Automated bone scan index as predictors of survival in prostate cancer
Prostate cancer (PCa) is the second most diagnosed cancer in men. Early diagnosis and right management of PCa is critical to reducing deaths; the life expectancy is the main factors to be considered in the management of PCa. Among patients who die from PCa, the incidence of skeletal involvement appe...
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Thieme Medical and Scientific Publishers Pvt. Ltd.
2017-10-01
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| Series: | World Journal of Nuclear Medicine |
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| Online Access: | http://www.thieme-connect.de/DOI/DOI?10.4103/1450-1147.215498 |
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| author | Joko Wiyanto Rini Shintawati Budi Darmawan Basuki Hidayat Achmad Kartamihardja |
| author_facet | Joko Wiyanto Rini Shintawati Budi Darmawan Basuki Hidayat Achmad Kartamihardja |
| author_sort | Joko Wiyanto |
| collection | DOAJ |
| description | Prostate cancer (PCa) is the second most diagnosed cancer in men. Early diagnosis and right management of PCa is critical to reducing deaths; the life expectancy is the main factors to be considered in the management of PCa. Among patients who die from PCa, the incidence of skeletal involvement appears to be >85%. Bone scan (BS) is the most common method for monitoring bone metastases in patients with PCa. The extent of bone metastasis was also associated with patient survival until now there is no clinically useful technique for measuring bone tumors and includes this information in the risk assessment. An alternative approach is to calculate a BS index (BSI) and it has shown clinical significance as a prognostic imaging biomarker. Some computer-assisted diagnosis (CAD) systems have been developed to measure BSI and are now available. The aim of this study was to investigate automated BSI (aBSI) measurements as predictors' survival in PCa. Retrospectively cohort studied fifty patients with PCa who had undergone BS between January 2010 and December 2011 at our institution. All data collected was updated up to August 2016. CAD system analyzing BS images to automatically compute BSI measurements. Patients were stratified into three BSI categories BSI value 0, BSI value ≤1 and BSI value >1. Kaplan–Meier estimates of the survival function and the log-rank test were used to indicate a significant difference between groups stratified in accordance with the BSI values. A total of 35 subjects deaths were registered, with a median survival time 36 months after the follow-up BS of 5 years. Subjects with low aBSI value had longer overall survival in comparison with the other subjects (P = 0.004). aBSI measurements were shown to be a strong prognostic survival indicator in PCa; survival is poor in high-BSI value. |
| format | Article |
| id | doaj-art-adca679f15ee4ef38199e0fae9ea4e9a |
| institution | OA Journals |
| issn | 1450-1147 1607-3312 |
| language | English |
| publishDate | 2017-10-01 |
| publisher | Thieme Medical and Scientific Publishers Pvt. Ltd. |
| record_format | Article |
| series | World Journal of Nuclear Medicine |
| spelling | doaj-art-adca679f15ee4ef38199e0fae9ea4e9a2025-08-20T01:59:53ZengThieme Medical and Scientific Publishers Pvt. Ltd.World Journal of Nuclear Medicine1450-11471607-33122017-10-01160426627010.4103/1450-1147.215498Automated bone scan index as predictors of survival in prostate cancerJoko Wiyanto0Rini Shintawati1Budi Darmawan2Basuki Hidayat3Achmad Kartamihardja4Department of Nuclear Medicine and Molecular Imaging, Dr. Hasan Sadikin General Hospital, Faculty of Medicine Universitas Padjadjaran, Jawa BaratDepartment of Nuclear Medicine and Molecular Imaging, Dr. Hasan Sadikin General Hospital, Faculty of Medicine Universitas Padjadjaran, Jawa BaratDepartment of Nuclear Medicine and Molecular Imaging, Dr. Hasan Sadikin General Hospital, Faculty of Medicine Universitas Padjadjaran, Jawa BaratDepartment of Nuclear Medicine and Molecular Imaging, Dr. Hasan Sadikin General Hospital, Faculty of Medicine Universitas Padjadjaran, Jawa BaratDepartment of Nuclear Medicine and Molecular Imaging, Dr. Hasan Sadikin General Hospital, Faculty of Medicine Universitas Padjadjaran, Jawa BaratProstate cancer (PCa) is the second most diagnosed cancer in men. Early diagnosis and right management of PCa is critical to reducing deaths; the life expectancy is the main factors to be considered in the management of PCa. Among patients who die from PCa, the incidence of skeletal involvement appears to be >85%. Bone scan (BS) is the most common method for monitoring bone metastases in patients with PCa. The extent of bone metastasis was also associated with patient survival until now there is no clinically useful technique for measuring bone tumors and includes this information in the risk assessment. An alternative approach is to calculate a BS index (BSI) and it has shown clinical significance as a prognostic imaging biomarker. Some computer-assisted diagnosis (CAD) systems have been developed to measure BSI and are now available. The aim of this study was to investigate automated BSI (aBSI) measurements as predictors' survival in PCa. Retrospectively cohort studied fifty patients with PCa who had undergone BS between January 2010 and December 2011 at our institution. All data collected was updated up to August 2016. CAD system analyzing BS images to automatically compute BSI measurements. Patients were stratified into three BSI categories BSI value 0, BSI value ≤1 and BSI value >1. Kaplan–Meier estimates of the survival function and the log-rank test were used to indicate a significant difference between groups stratified in accordance with the BSI values. A total of 35 subjects deaths were registered, with a median survival time 36 months after the follow-up BS of 5 years. Subjects with low aBSI value had longer overall survival in comparison with the other subjects (P = 0.004). aBSI measurements were shown to be a strong prognostic survival indicator in PCa; survival is poor in high-BSI value.http://www.thieme-connect.de/DOI/DOI?10.4103/1450-1147.215498artificial neural networksbone metastasesbone scanbone scan indexcomputer-assisted diagnosisprostate cancersurvival analysis |
| spellingShingle | Joko Wiyanto Rini Shintawati Budi Darmawan Basuki Hidayat Achmad Kartamihardja Automated bone scan index as predictors of survival in prostate cancer World Journal of Nuclear Medicine artificial neural networks bone metastases bone scan bone scan index computer-assisted diagnosis prostate cancer survival analysis |
| title | Automated bone scan index as predictors of survival in prostate cancer |
| title_full | Automated bone scan index as predictors of survival in prostate cancer |
| title_fullStr | Automated bone scan index as predictors of survival in prostate cancer |
| title_full_unstemmed | Automated bone scan index as predictors of survival in prostate cancer |
| title_short | Automated bone scan index as predictors of survival in prostate cancer |
| title_sort | automated bone scan index as predictors of survival in prostate cancer |
| topic | artificial neural networks bone metastases bone scan bone scan index computer-assisted diagnosis prostate cancer survival analysis |
| url | http://www.thieme-connect.de/DOI/DOI?10.4103/1450-1147.215498 |
| work_keys_str_mv | AT jokowiyanto automatedbonescanindexaspredictorsofsurvivalinprostatecancer AT rinishintawati automatedbonescanindexaspredictorsofsurvivalinprostatecancer AT budidarmawan automatedbonescanindexaspredictorsofsurvivalinprostatecancer AT basukihidayat automatedbonescanindexaspredictorsofsurvivalinprostatecancer AT achmadkartamihardja automatedbonescanindexaspredictorsofsurvivalinprostatecancer |