Develop prediction model to help forecast advanced prostate cancer patients’ prognosis after surgery using neural network
BackgroundThe effect of surgery on advanced prostate cancer (PC) is unclear and predictive model for postoperative survival is lacking yet.MethodsWe investigate the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database, to collect clinical features of advanced PC pa...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-03-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2024.1293953/full |
| _version_ | 1797254061502758912 |
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| author | Shanshan Li Siyu Cai Siyu Cai Jinghong Huang Zongcheng Li Zhengyu Shi Kai Zhang Juan Jiao Wei Li Yuanming Pan |
| author_facet | Shanshan Li Siyu Cai Siyu Cai Jinghong Huang Zongcheng Li Zhengyu Shi Kai Zhang Juan Jiao Wei Li Yuanming Pan |
| author_sort | Shanshan Li |
| collection | DOAJ |
| description | BackgroundThe effect of surgery on advanced prostate cancer (PC) is unclear and predictive model for postoperative survival is lacking yet.MethodsWe investigate the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database, to collect clinical features of advanced PC patients. According to clinical experience, age, race, grade, pathology, T, N, M, stage, size, regional nodes positive, regional nodes examined, surgery, radiotherapy, chemotherapy, history of malignancy, clinical Gleason score (composed of needle core biopsy or transurethral resection of the prostate specimens), pathological Gleason score (composed of prostatectomy specimens) and prostate-specific antigen (PSA) are the potential predictive variables. All samples are divided into train cohort (70% of total, for model training) and test cohort (30% of total, for model validation) by random sampling. We then develop neural network to predict advanced PC patients’ overall. Area under receiver operating characteristic curve (AUC) is used to evaluate model’s performance.Results6380 patients, diagnosed with advanced (stage III-IV) prostate cancer and receiving surgery, have been included. The model using all collected clinical features as predictors and based on neural network algorithm performs best, which scores 0.7058 AUC (95% CIs, 0.7021-0.7068) in train cohort and 0.6925 AUC (95% CIs, 0.6906-0.6956) in test cohort. We then package it into a Windows 64-bit software.ConclusionPatients with advanced prostate cancer may benefit from surgery. In order to forecast their overall survival, we first build a clinical features-based prognostic model. This model is accuracy and may offer some reference on clinical decision making. |
| first_indexed | 2024-04-24T21:43:58Z |
| format | Article |
| id | doaj.art-ae0aa5ac213c4cd3924961aa7b6ef325 |
| institution | Directory Open Access Journal |
| issn | 1664-2392 |
| language | English |
| last_indexed | 2024-04-24T21:43:58Z |
| publishDate | 2024-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj.art-ae0aa5ac213c4cd3924961aa7b6ef3252024-03-21T04:25:59ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922024-03-011510.3389/fendo.2024.12939531293953Develop prediction model to help forecast advanced prostate cancer patients’ prognosis after surgery using neural networkShanshan Li0Siyu Cai1Siyu Cai2Jinghong Huang3Zongcheng Li4Zhengyu Shi5Kai Zhang6Juan Jiao7Wei Li8Yuanming Pan9Department of Clinical Laboratory, The Seventh Medical Center of Chinese PLA General Hospital, Beijing, ChinaCancer Research Center, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, ChinaDermatology Department, General Hospital of Western Theater Command, Chengdu, Sichuan, ChinaDepartment of Biochemistry, School of Medicine/Key Laboratory of Xinjiang Ministry of Education, Shihezi University, Shihezi, Xinjiang, ChinaUrinary Surgery Department, The First People’s Hospital of Ziyang, Ziyang, Sichuan, ChinaChengdu Eighth People’s Hospital, Chengdu, Sichuan, ChinaGeneral Department, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Tongzhou District, Beijing, ChinaDepartment of Clinical Laboratory, The Seventh Medical Center of Chinese PLA General Hospital, Beijing, ChinaCancer Research Center, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, ChinaCancer Research Center, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, ChinaBackgroundThe effect of surgery on advanced prostate cancer (PC) is unclear and predictive model for postoperative survival is lacking yet.MethodsWe investigate the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database, to collect clinical features of advanced PC patients. According to clinical experience, age, race, grade, pathology, T, N, M, stage, size, regional nodes positive, regional nodes examined, surgery, radiotherapy, chemotherapy, history of malignancy, clinical Gleason score (composed of needle core biopsy or transurethral resection of the prostate specimens), pathological Gleason score (composed of prostatectomy specimens) and prostate-specific antigen (PSA) are the potential predictive variables. All samples are divided into train cohort (70% of total, for model training) and test cohort (30% of total, for model validation) by random sampling. We then develop neural network to predict advanced PC patients’ overall. Area under receiver operating characteristic curve (AUC) is used to evaluate model’s performance.Results6380 patients, diagnosed with advanced (stage III-IV) prostate cancer and receiving surgery, have been included. The model using all collected clinical features as predictors and based on neural network algorithm performs best, which scores 0.7058 AUC (95% CIs, 0.7021-0.7068) in train cohort and 0.6925 AUC (95% CIs, 0.6906-0.6956) in test cohort. We then package it into a Windows 64-bit software.ConclusionPatients with advanced prostate cancer may benefit from surgery. In order to forecast their overall survival, we first build a clinical features-based prognostic model. This model is accuracy and may offer some reference on clinical decision making.https://www.frontiersin.org/articles/10.3389/fendo.2024.1293953/fullprediction modelprostate cancerprognosissurgeryneural networkdeep learning |
| spellingShingle | Shanshan Li Siyu Cai Siyu Cai Jinghong Huang Zongcheng Li Zhengyu Shi Kai Zhang Juan Jiao Wei Li Yuanming Pan Develop prediction model to help forecast advanced prostate cancer patients’ prognosis after surgery using neural network Frontiers in Endocrinology prediction model prostate cancer prognosis surgery neural network deep learning |
| title | Develop prediction model to help forecast advanced prostate cancer patients’ prognosis after surgery using neural network |
| title_full | Develop prediction model to help forecast advanced prostate cancer patients’ prognosis after surgery using neural network |
| title_fullStr | Develop prediction model to help forecast advanced prostate cancer patients’ prognosis after surgery using neural network |
| title_full_unstemmed | Develop prediction model to help forecast advanced prostate cancer patients’ prognosis after surgery using neural network |
| title_short | Develop prediction model to help forecast advanced prostate cancer patients’ prognosis after surgery using neural network |
| title_sort | develop prediction model to help forecast advanced prostate cancer patients prognosis after surgery using neural network |
| topic | prediction model prostate cancer prognosis surgery neural network deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2024.1293953/full |
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