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

Artificial Intelligence in Detection, Management, and Prognosis of Bone Metastasis: A Systematic Review

BACKGROUND

Metastasis commonly occur in the bone tissue. Artificial intelligence (AI) has become increasingly prevalent in the medical sector as support in decision-making, diagnosis, and treatment processes. The objective of this systematic review was to assess the reliability of AI systems in clinical, radiological, and pathological aspects of bone metastases.

METHODS

We included studies that evaluated the use of AI applications in patients affected by bone metastases. Two reviewers performed a digital search on 31 December 2023 on PubMed, Scopus, and Cochrane library and extracted authors, AI method, interest area, main modalities used, and main objectives from the included studies.

RESULTS

We included 59 studies that analyzed the contribution of computational intelligence in diagnosing or forecasting outcomes in patients with bone metastasis. Six studies were specific for spine metastasis. The study involved nuclear medicine (44.1%), clinical research (28.8%), radiology (20.4%), or molecular biology (6.8%). When a primary tumor was reported, prostate cancer was the most common, followed by lung, breast, and kidney.

CONCLUSIONS

Appropriately trained AI models may be very useful in merging information to achieve an overall improved diagnostic accuracy and treatment for metastasis in the bone. Nevertheless, there are still concerns with the use of AI systems in medical settings. Ethical considerations and legal issues must be addressed to facilitate the safe and regulated adoption of AI technologies. The limitations of the study comprise a stronger emphasis on early detection rather than tumor management and prognosis as well as a high heterogeneity for type of tumor, AI technology and radiological techniques, pathology, or laboratory samples involved.

Bone Metastasis in Prostate Cancer: Bone Scan Versus PET Imaging

Prostate cancer is the second most common cause of malignancy among men, with bone metastasis being a significant source of morbidity and mortality in advanced cases. Detecting and treating bone metastasis at an early stage is crucial to improve the quality of life and survival of prostate cancer patients. This objective strongly relies on imaging studies. While CT and MRI have their specific utilities, they also possess certain drawbacks. Bone scintigraphy, although cost-effective and widely available, presents high false-positive rates. The emergence of PET/CT and PET/MRI, with their ability to overcome the limitations of standard imaging methods, offers promising alternatives for the detection of bone metastasis. Various radiotracers targeting cell division activity or cancer-specific membrane proteins, as well as bone seeking agents, have been developed and tested. The use of positron-emitting isotopes such as fluorine-18 and gallium-68 for labeling allows for a reduced radiation dose and unaffected biological properties. Furthermore, the integration of artificial intelligence (AI) and radiomics techniques in medical imaging has shown significant advancements in reducing interobserver variability, improving accuracy, and saving time. This article provides an overview of the advantages and limitations of bone scan using SPECT and SPECT/CT and PET imaging methods with different radiopharmaceuticals and highlights recent developments in hybrid scanners, AI, and radiomics for the identification of prostate cancer bone metastasis using molecular imaging.

Comparison of skeletal segmentation by deep learning-based and atlas-based segmentation in prostate cancer patients

OBJECTIVE

We aimed to compare the deep learning-based (VSBONE BSI) and atlas-based (BONENAVI) segmentation accuracy that have been developed to measure the bone scan index based on skeletal segmentation.

METHODS

We retrospectively conducted bone scans for 383 patients with prostate cancer. These patients were divided into two groups: 208 patients were injected with ^99mTc-hydroxymethylene diphosphonate processed by VSBONE BSI, and 175 patients were injected with ^99mTc-methylene diphosphonate processed by BONENAVI. Three observers classified the skeletal segmentations as either a "Match" or "Mismatch" in the following regions: the skull, cervical vertebrae, thoracic vertebrae, lumbar vertebrae, pelvis, sacrum, humerus, rib, sternum, clavicle, scapula, and femur. Segmentation error was defined if two or more observers selected "Mismatch" in the same region. We calculated the segmentation error rate according to each administration group and evaluated the presence of hot spots suspected bone metastases in "Mismatch" regions. Multivariate logistic regression analysis was used to determine the association between segmentation error and variables like age, uptake time, total counts, extent of disease, and gamma cameras.

RESULTS

The regions of "Mismatch" were more common in the long tube bones for VSBONE BSI and in the pelvis and axial skeletons for BONENAVI. Segmentation error was observed in 49 cases (23.6%) with VSBONE BSI and 58 cases (33.1%) with BONENAVI. VSBONE BSI tended that "Mismatch" regions contained hot spots suspected of bone metastases in patients with multiple bone metastases and showed that patients with higher extent of disease (odds ratio = 8.34) were associated with segmentation error in multivariate logistic regression analysis.

