgical Oncology
MD Jin Cheng, PhD Jingwei Wei, MD Tong Tong, MD Weiqi Sheng, MD Yinli Zhang, PhD Yuqi Han, PhD Dongsheng Gu, MD Nan Hong, MD Yingjiang Ye, PhD Jie Tian, MD Yi Wang
Wichtige Hinweise

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1245/​s10434-019-07910-x) contains supplementary material, which is available to authorized users.
Jingwei Wei, Tong Tong and Weiqi Sheng have the equal contribution as the first authors.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.



To predict histopathologic growth patterns (HGPs) in colorectal liver metastases (CRLMs) with a noninvasive radiomics model.


Patients with chemotherapy-naive CRLMs who underwent abdominal contrast-enhanced multidetector CT (MDCT) followed by partial hepatectomy between January 2007 and January 2019 from two institutions were included in this retrospective study. Hematoxylin- and eosin-stained histopathologic sections of CRLMs were reviewed, with HGPs defined according to international consensus. Lesions were divided into training and validation datasets based on patients’ sources. Radiomic features were extracted from pre- and post-contrast (arterial and portal venous) phase MDCT images, with review focusing on the segmented tumor–liver interface zones of CRLMs. Minimum redundancy maximum relevance and decision tree methods were used for radiomics modeling. Multivariable logistic regression analyses and ROC curves were used to assess the predictive performance of these models in predicting HGP types.


A total of 126 CRLMs with histopathologic-demonstrated desmoplastic (n = 68) or replacement (n = 58) HGPs were assessed. The radiomics signature consisted of 20 features of each phase selected. The 3 phases fused radiomics signature demonstrated the best predictive performance in distinguishing between replacement and desmoplastic HGPs (AUCs of 0.926 and 0.939 in the training and external validation cohorts, respectively). The clinical-radiomics combined model showed good discrimination (C-indices of 0.941 and 0.833 in the training and external validation cohorts, respectively).


A radiomics model derived from MDCT images may effectively predict the HGP of CRLMs, thus providing a basis for prognostic stratification and therapeutic decision-making.

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

e.Med Interdisziplinär

Mit e.Med Interdisziplinär erhalten Sie Zugang zu allen CME-Fortbildungen und Fachzeitschriften auf Zusätzlich können Sie eine Zeitschrift Ihrer Wahl in gedruckter Form beziehen – ohne Aufpreis.

Jetzt e.Med zum Sonderpreis bestellen!

Weitere Produktempfehlungen anzeigen
Supplementary material 1 (DOCX 15630 kb)
Über diesen Artikel
  1. Sie können e.Med Chirurgie 14 Tage kostenlos testen (keine Print-Zeitschrift enthalten). Der Test läuft automatisch und formlos aus. Es kann nur einmal getestet werden.

  2. Das kostenlose Testabonnement läuft nach 14 Tagen automatisch und formlos aus. Dieses Abonnement kann nur einmal getestet werden.


Neu im Fachgebiet Chirurgie

Mail Icon II Newsletter

Bestellen Sie unseren kostenlosen Newsletter Update Chirurgie und bleiben Sie gut informiert – ganz bequem per eMail.