Evaluation of usage of bone scan index in assessment of metastatic prostate cancer

Introduction: Prostate cancer has been the leading type of cancer to affect male population, and as such, it is a subject to efforts to furthermore diagnostic tools already in existence as well as development of new ones which will Aid early diagnostic, treatments as well as a follow up procedures and clinical trials. Bone scan index is a useful and objective biomarker used as a valuable tool for determination as to precise bone involvement in advanced cases, as well as a tool to predict the outcome in prostate cancer patients in clinical trials.Methods: This paper is a non-experimental (qualitative) research, that is, a scientific review of the literature.Results: The results we analyzed in this paper were collected from published academic journals.Conclusion: As a new imaging biomarker, bone scan index has potential to predict therapeutic effects and survival of patients with prostate cancer. Using measurable diagnostic image parameters, the bone scan index is important for determining metastatic bone changes in prostate cancer patients.

Analysis of Bone Scans in Various Tumor Entities Using a Deep-Learning-Based Artificial Neural Network Algorithm—Evaluation of Diagnostic Performance

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.

Study of the Usefulness of Bone Scan Index Calculated From 99m-Technetium-Hydroxymethylene Diphosphonate (99mtc-HMDP) Bone Scintigraphy for Bone Metastases from Prostate Cancer Using Deep Learning Algorithms

Background: BSI calculated from bone scintigraphy using 99mtechnetium-methylene diphosphonate (99mTc-MDP) is used as a quantitative indicator of metastatic bone involvement in bone metastasis diagnosis, therapeutic effect assessment, and prognosis prediction. However, the BONE NAVI, which calculates BSI, only supports bone scintigraphy using 99mTc-MDP. Aims: We developed a method in collaboration with the Tokyo University of Agriculture and Technology to calculate bone scan index (BSI) employing deep learning algorithms with bone scintigraphy images using 99mtechnetiumhydroxymethylene diphosphonate (99mTc-HMDP). We used a convolutional neural network (CNN) enabling the simultaneous processing of anterior and posterior bone scintigraphy images named CNNapis. Objectives: The purpose of this study is to investigate the usefulness of the BSI calculated by CNNapis as bone imaging and bone metabolic biomarkers in patients with bone metastases from prostate cancer. Methods: At our hospital, 121 bone scintigraphy scans using 99mTc-HMDP were performed and analyzed to examine bone metastases from prostate cancer, revealing the abnormal accumulation of radioisotope (RI) at bone metastasis sites. Blood tests for serum prostate-specific antigen (PSA) and alkaline phosphatase (ALP) were performed concurrently. BSI values calculated by CNNapis were used to quantify the metastatic bone tumor involvement. Correlations between BSI and PSA and between BSI and ALP were calculated. Subjects were divided into four groups by BSI values (Group 1, 0 to <1; Group 2, 1 to <3; Group 3, 3 to <10; Group 4, >10), and the PSA and ALP values in each group were statistically compared. Results: Patients diagnosed with bone metastases after bone scintigraphy were also diagnosed with bone metastases using CNNapis. BSI corresponding to the range of abnormal RI accumulation was calculated. PSA and BSI (r = 0.2791) and ALP and BSI (r = 0.6814) correlated positively. Significant intergroup differences in PSA between Groups 1 and 2, Groups 1 and 4, Groups 2 and 3, and Groups 3 and 4 and in ALP between Groups 1 and 4, Groups 2 and 4, and Groups 3 and 4 were found. Conclusion : BSI calculated using CNNapis correlated with ALP and PSA values and is useful as bone imaging and bone metabolic biomarkers, indicative of the activity and spread of bone metastases from prostate cancer.