CONCLUSIONS

VSBONE BSI has a potential to be higher segmentation accuracy compared with BONENAVI. However, the segmentation error in VSBONE BSI occurred dependent on bone metastases burden. We need to be careful when evaluating multiple bone metastases using VSBONE BSI.

Predicting the Prognosis of Prostate Cancer Bone Metastasis Using the Bone Scan Index and Hot Spots Calculated Using VSBONE^Ⓡ Bone Scan Index from Tc-99m-Hydroxymethylene Diphosphonate Bone Scintigraphy

<b><i>Introduction:</i></b> The bone scan index (BSI) is widely used as a quantitative indicator of bone metastasis, therapeutic effect assessment, and prognosis prediction in prostate cancer. However, the BONE NAVI, which calculates BSI, only supports bone scintigraphy using Tc-99m-methylene diphosphonate. We developed the VSBONE^Ⓡ BSI, which calculates BSI from bone scintigraphy using Tc-99m-hydroxymethylene diphosphonate (HMDP). The purpose of this study was to demonstrate that the BSI calculated using VSBONE^Ⓡ BSI and hot spots (HS), which indicates the number of abnormal accumulations, are useful prognostic factors for patients with prostate cancer bone metastasis, similar to BONE NAVI. <b><i>Methods:</i></b> We analyzed 322 patients who underwent bone scintigraphy for prostate cancer bone metastasis at our hospital. Initial bone scintigraphy was performed using Tc-99m-HMDP. All cases were retrospectively examined for their outcome and time to the final outcome. The results obtained were compared with the BSI and HS calculated using VSBONE^Ⓡ BSI. <b><i>Results:</i></b> When the patients were divided into two groups, HS &#x3e;2 and HS ≤2, the HS ≤2 group had a significantly longer survival time (<i>p</i> &#x3c; 0.001). In addition, when divided into two groups, BSI &#x3e;0.46 and BSI ≤0.46, the survival time of the BSI ≦0.46 group was significantly longer (<i>p</i> &#x3c; 0.001). <b><i>Conclusion:</i></b> BSI and HS obtained using VSBONE^Ⓡ BSI may be useful as prognostic predictors, similar to those obtained using BONE NAVI.

Deep learning-based detection of parathyroid adenoma by 99mTc-MIBI scintigraphy in patients with primary hyperparathyroidism

OBJECTIVE

It is important to detect parathyroid adenomas by parathyroid scintigraphy with 99m-technetium sestamibi (^99mTc-MIBI) before surgery. This study aimed to develop and validate deep learning (DL)-based models to detect parathyroid adenoma in patients with primary hyperparathyroidism, from parathyroid scintigrams with ^99mTc-MIBI.

METHODS

DL-based models for detecting parathyroid adenoma in early- and late-phase parathyroid scintigrams were, respectively, developed and evaluated. The training dataset used to train the models was collected from 192 patients (165 adenoma cases, mean age: 64 years ± 13, 145 women) and the validation dataset used to tune the models was collected from 45 patients (30 adenoma cases, mean age: 67 years ± 12, 37 women). The images were collected from patients who were pathologically diagnosed with parathyroid adenomas or in whom no lesions could be detected by either parathyroid scintigraphy or ultrasonography at our institution from June 2010 to March 2019. The models were tested on a dataset collected from 44 patients (30 adenoma cases, mean age: 67 years ± 12, 38 women) who took scintigraphy from April 2019 to March 2020. The models' lesion-based sensitivity and mean false positive indications per image (mFPI) were assessed with the test dataset.

RESULTS

The sensitivity was 82% [95% confidence interval 72-92%] with mFPI of 0.44 for the scintigrams of the early-phase model and 83% [73-92%] with mFPI of 0.31 for the scintigrams of the delayed-phase model in the test dataset, respectively.

CONCLUSIONS

The DL-based models were able to detect parathyroid adenomas with a high sensitivity using parathyroid scintigraphy with 99m-technetium sestamibi.

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

Simple Summary

Bone scintigraphy is the most widely used radionuclide technique to investigate bone metastasis, primarily due to its reasonable time and cost factor. However, it is important to recognize that bone scintigraphy to assess treatment response sometimes shows a “flare phenomenon”, which can be misinterpreted as disease progression. Distinction between flare phenomenon and disease progression could help in the decision to continue effective treatments in patients with flare phenomenon and to cease ineffective treatments and consider other salvage treatment plans in patients with disease progression. Despite many methods having been tried to answer this question, there was still no reliable way to differentiate between flare phenomenon and progression of bone metastases. Our results suggest that ALP is a useful serologic marker to differentiate flare phenomenon from disease progression on bone scintigraphy in breast or prostate cancer patients with bone metastasis.

Abstract

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.