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- Title
Radiomics performs comparable to morphologic assessment by expert radiologists for prediction of response to neoadjuvant chemoradiotherapy on baseline staging MRI in rectal cancer.
- Authors
van Griethuysen, Joost J. M.; Lambregts, Doenja M. J.; Trebeschi, Stefano; Lahaye, Max J.; Bakers, Frans C. H.; Vliegen, Roy F. A.; Beets, Geerard L.; Aerts, Hugo J. W. L.; Beets-Tan, Regina G. H.
- Abstract
Purpose: To compare the performance of advanced radiomics analysis to morphological assessment by expert radiologists to predict a good or complete response to chemoradiotherapy in rectal cancer using baseline staging MRI. Materials and methods: We retrospectively assessed the primary staging MRIs [prior to chemoradiotherapy (CRT)] of 133 rectal cancer patients from 2 centers. First, two expert radiologists subjectively estimated the likelihood of achieving a "complete response" (ypT0) and "good response" (TRG 1–2), using a 5-point score (based on TN-stage, MRF/EMVI-status, size/signal/shape). Next, tumor volumes were segmented on high b value DWI (semi-automated, corrected by 2 non-expert and 2-expert readers, resulting in 5 segmentations), copied to the remaining sequences after which a total of 2505 radiomic features were extracted from T2W, low and high b value DWI and ADC. Stability of features for noise due to inter-reader and inter-scanner and protocol variations was assessed using intraclass correlation (ICC) and the Kruskal–Wallis test. Using data from center 1 (n = 86; training set), top 9 features were selected using minimum Redundancy Maximum Relevance and combined in a logistic regression model. Finally, diagnostic performance of the fitted models was assessed on data from center 2 (n = 47; validation set) and compared to the performance of the radiologists. Results: The Radiomic models resulted in AUCs of 0.69–0.79 (with similar results for the segmentations performed by expert/non-expert readers) to predict response, results similar to the morphologic prediction by the expert radiologists (AUC 0.67–0.83). Radiomics using semi-automatically generated segmentations (without manual input) did not result in significant predictive performance. Conclusions: Radiomics could predict response to therapy with comparable diagnostic performance as expert radiologists, regardless of whether image segmentation was performed by non-expert or expert readers, indicating that expert input is not required in order for the radiomics workflow to produce significant predictive performance.
- Subjects
RECTAL cancer; CANCER radiotherapy; ENDORECTAL ultrasonography; RADIOLOGISTS; CHEMORADIOTHERAPY; KRUSKAL-Wallis Test; PROGRESSION-free survival; INTRACLASS correlation
- Publication
Abdominal Radiology, 2020, Vol 45, Issue 3, p632
- ISSN
2366-004X
- Publication type
Article
- DOI
10.1007/s00261-019-02321-8