Enhanced diagnostic accuracy for quantitative bone scan using an artificial neural network system: a Japanese multi-center database project

Artificial intelligence in skeletal metastasis imaging

In the field of metastatic skeletal oncology imaging, the role of artificial intelligence (AI) is becoming more prominent. Bone metastasis typically indicates the terminal stage of various malignant neoplasms. Once identified, it necessitates a comprehensive revision of the initial treatment regime, and palliative care is often the only resort. Given the gravity of the condition, the diagnosis of bone metastasis should be approached with utmost caution. AI techniques are being evaluated for their efficacy in a range of tasks within medical imaging, including object detection, disease classification, region segmentation, and prognosis prediction in medical imaging. These methods offer a standardized solution to the frequently subjective challenge of image interpretation.This subjectivity is most desirable in bone metastasis imaging. This review describes the basic imaging modalities of bone metastasis imaging, along with the recent developments and current applications of AI in the respective imaging studies. These concrete examples emphasize the importance of using computer-aided systems in the clinical setting. The review culminates with an examination of the current limitations and prospects of AI in the realm of bone metastasis imaging. To establish the credibility of AI in this domain, further research efforts are required to enhance the reproducibility and attain robust level of empirical support.

Flare phenomenon visualized by 99mTc-bone scintigraphy has prognostic value for patients with metastatic castration-resistant prostate cancer

OBJECTIVE

This study aimed to determine the prognostic value of the flare phenomenon in patients with metastatic castration-resistant prostate cancer (mCRPC) using the bone scan index (BSI) derived from 99mTc-methylenediphosphonate (MDP) bone scintigraphy images.

METHODS

We categorized 72 patients from the PROSTAT-BSI registry with mCRPC who were followed-up for 2 years after starting docetaxel chemotherapy to groups based on pre-chemotherapy BSI values of < 1, 1-4, and > 4. We assessed the effects of the flare phenomenon (defined as a > 10% increase in the BSI within 3 months of starting chemotherapy, followed by > 10% improvement within the next 3 months) on survival using Kaplan-Meier curves and Cox proportional hazard analyses.

RESULTS

The flare phenomenon was found in 26 (36%) of the 72 patients. Prostate-specific antigen (PSA), alkaline phosphatase (ALP), and hemoglobin (Hb) levels steadily increased, then deteriorated in patients with and without flare, respectively. Elevated BSI and PSA values at 3 months after starting therapy and the absence of abiraterone or/and enzalutamide therapy led to poor 2-year overall survival (OS) in the group without flare. In contrast, no influence was noticeable in the group with flare. The results of multivariable analyses that included only factors associated with PSA and BSI showed that increased baseline BSI (hazard ratio [HR], 1.39; 95% confidence interval [CI], 1.04-1.86; P = 0.023) and PSA (HR, 7.15; 95% CI 2.13-24.04; P = 0.0015) values could be independent risk factors for patients with mCRPC without flare. However, these factors lost significance during flare. The risk for all-cause death was significantly higher among patients with BSI > 4 without, than with flare. The results of univariable analyses indicated that flare positively impacted survival (HR, 0.24; 95% CI 0.06‒0.91; P = 0.035). Multivariable analysis did not identify any factors that could predict outcomes.

CONCLUSION

Favorable prognosis, with fewer disturbances from other factors such as the use of abiraterone or/and enzalutamide, PSA changes, and BSI, was attainable in cases when the mCRPC patient demonstrated flare phenomenon. Follow-up bone scintigraphy at least every 3 months could help to determine the prognosis of patients with bone metastasis of mCRPC.

Bone metastasis detection method based on improving golden jackal optimization using whale optimization algorithm

This paper presents a machine learning-based technique for interpreting bone scintigraphy images, focusing on feature extraction and introducing a new feature selection method called GJOW. GJOW enhances the effectiveness of the golden jackal optimization (GJO) algorithm by integrating operators from the whale optimization algorithm (WOA). The technique's performance is evaluated through extensive experiments using 18 benchmark datasets and 581 bone scan images obtained from a gamma camera, including 362 abnormal and 219 normal cases. The results highlight the superior predictive effectiveness of the GJOW algorithm in bone metastasis detection, achieving an accuracy of 71.79% and specificity of 91.14%. The contributions of this study include the introduction of a new machine learning-based approach for detecting bone metastasis using gamma camera scans, leading to improved accuracy in identifying bone metastases. The findings have practical implications for early detection and intervention, potentially improving patient outcomes.

