Automated Bone Scan Index as an Imaging Biomarker to Predict Overall Survival in the Zometa European Study/SPCG11

Reza, Mariana; Wirth, Manfred P.; Tammela, Teuvo L.J.; Cicalese, V.; Veiga, F. Gómez; Mulders, Peter F.A.; Miller, Kurt; Tubaro, Andrea; Debruyne, F.M.J.; Patel, Anup; Caris, Christien; Witjes, Wim P.J.; Thorsson, Ola; Wollmer, Per; Edenbrandt, Lars; Ohlsson, Mattias; Trägårdh, Elin; Bjartell, Anders

doi:10.1016/j.euo.2019.05.002

Cited by 11 publications

(7 citation statements)

References 21 publications

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“…Baseline characteristics of the eligible studies are presented in Table 1 (more details are provided in Tables S1-S4). Finally, 15 eligible study (12,(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34) syntheses, which were published between 2010 and 2019, comprised data for a total of 11,378 patients, whose mean age ranged from 66 to 72 years. Most studies were from America and Europe.…”

Section: Study Selection and Characteristicsmentioning

confidence: 99%

“…All enrolled studies were considered to be of high quality; the summary of risk of bias assessment is presented in Figure S1. Two of the studies (30,34) retrospectively analyzed their own previous randomized controlled trials and we recognized that these two studies were of good quality according to the original article and the trial register numbers they published.…”

Section: Study Selection and Characteristicsmentioning

confidence: 99%

“…Three studies (26,33,34) described that the BSI progression had an association with inferior OS (HR = 1.18, 95% CI: 1.08-1.29 P < 0.001), with significant heterogeneity (I 2 = 52.7%, P heterogeneity = 0.121). Forest plots ( Figure 3B) show the pooled result.…”

Section: Prognostic Value Of Bsi For Os In Mcrpcmentioning

confidence: 99%

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Revealing Prognostic Value of Skeletal-Related Parameters in Metastatic Castration-Resistant Prostate Cancer on Overall Survival: A Systematic Review and Meta-Analysis of Randomized Controlled Trial

et al. 2020

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Background: The skeleton is a preferred site for prostate cancer metastasis, and once metastases occur, the disease becomes incurable. Increasing evidence indicates the prognostic value of skeletal-related parameters, but remains controversial. Objective: To perform a systematic review of the existing literature on assessing the prognostic value of alkaline phosphatase (ALP), bone-specific alkaline phosphatase (BSAP), urinary N-telopeptide (uNTx), bone scan index (BSI), and Brief Pain Inventory Short Form (BPI-SF) score in castration-resistant prostate cancer (CRPC) patients with skeleton metastasis.

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Section: Study Selection and Characteristicsmentioning

confidence: 99%

Section: Study Selection and Characteristicsmentioning

confidence: 99%

See 1 more Smart Citation

Revealing Prognostic Value of Skeletal-Related Parameters in Metastatic Castration-Resistant Prostate Cancer on Overall Survival: A Systematic Review and Meta-Analysis of Randomized Controlled Trial

et al. 2020

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“…Artificial intelligence (AI) is being developed to assist the observer by highlighting suspicious uptake in diagnostic images 6 . Earlier studies have showed that AI can be trained to find suspicious bone involvement and provide the results quantitatively and objectively, rather than in a subjective way 7 . Other studies show that AI can be of benefit by decreasing inter-observer variations among physicians and increasing the sensitivity in finding lesions 8 .…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence could alert for focal skeleton/bone marrow uptake in Hodgkin’s lymphoma patients staged with FDG-PET/CT

Sadik

López-Urdaneta

Ulén³

et al. 2021

Sci Rep

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To develop an artificial intelligence (AI)-based method for the detection of focal skeleton/bone marrow uptake (BMU) in patients with Hodgkin’s lymphoma (HL) undergoing staging with FDG-PET/CT. The results of the AI in a separate test group were compared to the interpretations of independent physicians. The skeleton and bone marrow were segmented using a convolutional neural network. The training of AI was based on 153 un-treated patients. Bone uptake significantly higher than the mean BMU was marked as abnormal, and an index, based on the total squared abnormal uptake, was computed to identify the focal uptake. Patients with an index above a predefined threshold were interpreted as having focal uptake. As the test group, 48 un-treated patients who had undergone a staging FDG-PET/CT between 2017–2018 with biopsy-proven HL were retrospectively included. Ten physicians classified the 48 cases regarding focal skeleton/BMU. The majority of the physicians agreed with the AI in 39/48 cases (81%) regarding focal skeleton/bone marrow involvement. Inter-observer agreement between the physicians was moderate, Kappa 0.51 (range 0.25–0.80). An AI-based method can be developed to highlight suspicious focal skeleton/BMU in HL patients staged with FDG-PET/CT. Inter-observer agreement regarding focal BMU is moderate among nuclear medicine physicians.

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“…Arti cial intelligence (AI) is being developed to assist the observer by highlighting suspicious uptake in diagnostic images [6]. Earlier studies have showed that AI can be trained to nd suspicious bone involvement and provide the results quantitatively and objectively, rather than in a subjective way [7]. Other studies show that AI can be of bene t by decreasing inter-observer variations among physicians and increasing the sensitivity in nding lesions [8].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Could Alert for Focal Skeleton/Bone Marrow Uptake in Hodgkin´s Lymphoma Patients Staged With FDG-PET/CT

Sadik

López-Urdaneta

Ulén³

et al. 2021

Preprint

Self Cite

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Purpose: To develop an artificial intelligence (AI)-based method for the detection of focal skeleton/bone marrow uptake (BMU) in patients with Hodgkin´s lymphoma (HL) undergoing staging with FDG-PET/CT. The results of the AI in a separate test group were compared to the interpretations of independent physicians. Methods: The skeleton and bone marrow were segmented using a convolutional neural network. The training of AI was based on 153 un-treated patients. Bone uptake significantly higher than the mean BMU was marked as abnormal, and an index, based on the total squared abnormal uptake, was computed to identify the focal uptake. Patients with an index above a predefined threshold were interpreted as having focal uptake. As the test group, 48 un-treated patients who had undergone a staging FDG-PET/CT between 2017-2018 with biopsy-proven HL were retrospectively included. Ten physicians classified the 48 cases regarding focal skeleton/BMU. Results: The majority of the physicians agreed with the AI in 39/48 cases (81%) regarding focal skeleton/bone marrow involvement. Inter-observer agreement between the physicians was moderate, Kappa 0.51 (range 0.25-0.80). Conclusion: An AI-based method can be developed to highlight suspicious focal skeleton/BMU in HL patients staged with FDG-PET/CT. Inter-observer agreement regarding focal BMU is moderate among nuclear medicine physicians.

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Automated Bone Scan Index as an Imaging Biomarker to Predict Overall Survival in the Zometa European Study/SPCG11

Cited by 11 publications

References 21 publications

Revealing Prognostic Value of Skeletal-Related Parameters in Metastatic Castration-Resistant Prostate Cancer on Overall Survival: A Systematic Review and Meta-Analysis of Randomized Controlled Trial

Revealing Prognostic Value of Skeletal-Related Parameters in Metastatic Castration-Resistant Prostate Cancer on Overall Survival: A Systematic Review and Meta-Analysis of Randomized Controlled Trial

Artificial intelligence could alert for focal skeleton/bone marrow uptake in Hodgkin’s lymphoma patients staged with FDG-PET/CT

Artificial Intelligence Could Alert for Focal Skeleton/Bone Marrow Uptake in Hodgkin´s Lymphoma Patients Staged With FDG-PET/CT

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