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

  • Jin Cheng
  • Jingwei Wei
  • Tong Tong
  • Weiqi Sheng
  • Yinli Zhang
  • Yuqi Han
  • Dongsheng Gu
  • Nan Hong
  • Yingjiang Ye
  • Jie TianEmail author
  • Yi WangEmail author
Hepatobiliary Tumors
  • 71 Downloads

Abstract

Objectives

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

Methods

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.

Results

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).

Conclusions

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.

Notes

Acknowledgment

This work was supported by Natural Science Foundation of Beijing under Grant Nos. 7172226; Ministry of Science and Technology of China under Grant No. 2017YFA0205200; National Natural Science Foundation of China under Grant Nos. 81227901 and 81527805; Chinese Academy of Sciences under Grant Nos. GJJSTD20170004 and QYZDJ-SSW-JSC005; Beijing Municipal Science & Technology Commission under Grant Nos. Z161100002616022 and Z171100000117023; and the Key International Cooperation Projects of the Chinese Academy of Sciences under Grant No. 173211KYSB20160053.

Disclosures

The authors declare that they have no conflict of interest.

Supplementary material

10434_2019_7910_MOESM1_ESM.docx (15.3 mb)
Supplementary material 1 (DOCX 15630 kb)

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Copyright information

© Society of Surgical Oncology 2019

Authors and Affiliations

  • Jin Cheng
    • 1
  • Jingwei Wei
    • 2
    • 3
    • 4
  • Tong Tong
    • 5
  • Weiqi Sheng
    • 6
  • Yinli Zhang
    • 7
  • Yuqi Han
    • 2
    • 3
    • 4
  • Dongsheng Gu
    • 2
    • 3
    • 4
  • Nan Hong
    • 1
  • Yingjiang Ye
    • 8
  • Jie Tian
    • 2
    • 3
    • 4
    • 9
    • 10
    Email author
  • Yi Wang
    • 1
    Email author
  1. 1.Department of RadiologyPeking University People’s HospitalBeijingChina
  2. 2.Key Laboratory of Molecular ImagingInstitute of Automation, Chinese Academy of SciencesBeijingChina
  3. 3.Beijing Key Laboratory of Molecular ImagingBeijingChina
  4. 4.University of Chinese Academy of SciencesBeijingChina
  5. 5.Department of Radiology, Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai Medical CollegeFudan UniversityShanghaiChina
  6. 6.Department of Pathology, Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai Medical CollegeFudan UniversityShanghaiChina
  7. 7.Department of PathologyPeking University People’s HospitalBeijingChina
  8. 8.Department of Gastrointestinal SurgeryPeking University People’ HospitalBeijingChina
  9. 9.Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of MedicineBeihang UniversityBeijingChina
  10. 10.Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and TechnologyXidian UniversityXi’anChina

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