A novel artificial intelligence biomarker: Validation of the automated bone scan index (aBSI) in veterans with metastatic prostate cancer.

e17507 Background: Prostate cancer commonly metastasizes to the bone and is associated with reduced survival, pathologic fractures and bone pain. The assessment of bone lesions is made with the technetium Tc99m(99mTc) bone scan, which relies on the subjective interpretation of radiologists and has a wide interobserver variability. There is an unmet need for a more objective and quantifiable measurement tool. Progenics Pharmaceuticals has introduced an automated bone scan index (aBSI), which employs artificial intelligence to quantify skeletal tumor burden. The automated bone scan index has been prospectively validated and is reproducible in large Phase III studies. The aBSI was validated by our study in the Veteran population at the West LA VA Medical Center. Methods: The first positive technetium 99 Tc99m bone scans of veterans diagnosed with metastatic, castration-sensitive prostate cancer were evaluated. Since 2011, a total of 107 evaluable patient bone scans were studied (n = 107). Patients with visceral metastases were excluded to evaluate only those with skeletal metastases. An automated bone scan index (aBSI) was generated for each scan using the Progenics Pharmaceuticals’ artificial intelligence platform. Multivariate analysis of aBSI with overall survival, prostate cancer specific survival, time from diagnosis to first positive bone scan, age at diagnosis, ethnicity, and Gleason score was assessed. Results: The study demonstrated a wide range of aBSI values (Range 0-16.84). Values calculated above the Median aBSI value (1.0) were prognostic for Overall Survival (p = 0.0009) and Prostate Cancer-Specific Survival (p = 0.0011). Patients in the highest quartile of aBSI values (range 5.2-16.84) showed a statistically significant Prostate Cancer-Specific Mortality (p = 0.0300) when compared to the lowest two quartiles (Range 0-1.07). The time from diagnosis to the first positive Tc99m bone scan statistically correlated with aBSI values (p = 0.0016). Multivariate analysis using Cox regression was utilized in the final statistical analysis of prostate cancer-specific mortality and overall survival. Conclusions: The automated Bone Scan Index provides a quantifiable and validated artificial intelligence biomarker to address an unmet need among metastatic prostate cancer patients. This tool was validated among Veterans, a pertinent population that is commonly affected by metastatic prostate cancer.

Bone scan index on bone scintigraphy and radiation therapy for bone metastases from cancers other than prostate and breast cancers: a retrospective observational study

Background A low bone scan index that is associated with a better prognosis in patients with bone metastases from prostate or breast cancer, the former often being osteolytic, has been established. In this study we aimed to use new automatic analysis software (VSBONE BSI; Nihon Medi-Physics, Tokyo, Japan) to investigate whether the pre-radiation therapy bone scan index, derived from bone scintigraphy images, is a prognostic indicator in patients undergoing radiation therapy for bone metastases from cancers other than breast or prostate cancer. Methods In this retrospective single institution study, we analyzed data of 51 patients who had undergone whole-body scintigraphy before receiving radiation therapy for bone metastases from cancers other than breast and prostate cancer between 2013 and 2019. Their bone metastases were classified as osteoblastic, osteolytic, or mixed and their pre-radiation bone scan indexes were automatically calculated using newly developed software (VSBONE BSI; Nihon Medi-Physics, Tokyo, Japan). Univariate and multivariate analyses were performed to identify associations between selected clinical variables and overall survival. Results We did not find a significant association between BSI and overall survival, possibly because osteolytic lesions may be underestimated by bone scan indexes. However, we did find that younger patients (aged less than the median of 66 years at the time of bone scintigraphy or of diagnosis of bone metastases) had significantly better overall survivals than older patients (P = 0.016 and P = 0.036, respectively). Additionally, bone scan indexes were significantly lower in patient with solitary or osteolytic bone metastases than in those with osteoblastic or mixed bone metastases (P = 0.035 and P = <0.001, respectively), and significantly higher in those with lung cancer than in those with other types of cancer (mean BSI 3.26% vs. 1.97%; P = 0.009). Conclusions The only significant association with survival identified in this study was for age at the time of bone scintigraphy and at time of diagnosis of bone metastases. In particular, we found no association between bone scan index and survival in the whole study cohort.