Pilot study of an artificial intelligence-based deep learning algorithm to predict time to castration-resistant prostate cancer for metastatic hormone-naïve prostate cancer.
Wataru NakataHideo MoriGoh TsujimuraYuichi TsujimotoTakayoshi GotohMasao TsujihataPublished in: Japanese journal of clinical oncology (2022)
The object in this study is to develop an artificial intelligence-based deep learning algorithm for prediction of time to castration-resistant prostate cancer by combined androgen blockade therapy in metastatic hormone-naïve prostate cancer. We included 180 metastatic hormone-naïve prostate cancer patients who initially received combined androgen blockade. We first evaluated whether time to castration-resistant prostate cancer was a significant prognostic factor. Then, using the patients' needle-biopsy specimen images, we developed and validated our deep learning algorithm. The results are shown below. First, we confirmed that time to castration-resistant prostate cancer correlated with overall survival (P < 0.001). Next, we selected two groups by time to castration-resistant prostate cancer of >24 months (n = 18) and <6 months (n = 6) and developed a deep learning algorithm by artificial intelligence-based machine deep learning. In 16 other metastatic hormone-naïve prostate cancer patients used as an external validation set, we confirmed the prediction accuracy remained significant (P < 0.05). In conclusion, our obtained deep learning algorithm has high predictive ability for the effectiveness of combined androgen blockade.
Keyphrases
- deep learning
- artificial intelligence
- prostate cancer
- big data
- convolutional neural network
- prognostic factors
- machine learning
- squamous cell carcinoma
- small cell lung cancer
- radical prostatectomy
- end stage renal disease
- systematic review
- ejection fraction
- newly diagnosed
- chronic kidney disease
- stem cells
- cell therapy
- free survival
- benign prostatic hyperplasia
- fine needle aspiration