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

• Published in: Clinical & experimental metastasis Vol. 38; no. 5; pp. 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: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.10.2021; Springer Nature B.V
• 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; Deep learning; Machine learning; Biomarkers; Tomography; X-ray; Liver neoplasms; Computed

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...