Computer-assisted interpretation of planar whole-body bone scintigraphy in patients with newly diagnosed prostate cancer

Petersen, Lars Jelstrup; Mortensen, Jørgen Birk; Bertelsen, Henrik

doi:10.1097/mnm.0000000000000307

Cited by 8 publications

(17 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Patient-specific settings, based on EAU risk classification, did not improve the diagnostic performance of the software. These findings are in line with our recent study on the use of a patient-specific setting for the diagnostic properties of EXINI Bone BSI on a patient level [19]. This function has been removed in recent versions of the software.…”

Section: Discussionsupporting

confidence: 89%

“…A manual correction of artefacts, e.g., lesions such as bladder catheters or bone implants that were classified as malignant lesions, was performed prior to analysis [19]. The BSI values, as well as the total number of malignant lesions, were significantly higher in patients who were classified as M1 by experts versus patients who were classified as M0 (both, p < 0.00001, Table 1).…”

Section: Resultsmentioning

confidence: 99%

“…The need for additional single photon emission computed tomography/computed tomography (SPECT/CT) was determined by the nuclear medicine physician in charge, whereas the use of supplementary magnetic resonance imaging (MRI) and/or CT was at the discretion of the referring urologist. The original study population consisted of 635 patients [18], of which 614 patients had a whole-body BS suitable for software analysis [19]. Data from one site (n = 272 patients) was excluded in this report due to the use of Interfile format for data storage.…”

Section: Methodsmentioning

confidence: 99%

“…Data from one site (n = 272 patients) was excluded in this report due to the use of Interfile format for data storage. It has previously been shown that EXINI Bone BSI provided significantly different results for Digital Imaging and Communications in Medicine (DICOM) file formats, compared to Interfile file formats [19]. Thus, this report included data for 342 patients from two sites that used DICOM files, which is the most common file format and the only format that is allowed in subsequent versions of the software.…”

Section: Methodsmentioning

confidence: 99%

“…We used ‘balanced mode’ as the default. The individual risk was calculated based on a modified European Association of Urology (EAU) risk classification [2], as previously described [19]. Thus, patients were classified as follows: 1) low risk if T1-T2, Gleason 2–6, and prostate specific antigen (PSA) < 10 ng/mL; 2) intermediate risk if T1-T2, Gleason 7 or PSA 10–20 ng/mL; 3) high risk if T3 or Gleason score 8–10 or PSA >20 ng/mL; or 4) very high risk if T4 or any T, N1.…”

Section: Methodsmentioning

confidence: 99%

See 4 more Smart Citations

Prospective evaluation of computer-assisted analysis of skeletal lesions for the staging of prostate cancer

2017

Self Cite

View full text Add to dashboard Cite

BackgroundThe purpose of this study was to compare the agreement of the bone scan index (BSI) using EXINI BoneBSI versus experts’ readings in the initial staging for bone metastasis in prostate cancer. In addition, the diagnostic outcome was assessed in a large subset of patients where a true reference for metastases could be determined based on clinical and biochemical follow-up and/or supplementary imaging.MethodsA total of 342 patients had a bone scintigraphy as part of routine staging for prostate cancer. Supplementary imaging was obtained at the discretion of the referring urologist. After full recruitment, the BSI and the number of malignant lesions were calculated using EXINI BoneBSI, and three imaging experts independently classified bone status by a dichotomous outcome (M1 for bone metastasis, M0 for no bone metastasis). A true reference was available in a subset of the patients based on post-operative prostate-specific antigen responses after radical prostatectomy and/or supplementary imaging.ResultsSoftware analysis with a BSI > 0 as the cut-off for metastasis showed excellent agreement with expert classification for M1 disease (96% of the patients) but modest agreement for M0 disease (38%). With a BSI > 1, the agreement was 58% for M1 and 98% for M0. Software analyses based on individual European Association of Urology risk classification did not improve the diagnostic performance. Among patients with a true reference, the software showed metastasis in 64% of the M0 patients but correctly classified metastases in all M1 patients. The sensitivity was 100%, the specificity was 36%, the positive predictive value was 12.6% and the negative predictive value was 100% with a BSI >0 compared with 66.7%, 97.8%, 72.7%, and 97.0% with a BSI > 1.ConclusionThe diagnostic value of using EXINI Bone for the BSI in the staging of newly diagnosed prostate cancer is limited.

show abstract

Section: Discussionsupporting

confidence: 89%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Prospective evaluation of computer-assisted analysis of skeletal lesions for the staging of prostate cancer

2017

Self Cite

View full text Add to dashboard Cite

show abstract

Automated measurement of bone scan index from a whole-body bone scintigram

et al. 2019

View full text Add to dashboard Cite

Purpose We propose a deep learning-based image interpretation system for skeleton segmentation and extraction of hot spots of bone metastatic lesion from a whole-body bone scintigram followed by automated measurement of a bone scan index (BSI), which will be clinically useful. Methods The proposed system employs butterfly-type networks (BtrflyNets) for skeleton segmentation and extraction of hot spots of bone metastatic lesions, in which a pair of anterior and posterior images are processed simultaneously. BSI is then measured using the segmented bones and extracted hot spots. To further improve the networks, deep supervision (DSV) and residual learning technologies were introduced. Results We evaluated the performance of the proposed system using 246 bone scintigrams of prostate cancer in terms of accuracy of skeleton segmentation, hot spot extraction, and BSI measurement, as well as computational cost. In a threefold cross-validation experiment, the best performance was achieved by BtrflyNet with DSV for skeleton segmentation and BtrflyNet with residual blocks. The cross-correlation between the measured and true BSI was 0.9337, and the computational time for a case was 112.0 s. Conclusion We proposed a deep learning-based BSI measurement system for a whole-body bone scintigram and proved its effectiveness by threefold cross-validation study using 246 whole-body bone scintigrams. The automatically measured BSI and computational time for a case are deemed clinically acceptable and reliable.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

show abstract

Extraction of metastasis hotspots in a whole-body bone scintigram based on bilateral asymmetry

et al. 2021

View full text Add to dashboard Cite

Computer-assisted interpretation of planar whole-body bone scintigraphy in patients with newly diagnosed prostate cancer

Cited by 8 publications

References 18 publications

Prospective evaluation of computer-assisted analysis of skeletal lesions for the staging of prostate cancer

Prospective evaluation of computer-assisted analysis of skeletal lesions for the staging of prostate cancer

Automated measurement of bone scan index from a whole-body bone scintigram

Extraction of metastasis hotspots in a whole-body bone scintigram based on bilateral asymmetry

Contact Info

Product

Resources

About