Distinguishing pure histopathological growth patterns of colorectal liver metastases on CT using deep learning and radiomics: a pilot study

Histopathological growth patterns (HGPs) are independent prognosticators for colorectal liver metastases (CRLM). Currently, HGPs are determined postoperatively. In this study, we evaluated radiomics for preoperative prediction of HGPs on computed tomography (CT), and its robustness to segmentation a...

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Bibliographic Details
Published in:Clinical & experimental metastasis 2021-10, Vol.38 (5), p.483-494
Main Authors: Starmans, Martijn P. A., Buisman, Florian E., Renckens, Michel, Willemssen, François E. J. A., van der Voort, Sebastian R., Groot Koerkamp, Bas, Grünhagen, Dirk J., Niessen, Wiro J., Vermeulen, Peter B., Verhoef, Cornelis, Visser, Jacob J., Klein, Stefan
Format: Article
Language:English
Subjects:
Aged
Artificial neural networks
Biomarkers
Biomedical and Life Sciences
Biomedicine
Cancer Research
Colorectal Neoplasms - pathology
Computed tomography
Correlation coefficients
Deep Learning
Evaluation
Female
Growth patterns
Hematology
Humans
Learning algorithms
Liver
Liver cancer
Liver Neoplasms - diagnostic imaging
Liver Neoplasms - secondary
Machine learning
Male
Metastases
Metastasis
Middle Aged
Neural networks
Oncology
Optimization
Pilot Projects
Prediction models
Radiomics
Research Paper
Robustness
Segmentation
Surgical Oncology
Tomography, X-Ray Computed - methods
Variation
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Summary:Histopathological growth patterns (HGPs) are independent prognosticators for colorectal liver metastases (CRLM). Currently, HGPs are determined postoperatively. In this study, we evaluated radiomics for preoperative prediction of HGPs on computed tomography (CT), and its robustness to segmentation and acquisition variations. Patients with pure HGPs [i.e. 100% desmoplastic (dHGP) or 100% replacement (rHGP)] and a CT-scan who were surgically treated at the Erasmus MC between 2003–2015 were included retrospectively. Each lesion was segmented by three clinicians and a convolutional neural network (CNN). A prediction model was created using 564 radiomics features and a combination of machine learning approaches by training on the clinician’s and testing on the unseen CNN segmentations. The intra-class correlation coefficient (ICC) was used to select features robust to segmentation variations; ComBat was used to harmonize for acquisition variations. Evaluation was performed through a 100 × random-split cross-validation. The study included 93 CRLM in 76 patients (48% dHGP; 52% rHGP). Despite substantial differences between the segmentations of the three clinicians and the CNN, the radiomics model had a mean area under the curve of 0.69. ICC-based feature selection or ComBat yielded no improvement. Concluding, the combination of a CNN for segmentation and radiomics for classification has potential for automatically distinguishing dHGPs from rHGP, and is robust to segmentation and acquisition variations. Pending further optimization, including extension to mixed HGPs, our model may serve as a preoperative addition to postoperative HGP assessment, enabling further exploitation of HGPs as a biomarker.
ISSN:0262-0898
1573-7276
DOI:10.1007/s10585-021-10119-6