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Prediction of transformation in the histopathological growth pattern of colorectal liver metastases after chemotherapy using CT-based radiomics

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Abstract

Chemotherapy alters the prognostic biomarker histopathological growth pattern (HGP) phenotype in colorectal liver metastases (CRLMs) patients. We aimed to develop a CT-based radiomics model to predict the transformation of the HGP phenotype after chemotherapy. This study included 181 patients with 298 CRLMs who underwent preoperative contrast-enhanced CT followed by partial hepatectomy between January 2007 and July 2022 at two institutions. HGPs were categorized as pure desmoplastic HGP (pdHGP) or non-pdHGP. The samples were allocated to training, internal validation, and external validation cohorts comprising 153, 65, and 29 CRLMs, respectively. Radiomics analysis was performed on pre-enhanced, arterial phase, portal venous phase (PVP), and fused images. The model was used to predict prechemotherapy HGPs in 112 CRLMs, and HGP transformation was analysed by comparing these findings with postchemotherapy HGPs determined pathologically. The prevalence of pdHGP was 19.8% (23/116) and 45.8% (70/153) in chemonaïve and postchemotherapy patients, respectively (P < 0.001). The PVP radiomics signature showed good performance in distinguishing pdHGP from non-pdHGPs (AUCs of 0.906, 0.877, and 0.805 in the training, internal validation, and external validation cohorts, respectively). The prevalence of prechemotherapy pdHGP predicted by the radiomics model was 33.0% (37/112), and the prevalence of postchemotherapy pdHGP according to the pathological analysis was 47.3% (53/112; P = 0.029). The transformation of HGP was bidirectional, with 15.2% (17/112) of CRLMs transforming from prechemotherapy pdHGP to postchemotherapy non-pdHGP and 30.4% (34/112) transforming from prechemotherapy non-pdHGP to postchemotherapy pdHGP (P = 0.005). CT-based radiomics method can be used to effectively predict the HGP transformation in chemotherapy-treated CRLM patients, thereby providing a basis for treatment decisions.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported by [the Natural Science Foundation of Beijing Municipality] (Grant numbers [7232187] and [7202217]); and [the Beijing Xisike Clinical Oncology Research Foundation] (Grant numbers [Y-Young2022-0329]).

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Author notes

  1. Weiqi Sheng, Jin Cheng and Yi Wang contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Peking University People’s Hospital, 11 Xizhimen South St, Beijing, 100044, China

    Shengcai Wei, Xinyi Gou, Nan Hong, Jin Cheng & Yi Wang

  2. Department of Pathology, Peking University People’s Hospital, 11 Xizhimen South St, Beijing, 100044, China

    Yinli Zhang

  3. Department of Research and Development, United Imaging Intelligence (Beijing) Co., Ltd, Yongteng North Road, Haidian District, Beijing, 100094, China

    Jingjing Cui

  4. Department of Research and Development, Beijing United Imaging Research Institute of Intelligent Imaging, Yongteng North Road, Haidian District, Beijing, 100089, China

    Xiaoming Liu

  5. Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China

    Weiqi Sheng

  6. Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China

    Weiqi Sheng

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  1. Shengcai Wei
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  2. Xinyi Gou
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  9. Yi Wang
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Contributions

S.C.W. designed the study, acquired the data, interpreted the results and drafted the manuscript. X.Y.G. and Y.L.Z. acquired the data, interpreted the results and generated the figures. J.J.C., X.M.L., and N.H. analyzed and interpreted the data and searched the literatures. W.Q.S. designed the study, acquired the data and generated the figures. J.C. and Y.W. conceived and designed the study, interpreted the results and provided funding support. All authors were involved in writing the paper and had final approval of the submitted and published versions.

Corresponding authors

Correspondence to Weiqi Sheng, Jin Cheng or Yi Wang.

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The study was approved by the institutional review boards at the Peking University People’s Hospital and Fudan University Shanghai Cancer Center with a waiver of informed consent.

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The authors declare no potential conflicts of interest.

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Wei, S., Gou, X., Zhang, Y. et al. Prediction of transformation in the histopathological growth pattern of colorectal liver metastases after chemotherapy using CT-based radiomics. Clin Exp Metastasis 41, 143–154 (2024). https://doi.org/10.1007/s10585-024-10275-5

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