ArticleCancers2020
Analysis of Bone Scans in Various Tumor Entities Using a Deep-Learning-Based Artificial Neural Network Algorithm-Evaluation of Diagnostic Performance.
Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 33 citations in OpenAlex.
- Artificial Intelligence in bone Metastases: A systematic review in guideline adherence of 92 studies.Journal of bone oncology · 2025Review
- Clinical performance of deep learning-enhanced ultrafast whole-body scintigraphy in patients with suspected malignancy.BMC medical imaging · 2024Article
- Stability of standardized uptake values for quantitative bone SPECT for jawbone lesions: a single-center cross-sectional study.BMC oral health · 2024Article
- Four Markers Useful for the Distinction of Intrauterine Growth Restriction in Sheep.Animals : an open access journal from MDPI · 2023Article
- Developing neural network diagnostic models and potential drugs based on novel identified immune-related biomarkers for celiac disease.Human genomics · 2023Article
- Skeleton Segmentation on Bone Scintigraphy for BSI Computation.Diagnostics (Basel, Switzerland) · 2023Article
- Clinical Significance of the Highest Regional Bone Scan Index in Patients with Metastatic Castration-Resistant Prostate Cancer.Nuclear medicine and molecular imaging · 2022Article
- Application of SPECT and PET / CT with computer-aided diagnosis in bone metastasis of prostate cancer: a review.Cancer imaging : the official publication of the International Cancer Imaging Society · 2022Review
- Detection of Bone Metastases on Bone Scans through Image Classification with Contrastive Learning.Journal of personalized medicine · 2021Article
- Progress in diagnosis of bone metastasis of prostate cancer.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2021Article
- Status quo and future prospects of artificial neural network from the perspective of gastroenterologists.World journal of gastroenterology · 2021Review
- Lesion-Based Bone Metastasis Detection in Chest Bone Scintigraphy Images of Prostate Cancer Patients Using Pre-Train, Negative Mining, and Deep Learning.Diagnostics (Basel, Switzerland) · 2021Article
- Detection and Segmentation of Pelvic Bones Metastases in MRI Images for Patients With Prostate Cancer Based on Deep Learning.Frontiers in oncology · 2021Article
- Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies.Frontiers in oncology · 2021Review
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The bone scan index (BSI), initially introduced for metastatic prostate cancer, quantifies the osseous tumor load from planar bone scans. Following the basic idea of radiomics, this method incorporates specific deep-learning techniques (artificial neural network) in its development to provide automatic calculation, feature extraction, and diagnostic support. As its performance in tumor entities, not including prostate cancer, remains unclear, our aim was to obtain more data about this aspect. The results of BSI evaluation of bone scans from 951 consecutive patients with different tumors were retrospectively compared to clinical reports (bone metastases, yes/no). Statistical analysis included entity-specific receiver operating characteristics to determine optimized BSI cut-off values. In addition to prostate cancer (cut-off = 0.27%, sensitivity (SN) = 87%, specificity (SP) = 99%), the algorithm used provided comparable results for breast cancer (cut-off 0.18%, SN = 83%, SP = 87%) and colorectal cancer (cut-off = 0.10%, SN = 100%, SP = 90%). Worse performance was observed for lung cancer (cut-off = 0.06%, SN = 63%, SP = 70%) and renal cell carcinoma (cut-off = 0.30%, SN = 75%, SP = 84%). The algorithm did not perform satisfactorily in melanoma (SN = 60%). For most entities, a high negative predictive value (NPV ≥ 87.5%, melanoma 80%) was determined, whereas positive predictive value (PPV) was clinically not applicable. Automatically determined BSI showed good sensitivity and specificity in prostate cancer and various other entities. Particularly, the high NPV encourages applying BSI as a tool for computer-aided diagnostic in various tumor entities.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.