Prognosis of patients with prostate cancer and bone metastasis from the Japanese Prostatic Cancer Registry of Standard Hormonal and Chemotherapy Using Bone Scan Index cohort study

Nakajima, Kénichi; Mizokami, Atsushi; Matsuyama, Hideyasu; Ichikawa, Tomohiko; Kaneko, Go; Takahashi, Satoru; Shiina, Hìroaki; Horikoshi, Hiroyuki; Hashine, Katsuyoshi; Sugiyama, Yutaka; Miyao, Takeshi; Kamiyama, Manabu; Harada, Kiyoshi; Ito, Akito; Investigators, Prostat-Bsi

doi:10.1111/iju.14614

Cited by 14 publications

(14 citation statements)

References 29 publications

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“…After each metastatic hot spot is determined, the BSI is calculated as the sum of all such fractions using software (EXINI bone in Europe and North America; BONENAVI in Japan) [ 39 ]. The BSI has shown clinical impact as a prognostic biomarker in patients with prostate cancer [ 40 , 41 ]. It was reported that the BSI more appropriately reflected treatment response than visual interpretation [ 42 ].…”

Section: Discussionmentioning

confidence: 99%

Reliability of Alkaline Phosphatase for Differentiating Flare Phenomenon from Disease Progression with Bone Scintigraphy

Jung

Hong

et al. 2022

Cancers

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The flare phenomenon (FP) on bone scintigraphy after the initiation of systemic treatment seriously complicates evaluations of therapeutic response in patients with bone metastases. The aim of this study was to evaluate whether serum alkaline phosphatase (ALP) can differentiate FP from disease progression on bone scintigraphy in these patients. Breast or prostate cancer patients with bone metastases who newly underwent systemic therapy were reviewed. Pretreatment baseline and follow-up data, including age, pathologic factors, type of systemic therapy, radiologic and bone scintigraphy findings, and ALP levels, were obtained. Univariate and multivariate analyses of these factors were performed to predict FP. An increased extent and/or new lesions were found in 160 patients on follow-up bone scintigraphy after therapy. Among the 160 patients, 80 (50%) had an improvement on subsequent bone scintigraphy (BS), while subsequent scintigraphy also showed an increased uptake in 80 (50%, progression). Multiple regression analysis revealed that stable or decreased ALP was an independent predictor for FP (p < 0.0001). ALP was an independent predictor for FP on subgroup analysis for breast and prostate cancer (p = 0.001 and p = 0.0223, respectively). Results of the study suggest that ALP is a useful serologic marker to differentiate FP from disease progression on bone scintigraphy in patients with bone metastasis. Clinical interpretation for scintigraphic aggravation can be further improved by the ALP data and it may prevent fruitless changes of therapeutic modality by misdiagnosis of disease progression in cases of FP.

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Section: Discussionmentioning

confidence: 99%

Reliability of Alkaline Phosphatase for Differentiating Flare Phenomenon from Disease Progression with Bone Scintigraphy

Jung

Hong

et al. 2022

Cancers

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“…Although BSI provided a relatively objective estimation of metastatic burden of PCa, especially with the assistance of computer-aided diagnostic system, it was essentially an indirect indicator reflecting disease aggressiveness [33,34]. Further analysis were required to set up thresholds for risk stratification of prognosis [35]. Besides, the calculation of BSI did not consider the different effects among metastasis sites, since metastases to appendicular skeleton, such as ribs and limbs, had been reported to be significantly correlated with poorer response to androgen deprivation therapy and shorter survival time [30].…”

Section: Discussionmentioning

confidence: 99%

Automatic Prediction Model of Overall Survivalin Prostate Cancer Patients with BoneMetastasis using Deep Neural Networks

