Ji Y Ha1, Kyung N Jeon1,2, Kyungsoo Bae1,2, Bong H Choi1,3. 1. 1 Department of Radiology, Gyeongsang National University School of Medicine, Jinju, Republic of Korea. 2. 2 Department of Radiology, Gyeongsang National University Changwon Hospital, Changwon, Republic of Korea. 3. 3 Department of Nuclear Medicine, Gyeongsang National University Hospital, Jinju, Republic of Korea.
Abstract
OBJECTIVE: To evaluate the effect of CT software that generates rib unfolding images and automatically numbers ribs and thoracic spines on radiologist performance in detecting thoracic bone metastases from breast cancer. METHODS: A total of 126 patients with breast cancer who underwent chest CT and fludeoxyglucose (FDG)-positron emission tomography (PET)/CT and/or bone scans were retrospectively reviewed. One board-certified radiologist (R1) and one radiology resident (R2) independently assessed the original chest CT and rib unfolding images using a commercially available post-processing software (Bone Reading) application to evaluate metastasis in the ribs and thoracic spines. Results were compared with reference standard based on CT, FDG-PET/CT and/or bone scan with follow-up. RESULTS: Based on reference standard, 78 metastatic bone lesions in 26 patients were identified. On per-patient-based analysis, Bone Reading assessed by R1/R2 had a sensitivity of 84.6%/80.8% and a specificity of 94.0%/94.0% with an accuracy of 92.1%/91.3%. The original CT reading yielded a sensitivity of 73.1%/57.7% and a specificity of 95.0%/94.0% with an accuracy of 90.5%/86.5%. The sensitivity and accuracy of Bone Reading were significantly higher than those of CT reading, as assessed by R2 (both p = 0.031). On per-lesion-based analysis, Bone Reading assessed by R1/R2 yielded a sensitivity of 84.6%/82.1% and a specificity of 99.7%/99.6% with an accuracy of 99.4%/99.3%, while the original CT reading yielded a sensitivity of 71.8%/62.8% and a specificity of 99.6%/99.5% with an accuracy of 99.2%/98.9%. The sensitivity and accuracy with Bone Reading application were significantly higher than those with CT reading by both readers (R1, p = 0.006 and p = 0.036, respectively; R2, both p < 0.001). The mean reading time needed for Bone Reading application was significantly shorter than that for original chest CT reading (p < 0.001). Bone Reading application helped readers find small and sclerotic lesions missed in original CT reading. CONCLUSION: In patients with breast cancer, the use of Bone Reading application improved radiologist performance in bone metastasis detection compared with original chest CT reading with reduced reading time. This software will be more helpful to inexperienced radiologists for improving the reading performance. Advances in knowledge: Small and sclerotic lesions can be easily missed in original CT reading. Using Bone Reading CT software can enhance the performance of radiologists in detecting bone metastasis in breast cancer. False-negative rates can be significantly reduced in both inexperienced and experienced readers.
OBJECTIVE: To evaluate the effect of CT software that generates rib unfolding images and automatically numbers ribs and thoracic spines on radiologist performance in detecting thoracic bone metastases from breast cancer. METHODS: A total of 126 patients with breast cancer who underwent chest CT and fludeoxyglucose (FDG)-positron emission tomography (PET)/CT and/or bone scans were retrospectively reviewed. One board-certified radiologist (R1) and one radiology resident (R2) independently assessed the original chest CT and rib unfolding images using a commercially available post-processing software (Bone Reading) application to evaluate metastasis in the ribs and thoracic spines. Results were compared with reference standard based on CT, FDG-PET/CT and/or bone scan with follow-up. RESULTS: Based on reference standard, 78 metastatic bone lesions in 26 patients were identified. On per-patient-based analysis, Bone Reading assessed by R1/R2 had a sensitivity of 84.6%/80.8% and a specificity of 94.0%/94.0% with an accuracy of 92.1%/91.3%. The original CT reading yielded a sensitivity of 73.1%/57.7% and a specificity of 95.0%/94.0% with an accuracy of 90.5%/86.5%. The sensitivity and accuracy of Bone Reading were significantly higher than those of CT reading, as assessed by R2 (both p = 0.031). On per-lesion-based analysis, Bone Reading assessed by R1/R2 yielded a sensitivity of 84.6%/82.1% and a specificity of 99.7%/99.6% with an accuracy of 99.4%/99.3%, while the original CT reading yielded a sensitivity of 71.8%/62.8% and a specificity of 99.6%/99.5% with an accuracy of 99.2%/98.9%. The sensitivity and accuracy with Bone Reading application were significantly higher than those with CT reading by both readers (R1, p = 0.006 and p = 0.036, respectively; R2, both p < 0.001). The mean reading time needed for Bone Reading application was significantly shorter than that for original chest CT reading (p < 0.001). Bone Reading application helped readers find small and sclerotic lesions missed in original CT reading. CONCLUSION: In patients with breast cancer, the use of Bone Reading application improved radiologist performance in bone metastasis detection compared with original chest CT reading with reduced reading time. This software will be more helpful to inexperienced radiologists for improving the reading performance. Advances in knowledge: Small and sclerotic lesions can be easily missed in original CT reading. Using Bone Reading CT software can enhance the performance of radiologists in detecting bone metastasis in breast cancer. False-negative rates can be significantly reduced in both inexperienced and experienced readers.
