Analysis of regional bone scan index measurements for the survival of patients with prostate cancer

Kalderstam, Jonas; Sadik, May; Edenbrandt, Lars; Ohlsson, Mattias

doi:10.1186/1471-2342-14-24

Cited by 11 publications

(5 citation statements)

References 26 publications

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“…Thirdly, our study just focused on the performance of the AI model on the diagnosis of absence or presence of bone metastasis to assist nuclear medicine physicians' interpretation. However, a series previous study demonstrated that the bone scan index (BSI) calculated by artificial neural networks is an effective biomarker for predicting the prognosis or survival of some malignant cancers 26 – 29 . Whether our AI model could be beneficial to the assessment of the prognosis or survival of some malignant cancers like BSI, it might require more concentration on lesion-based analysis.…”

Section: Discussionmentioning

confidence: 99%

Deep neural network based artificial intelligence assisted diagnosis of bone scintigraphy for cancer bone metastasis

Zhen

Jiang

et al. 2020

Sci Rep

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Bone scintigraphy (BS) is one of the most frequently utilized diagnostic techniques in detecting cancer bone metastasis, and it occupies an enormous workload for nuclear medicine physicians. So, we aimed to architecture an automatic image interpreting system to assist physicians for diagnosis. We developed an artificial intelligence (AI) model based on a deep neural network with 12,222 cases of 99mTc-MDP bone scintigraphy and evaluated its diagnostic performance of bone metastasis. This AI model demonstrated considerable diagnostic performance, the areas under the curve (AUC) of receiver operating characteristic (ROC) was 0.988 for breast cancer, 0.955 for prostate cancer, 0.957 for lung cancer, and 0.971 for other cancers. Applying this AI model to a new dataset of 400 BS cases, it represented comparable performance to that of human physicians individually classifying bone metastasis. Further AI-consulted interpretation also improved human diagnostic sensitivity and accuracy. In total, this AI model performed a valuable benefit for nuclear medicine physicians in timely and accurate evaluation of cancer bone metastasis.

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Section: Discussionmentioning

confidence: 99%

Deep neural network based artificial intelligence assisted diagnosis of bone scintigraphy for cancer bone metastasis

Zhen

Jiang

et al. 2020

Sci Rep

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“…Commercially available software, such as EXINI Bone BSI , has become an established modality in both clinical settings and clinical trials for the objective and quantitative assessment of bone involvement in prostate cancer patients [ 4 – 8 ]. The clinical value of lesional data, in particular the BSI, has been investigated in many trials in different clinical scenarios, including in the prognosis of prostate cancer [ 11 – 15 ] and in the assessment of the treatment response to anti-cancer treatments [ 11 , 16 , 25 ]. To the best of our knowledge, no previous reports have focused on the diagnostic role of the BSI in the staging of newly diagnosed prostate cancer.…”

Section: Discussionmentioning

confidence: 99%

“…The bone scan index (BSI), the cumulated tumour load of malignant lesions in the entire skeleton are the most used endpoints with this software, which has been assessed in a number of clinical scenarios. The BSI has been shown to carry solid prognostic value for estimating the outcome and survival in metastatic, hormone-naïve and castration-resistant prostate cancer [ 9 – 15 ] and to be valuable in the assessment of treatment response to anti-cancer treatments [ 11 , 16 , 17 ]. The diagnostic value of the BSI for the staging of newly diagnosed prostate cancer has not been assessed so far.…”

Section: Introductionmentioning

confidence: 99%

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

2017

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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.

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“…Recently, the BSI, using a computer-aided diagnosis system for bone scans, has been expected to be an objective and quantitative clinical tool for evaluation of bone metastatic prostate cancer. The BSI is useful for prediction of survival in men with prostate cancer in various situations, such as hormone-naive cancer or CRPC [ 11 , 13 , 14 , 25 – 29 ]. In this study, we measured BSI using BONENAVI version 2, which is a computer-assisted diagnosis system for bone scanning.…”

Section: Discussionmentioning

confidence: 99%

Prognostic value of the bone scan index using a computer-aided diagnosis system for bone scans in hormone-naive prostate cancer patients with bone metastases

et al. 2016

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BackgroundThe bone scan index (BSI) using a computer-aided diagnosis system for bone scans is expected to be an objective and quantitative clinical tool for evaluating bone metastatic prostate cancer. This study aimed to evaluate the pretreatment BSI as a prognostic factor in hormone-naive prostate cancer patients with bone metastases.MethodsThe study included 60 patients with hormone-naive, bone metastatic prostate cancer that was initially treated with combined androgen blockade therapy. The BONENAVI system was used for calculating the BSI. We evaluated the correlation between overall survival (OS) and pretreatment clinicopathological characteristics, including patients’ age, initial prostate-specific antigen (PSA) value, Gleason scores, clinical TNM stage, and the BSI. Cox proportional hazards regression models were used for statistical analysis.ResultsThe median follow-up duration was 21.4 months. Clinical or PSA progression occurred in 37 (61.7 %) patients and 18 (30.0 %) received docetaxel. Death occurred in 16 (26.7 %) patients. Of these deaths, 15 (25.0 %) were due to prostate cancer. The median OS was not reached. In multivariate analysis, age and the BSI were independent prognostic factors for OS. We evaluated the discriminatory ability of our models, including or excluding BSI by quantifying the C-index. The BSI improved the C-index from 0.751 to 0.801 for OS. Median OS was not reached in patients with a BSI ≤1.9 and median OS was 34.8 months in patients with a BSI >1.9 (p = 0.039).ConclusionsThe pretreatment BSI and patients’ age are independent prognostic factors for patients with hormone-naive, bone metastatic prostate cancer.

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Analysis of regional bone scan index measurements for the survival of patients with prostate cancer

Cited by 11 publications

References 26 publications

Deep neural network based artificial intelligence assisted diagnosis of bone scintigraphy for cancer bone metastasis

Deep neural network based artificial intelligence assisted diagnosis of bone scintigraphy for cancer bone metastasis

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

Prognostic value of the bone scan index using a computer-aided diagnosis system for bone scans in hormone-naive prostate cancer patients with bone metastases

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