Wang

Xiong

Jiang

et al. 2022

Preprint

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Background: Bone was the most common site of metastasis in prostate cancer(PCa) patients and was correlated with poor prognosis and increasing economicalburden. Studies were limited on the prognostic prediction for metastatic PCapatients with the assistance of neural network. Methods: Four convolutional neural network (CNN) models were developed andevaluated to predict overall survival (OS) of PCa patients with bone metastasis.All the CNN models were first trained with 64 samples and evaluated with 10samples, two models used only bone scan images and two models used both bonescan images and clinical parameters (CPs). Predictions of the best models werecompared with those of two urology surgeons on 20 test samples. Results: Our best models could predict OS of PCa patients with bone metastasiswith AUC = 0.8022 by using only bone scan images and AUC = 0.8132 by usingboth bone scan image and CPs on 20 test samples. When the sensitivities(specificities) set equal to average level of urology surgeons, their specificities(sensitivities) were 0%(7.2%) and 30.77%(7.7%) higher, which showedsignificant advantages of CNN models. Conclusion: The CNN models were suitable to predict OS in PCa patients withbone metastasis using bone scan images and CPs. Our models showed betterperformance in terms of accuracy and stability than urology surgeons. Keywords: Bone Metastasis; Bone Scan; Convolutional Neural Network;Prostate Cancer; Overall Survival

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“…We previously performed a multicenter study (30 hospitals in Japan) to identify prognostic factors, including the bone scan index (BSI), in patients with mHSPC ( n = 148) and mCRPC ( n = 99), and the findings obtained revealed that BSI was one of the significant prognostic factors for 3‐year OS (PROSTAT‐BSI study, Figure 1). 13 Of note, each attending physician determined the diagnosis of CRPC and the course of treatment. The patients were enrolled from 2012 to 2015.…”

Section: Methodsmentioning

confidence: 99%

Predictive factors for the effectiveness of novel androgen receptor axis‐targeted agents in patients with metastatic prostate cancer

et al. 2022

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Objective Novel androgen receptor axis‐targeted agents (ARATAs) have been developed for mCRPC and improved overall survival (OS). Here, we aimed to find predictors who will receive the greatest benefits from ARATAs. Methods We previously performed a multicenter study to identify prognostic factors for metastatic hormone‐sensitive prostate cancer (mHSPC, n = 148) and mCRPC (n = 99), and showed that the bone scan index (BSI) was one of the significant prognostic factors for 3‐year OS (PROSTAT‐BSI study). mHSPC progressed to mCRPC (n = 101), for which 69 patients were treated with (n = 39) or without ARATAs (n = 30, prior to the approval of ARATAs). The 69 patients were divided into two groups according to patient factors, and these cohorts were further divided into two subgroups by usage of ARATAs. OS was compared between subgroups in each group. Results The predictors were age (<71.4 years), serum levels of C‐reactive protein (≥0.16 ng/ml) and alkaline phosphatase (≥548 U/L), time to PSA progression after ADT (<8.9 months), the lowest PSA level (≥1 ng/ml) after ADT, and the rate of PSA decline 3 months after ADT (<0.987), whereas hemoglobin levels, PSA before ADT, Gleason scores, existence of visceral metastases, and BSI were not. Conclusions The present study identified predictors for the effectiveness of ARATAs. The number of bone metastases (≒BSI), existence of visceral metastases, and Gleason scores, which were identified as high‐risk factors in the LATITUDE study and disease volume in CHAARTED criteria, did not appear to be useful for predicting effectiveness from ARATAs.

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Prognosis of patients with prostate cancer and bone metastasis from the Japanese Prostatic Cancer Registry of Standard Hormonal and Chemotherapy Using Bone Scan Index cohort study

Cited by 14 publications

References 29 publications

Reliability of Alkaline Phosphatase for Differentiating Flare Phenomenon from Disease Progression with Bone Scintigraphy

Reliability of Alkaline Phosphatase for Differentiating Flare Phenomenon from Disease Progression with Bone Scintigraphy

Automatic Prediction Model of Overall Survivalin Prostate Cancer Patients with BoneMetastasis using Deep Neural Networks

Predictive factors for the effectiveness of novel androgen receptor axis‐targeted agents in patients with metastatic prostate cancer

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