PT  - JOURNAL ARTICLE
AU  - AKAZAWA, MUNETOSHI
AU  - HASHIMOTO, KAZUNORI
TI  - Preliminary Results of Deep Learning Approach for Preoperative Diagnosis of Ovarian Cancer Based on Pelvic MRI Scans
AID  - 10.21873/anticanres.16568
DP  - 2023 Aug 01
TA  - Anticancer Research
PG  - 3817--3821
VI  - 43
IP  - 8
4099  - http://ar.iiarjournals.org/content/43/8/3817.short
4100  - http://ar.iiarjournals.org/content/43/8/3817.full
SO  - Anticancer Res2023 Aug 01; 43
AB  - Background/Aim: To predict the pathological diagnosis of ovarian tumors using preoperative MRI images, using deep learning models. Patients and Methods: A total of 185 patients were enrolled, including 40 with ovarian cancers, 25 with borderline malignant tumors, and 120 with benign tumors. Using sagittal and horizontal T2-weighted images (T2WI), we constructed the pre-trained convolutional neural networks to predict pathological diagnoses. The performance of the model was assessed by precision, recall, and F1-score on macro-average with 95% confidence interval (95%CI). The accuracy and area under the curve (AUC) were also assessed after binary transformation by the division into benign and non-benign groups. Results: The macro-average accuracy in the three-class classification was 0.523 (95%CI=0.504-0.544) for sagittal images and 0.426 (95%CI=0.404-0.446) for horizontal images. The model achieved a precision of 0.63 (95%CI=0.61-0.66), recall of 0.75 (95%CI=0.72-0.78), and F1 score of 0.69 (95%CI=0.67-0.71) for benign tumor. Regarding the discrimination between benign and non-benign tumors, the accuracy in the binary-class classification was 0.628 (95%CI=0.592-0.662) for sagittal images and AUC was 0.529 (95%CI=0.500-0.557). Conclusion: Using deep learning, we could perform pathological diagnosis from preoperative MRI images.