Authors: Christina Fitzmaurice; Daniel Dicker; Amanda Pain; Hannah Hamavid; Maziar Moradi-Lakeh; Michael F MacIntyre; Christine Allen; Gillian Hansen; Rachel Woodbrook; Charles Wolfe; Randah R Hamadeh; Ami Moore; Andrea Werdecker; Bradford D Gessner; Braden Te Ao; Brian McMahon; Chante Karimkhani; Chuanhua Yu; Graham S Cooke; David C Schwebel; David O Carpenter; David M Pereira; Denis Nash; Dhruv S Kazi; Diego De Leo; Dietrich Plass; Kingsley N Ukwaja; George D Thurston; Kim Yun Jin; Edgar P Simard; Edward Mills; Eun-Kee Park; Ferrán Catalá-López; Gabrielle deVeber; Carolyn Gotay; Gulfaraz Khan; H Dean Hosgood; Itamar S Santos; Janet L Leasher; Jasvinder Singh; James Leigh; Jost B Jonas; Jost Jonas; Juan Sanabria; Justin Beardsley; Kathryn H Jacobsen; Ken Takahashi; Richard C Franklin; Luca Ronfani; Marcella Montico; Luigi Naldi; Marcello Tonelli; Johanna Geleijnse; Max Petzold; Mark G Shrime; Mustafa Younis; Naohiro Yonemoto; Nicholas Breitborde; Paul Yip; Farshad Pourmalek; Paulo A Lotufo; Alireza Esteghamati; Graeme J Hankey; Raghib Ali; Raimundas Lunevicius; Reza Malekzadeh; Robert Dellavalle; Robert Weintraub; Robyn Lucas; Roderick Hay; David Rojas-Rueda; Ronny Westerman; Sadaf G Sepanlou; Sandra Nolte; Scott Patten; Scott Weichenthal; Semaw Ferede Abera; Seyed-Mohammad Fereshtehnejad; Ivy Shiue; Tim Driscoll; Tommi Vasankari; Ubai Alsharif; Vafa Rahimi-Movaghar; Vasiliy V Vlassov; W S Marcenes; Wubegzier Mekonnen; Yohannes Adama Melaku; Yuichiro Yano; Al Artaman; Ismael Campos; Jennifer MacLachlan; Ulrich Mueller; Daniel Kim; Matias Trillini; Babak Eshrati; Hywel C Williams; Kenji Shibuya; Rakhi Dandona; Kinnari Murthy; Benjamin Cowie; Azmeraw T Amare; Carl Abelardo Antonio; Carlos Castañeda-Orjuela; Coen H van Gool; Francesco Violante; In-Hwan Oh; Kedede Deribe; Kjetil Soreide; Luke Knibbs; Maia Kereselidze; Mark Green; Rosario Cardenas; Nobhojit Roy; Taavi Tillmann; Taavi Tillman; Yongmei Li; Hans Krueger; Lorenzo Monasta; Subhojit Dey; Sara Sheikhbahaei; Nima Hafezi-Nejad; G Anil Kumar; Chandrashekhar T Sreeramareddy; Lalit Dandona; Haidong Wang; Stein Emil Vollset; Ali Mokdad; Joshua A Salomon; Rafael Lozano; Theo Vos; Mohammad Forouzanfar; Alan Lopez; Christopher Murray; Mohsen Naghavi Journal: JAMA Oncol Date: 2015-07 Impact factor: 31.777
Authors: Georg Homann; Deedar F Mustafa; Hendrik Ditt; Werner Spengler; Hans-Georg Kopp; Konstantin Nikolaou; Marius Horger Journal: Acad Radiol Date: 2015-01-10 Impact factor: 3.173
Authors: Helmut Ringl; Mathias Lazar; Michael Töpker; Ramona Woitek; Helmut Prosch; Ulrika Asenbaum; Csilla Balassy; Daniel Toth; Michael Weber; Stefan Hajdu; Grzegorz Soza; Andreas Wimmer; Thomas Mang Journal: Eur Radiol Date: 2015-02-14 Impact factor: 5.315
Authors: Katherine P Andriole; Richard L Morin; Ronald L Arenson; John A Carrino; Bradley J Erickson; Steven C Horii; David W Piraino; Bruce I Reiner; J Anthony Seibert; Eliot Siegel Journal: J Digit Imaging Date: 2004-11-25 Impact factor: 4.056
Authors: Tsuyoshi Hamaoka; John E Madewell; Donald A Podoloff; Gabriel N Hortobagyi; Naoto T Ueno Journal: J Clin Oncol Date: 2004-07-15 Impact factor: 44.544
Authors: C C Quattrocchi; S Piciucchi; M Sammarra; D Santini; B Vincenzi; G Tonini; R F Grasso; B B Zobel Journal: Radiol Med Date: 2007-10-21 Impact factor: 6.313
Authors: Martin Kolopp; Nicolas Douis; Ayla Urbaneja; Cédric Baumann; Pedro Augusto Gondim Teixeira; Alain Blum; Laurent Martrille Journal: Int J Legal Med Date: 2019-11-16 Impact factor: 2.686