Bone Health Management in the Continuum of Prostate Cancer Disease

Prostate cancer (PCa) is the second-leading cause of cancer-related deaths in men. PCa cells require androgen receptor (AR) signaling for their growth and survival. Androgen deprivation therapy (ADT) is the preferred treatment for patients with locally advanced and metastatic PCa disease. Despite their initial response to androgen blockade, most patients eventually will develop metastatic castration-resistant prostate cancer (mCRPC). Bone metastases are common in men with mCRPC, occurring in 30% of patients within 2 years of castration resistance and in >90% of patients over the course of the disease. Patients with mCRPC-induced bone metastasis develop lesions throughout their skeleton; the 5-year survival rate for these patients is 47%. Bone-metastasis-induced early changes in the bone that proceed the osteoblastic response in the bone matrix are monitored and detected via modern magnetic resonance and PET/CT imaging technologies. Various treatment options, such as targeting osteolytic metastasis with bisphosphonates, prednisone, dexamethasone, denosumab, immunotherapy, external beam radiation therapy, radiopharmaceuticals, surgery, and pain medications are employed to treat prostate-cancer-induced bone metastasis and manage bone health. However, these diagnostics and treatment options are not very accurate nor efficient enough to treat bone metastases and manage bone health. In this review, we present the pathogenesis of PCa-induced bone metastasis, its deleterious impacts on vital organs, the impact of metastatic PCa on bone health, treatment interventions for bone metastasis and management of bone- and skeletal-related events, and possible current and future therapeutic options for bone management in the continuum of prostate cancer disease.

New index to assess the extent of bone disease in patients with prostate cancer using SPECT/CT

OBJECTIVE

Assessing the extent of bone metastases in patients with prostate cancer is very important to predict patient prognosis. Therefore, the bone scan index (BSI), which is easy to use, has been used; however, the accuracy is not that high. In this study, we proposed a new index for the extent of bone disease using single-photon emission computed tomography with computed tomography (SPECT/CT) images and assessed the accuracy of calculation.

METHODS

In this study, a total of 46 bone scans from 12 patients with prostate cancer treated for bone metastases with Radium-223 were included. Whole-body planar images were obtained 150-180 min after an intravenous injection of ^99mTc-methylene diphosphonate, and cervical-to-pelvic SPECT/CT was immediately obtained. The total bone volume (TBV) and regional metabolic bone volume (MBV) were defined as Hounsfield unit of > 120, standardized uptake value (SUV) of > 0.5, and SUV of > 5-8 in four levels, respectively. Bone metabolism volumetric index (BMVI) was calculated as the percentage of the total MBV divided by TBV. The variability of the TBV measurement was evaluated by the percentage coefficient of variance (%CV) of TBV within individual patients. We evaluated the correlation of TBV with age, height, weight, and body mass index and the correlation and agreement between BSI and BMVI.

RESULTS

The mean and %CV of TBV were 4661.7 cm³ and 2.8%, respectively, and TBV was strongly correlated with body weight. BMVI was significantly higher than BSI and correlated with alkaline phosphatase. For patients with progressive bone metastases, BSI was clearly underestimated, whereas BMVI was elevated.

CONCLUSIONS

Although assessed in a small number of cases, the new index for assessing the extent of bone disease using SPECT/CT imaging was highly value than BSI and was significantly correlated with alkaline phosphatase. Therefore, this study suggests that BMVI could improve the low sensitivity of BSI in patients with low extent of disease grade.

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.

Application of SPECT and PET / CT with computer-aided diagnosis in bone metastasis of prostate cancer: a review

Bone metastasis has a significant influence on the prognosis of prostate cancer(PCa) patients. In this review, we discussed the current application of PCa bone metastasis diagnosis with single-photon emission computed tomography (SPECT) and positron emission tomography/computed tomography (PET/CT) computer-aided diagnosis(CAD) systems. A literature search identified articles concentrated on PCa bone metastasis and PET/CT or SPECT CAD systems using the PubMed database. We summarized the previous studies focused on CAD systems and manual quantitative markers calculation, and the coincidence rate was acceptable. We also analyzed the quantification methods, advantages, and disadvantages of CAD systems. CAD systems can detect abnormal lesions of PCa patients’ 99mTc-MDP-SPECT, 18F-FDG-PET/CT, 18F-NaF-PET/CT, and 68 Ga-PSMA PET/CT images automated or semi-automated. CAD systems can also calculate the quantitative markers, which can quantify PCa patients’ whole-body bone metastasis tumor burden accurately and quickly and give a standardized and objective result. SPECT and PET/CT CAD systems are potential tools to monitor and quantify bone metastasis lesions of PCa patients simply and accurately, the future clinical application of CAD systems in diagnosing PCa bone metastasis lesions is necessary and feasible.

Novel diagnostic model for bone metastases in renal cell carcinoma patients based on bone scintigraphy analyzed by computer-aided diagnosis software and bone turnover markers

Background

Computer-assisted diagnosis (CAD) systems for bone scans have been introduced as clinical quality assurance tools, but few studies have reported on its utility for renal cell carcinoma (RCC) patients. The aim of this study was to assess the diagnostic validity of the CAD system for bone scans and to construct a novel diagnostic system for bone metastases in RCC patients.

Methods

We evaluated bone scan images of 300 RCC patients. Artificial neural network (ANN) values, which represent the probability of abnormality, were calculated by BONENAVI, the CAD software for bone scans. By analyzing ANN values, we assessed the diagnostic validity of BONENAVI. Next, we selected 108 patients who underwent measurements of bone turnover markers and assessed the combined diagnostic validity of BONENAVI and bone turnover markers.

