OBJECTIVE: The development and evaluation of a computer-aided bone scan analysis technique to quantify changes in tumor burden and assess treatment effects in prostate cancer clinical trials. METHODS: We have developed and report on a commercial fully automated computer-aided detection (CAD) system. Using this system, scan images were intensity normalized, and then lesions were identified and segmented by anatomic region-specific intensity thresholding. Detected lesions were compared against expert markings to assess the accuracy of the CAD system. The metrics Bone Scan Lesion Area, Bone Scan Lesion Intensity, and Bone Scan Lesion Count were calculated from identified lesions, and their utility in assessing treatment effects was evaluated by analyzing before and after scans from metastatic castration-resistant prostate cancer patients: 10 treated and 10 untreated. In this study, patients were treated with cabozantinib, a MET/vascular endothelial growth factor inhibitor resulting in high rates of resolution of bone scan abnormalities. RESULTS: Our automated CAD system identified bone lesion pixels with 94% sensitivity, 89% specificity, and 89% accuracy. Significant differences in changes from baseline were found between treated and untreated groups in all assessed measurements derived by our system. The most significant measure, Bone Scan Lesion Area, showed a median (interquartile range) change from baseline at week 6 of 7.13% (27.61) in the untreated group compared with -73.76% (45.38) in the cabozantinib-treated group (P=0.0003). CONCLUSION: Our system accurately and objectively identified and quantified metastases in bone scans, allowing for interpatient and intrapatient comparison. It demonstrates potential as an objective measurement of treatment effects, laying the foundation for validation against other clinically relevant outcome measures.
OBJECTIVE: The development and evaluation of a computer-aided bone scan analysis technique to quantify changes in tumor burden and assess treatment effects in prostate cancer clinical trials. METHODS: We have developed and report on a commercial fully automated computer-aided detection (CAD) system. Using this system, scan images were intensity normalized, and then lesions were identified and segmented by anatomic region-specific intensity thresholding. Detected lesions were compared against expert markings to assess the accuracy of the CAD system. The metrics Bone Scan Lesion Area, Bone Scan Lesion Intensity, and Bone Scan Lesion Count were calculated from identified lesions, and their utility in assessing treatment effects was evaluated by analyzing before and after scans from metastatic castration-resistant prostate cancerpatients: 10 treated and 10 untreated. In this study, patients were treated with cabozantinib, a MET/vascular endothelial growth factor inhibitor resulting in high rates of resolution of bone scan abnormalities. RESULTS: Our automated CAD system identified bone lesion pixels with 94% sensitivity, 89% specificity, and 89% accuracy. Significant differences in changes from baseline were found between treated and untreated groups in all assessed measurements derived by our system. The most significant measure, Bone Scan Lesion Area, showed a median (interquartile range) change from baseline at week 6 of 7.13% (27.61) in the untreated group compared with -73.76% (45.38) in the cabozantinib-treated group (P=0.0003). CONCLUSION: Our system accurately and objectively identified and quantified metastases in bone scans, allowing for interpatient and intrapatient comparison. It demonstrates potential as an objective measurement of treatment effects, laying the foundation for validation against other clinically relevant outcome measures.
Authors: David Mattes; David R Haynor; Hubert Vesselle; Thomas K Lewellen; William Eubank Journal: IEEE Trans Med Imaging Date: 2003-01 Impact factor: 10.048
Authors: Charles J Ryan; Shreya Shah; Eleni Efstathiou; Matthew R Smith; Mary-Ellen Taplin; Glenn J Bubley; Christopher J Logothetis; Thian Kheoh; Christine Kilian; Christopher M Haqq; Arturo Molina; Eric J Small Journal: Clin Cancer Res Date: 2011-06-01 Impact factor: 12.531
Authors: Weon-Kyoo You; Barbara Sennino; Casey W Williamson; Beverly Falcón; Hiroya Hashizume; Li-Chin Yao; Dana T Aftab; Donald M McDonald Journal: Cancer Res Date: 2011-05-25 Impact factor: 12.701
Authors: Guru Sonpavde; Gregory R Pond; William R Berry; Ronald de Wit; Mario A Eisenberger; Ian F Tannock; Andrew J Armstrong Journal: Cancer Date: 2011-03-01 Impact factor: 6.860
Authors: May Sadik; Madis Suurkula; Peter Höglund; Andreas Järund; Lars Edenbrandt Journal: Eur J Nucl Med Mol Imaging Date: 2008-03-29 Impact factor: 9.236
Authors: Aseem Anand; Michael J Morris; Reza Kaboteh; Lena Båth; May Sadik; Peter Gjertsson; Milan Lomsky; Lars Edenbrandt; David Minarik; Anders Bjartell Journal: J Nucl Med Date: 2015-08-27 Impact factor: 10.057
Authors: Bernard Escudier; Thomas Powles; Robert J Motzer; Thomas Olencki; Osvaldo Arén Frontera; Stephane Oudard; Frederic Rolland; Piotr Tomczak; Daniel Castellano; Leonard J Appleman; Harry Drabkin; Daniel Vaena; Steven Milwee; Jillian Youkstetter; Julie C Lougheed; Sergio Bracarda; Toni K Choueiri Journal: J Clin Oncol Date: 2018-01-08 Impact factor: 44.544
Authors: Howard I Scher; Michael J Morris; Walter M Stadler; Celestia Higano; Ethan Basch; Karim Fizazi; Emmanuel S Antonarakis; Tomasz M Beer; Michael A Carducci; Kim N Chi; Paul G Corn; Johann S de Bono; Robert Dreicer; Daniel J George; Elisabeth I Heath; Maha Hussain; Wm Kevin Kelly; Glenn Liu; Christopher Logothetis; David Nanus; Mark N Stein; Dana E Rathkopf; Susan F Slovin; Charles J Ryan; Oliver Sartor; Eric J Small; Matthew Raymond Smith; Cora N Sternberg; Mary-Ellen Taplin; George Wilding; Peter S Nelson; Lawrence H Schwartz; Susan Halabi; Philip W Kantoff; Andrew J Armstrong Journal: J Clin Oncol Date: 2016-02-22 Impact factor: 44.544
Authors: Ethan M Basch; Mark Scholz; Johann S de Bono; Nicholas Vogelzang; Paul de Souza; Gavin Marx; Ulka Vaishampayan; Saby George; James K Schwarz; Emmanuel S Antonarakis; Joseph M O'Sullivan; Arash Rezazadeh Kalebasty; Kim N Chi; Robert Dreicer; Thomas E Hutson; Amylou C Dueck; Antonia V Bennett; Erica Dayan; Milan Mangeshkar; Jaymes Holland; Aaron L Weitzman; Howard I Scher Journal: Eur Urol Date: 2018-12-04 Impact factor: 20.096
Authors: Richard J Lee; Philip J Saylor; M Dror Michaelson; S Michael Rothenberg; Malgorzata E Smas; David T Miyamoto; Carol A Gurski; Wanling Xie; Shyamala Maheswaran; Daniel A Haber; Jonathan G Goldin; Matthew R Smith Journal: Clin Cancer Res Date: 2013-04-03 Impact factor: 12.531