Results

Forty-three out of 300 RCC patients had bone metastases. The AUC of ANN values was 0.764 and the optimum sensitivity and specificity were 83.7 and 62.7%. By logistic analysis of 108 cases, we found that ICTP, a bone resorption marker, could be a diagnostic marker. The AUC of ICTP was 0.776 and the optimum sensitivity and specificity were 57.1 and 86.8%. Subsequently, we developed a novel diagnostic model based on ANN values and ICTP. Using this model, the AUC was 0.849 and the optimum sensitivity and specificity were 76.2 and 80.7%.

Conclusion

By combining the high sensitivity provided by BONENAVI and the high specificity provided by ICTP, we constructed a novel, high-accuracy diagnostic model for bone metastases in RCC patients.

Whole Skeletal Mean SUV Measured on 18F-NaF PET/CT Studies as a Prognostic Indicator in Patients with Bone Metastatic Breast Cancer

In this work we assessed the association between the whole skeletal mean standardized uptake value (SUV) measured on 18F-NaF PET/CT studies and the overall survival (OS) of bone metastatic breast cancer patients. Methods: We retrospectively analyzed 176 patients with breast cancer and bone metastatic disease who performed 18F-NaF PET/CT studies. The outcomes of the patients (dead or alive) were established based on the last information available on their files. The mean and maximum SUVs were measured in a whole skeletal volume of interest (wsVOI). The wsVOI was defined based on the CT component of the PET/CT study using Hounsfield Units thresholds. The wsVOI was then applied on the 18F-NaF PET image. Univariate analyses were performed to assess the association of the SUVs with OS. We also analyzed the association of the age of the patients, the presence of visceral metastatic disease, histological subtypes, presence of hormone receptors, human epidermal growth factor receptor 2 expression and the creatinine, CA15-3 and alkaline phosphatase (ALP) levels with OS. The variables statistically significant in the univariate analyses were included in a multivariate cox regression survival analysis. Results: In the univariate analyses there were associations of the mean and maximum whole skeletal SUVs, estrogen receptor status and the CA15-3 and ALP levels with OS. In the multivariate analysis, all the variables that were statistically significant in the univariate analysis but the CA15-3 were associated with OS. Conclusion: In patients with bone metastatic breast cancer, the whole skeletal mean SUV is an independent predictor of overall survival.

Extraction of metastasis hotspots in a whole-body bone scintigram based on bilateral asymmetry

PURPOSE

A hotspot of bone metastatic lesion in a whole-body bone scintigram is often observed as left-right asymmetry. The purpose of this study is to present a network to evaluate bilateral difference of a whole-body bone scintigram, and to subsequently integrate it with our previous network that extracts the hotspot from a pair of anterior and posterior images.

METHODS

Input of the proposed network is a pair of scintigrams that are the original one and the flipped version with respect to body axis. The paired scintigrams are processed by a butterfly-type network (BtrflyNet). Subsequently, the output of the network is combined with the output of another BtrflyNet for a pair of anterior and posterior scintigrams by employing a convolutional layer optimized using training images.

RESULTS

We evaluated the performance of the combined networks, which comprised two BtrflyNets followed by a convolutional layer for integration, in terms of accuracy of hotspot extraction using 1330 bone scintigrams of 665 patients with prostate cancer. A threefold cross-validation experiment showed that the number of false positive regions was reduced from 4.30 to 2.13 for anterior and 4.71 to 2.62 for posterior scintigrams on average compared with our previous model.

CONCLUSIONS

This study presented a network for hotspot extraction of bone metastatic lesion that evaluates bilateral difference of a whole-body bone scintigram. When combining the network with the previous network that extracts the hotspot from a pair of anterior and posterior scintigrams, the false positives were reduced by nearly half compared to our previous model.

Diagnostic performance of deep learning models for detecting bone metastasis on whole-body bone scan in prostate cancer

PURPOSE

We evaluated the performance of deep learning classifiers for bone scans of prostate cancer patients.

METHODS

A total of 9113 consecutive bone scans (5342 prostate cancer patients) were initially evaluated. Bone scans were labeled as positive/negative for bone metastasis using clinical reports and image review for ground truth diagnosis. Two different 2D convolutional neural network (CNN) architectures were proposed: (1) whole body-based (WB) and (2) tandem architectures integrating whole body and local patches, here named as "global-local unified emphasis" (GLUE). Both models were trained using abundant (72%:8%:20% for training:validation:test sets) and limited training data (10%:40%:50%). The allocation of test sets was rotated across all images: therefore, fivefold and twofold cross-validation test results were available for abundant and limited settings, respectively.

RESULTS

A total of 2991 positive and 6142 negative bone scans were used as input. For the abundant training setting, the receiver operating characteristics curves of both the GLUE and WB models indicated excellent diagnostic ability in terms of the area under the curve (GLUE: 0.936-0.955, WB: 0.933-0.957, P > 0.05 in four of the fivefold tests). The overall accuracies of the GLUE and WB models were 0.900 and 0.889, respectively. With the limited training setting, the GLUE models showed significantly higher AUCs than the WB models (0.894-0.908 vs. 0.870-0.877, P < 0.0001).

CONCLUSION

Our 2D-CNN models accurately classified bone scans of prostate cancer patients. While both showed excellent performance with the abundant dataset, the GLUE model showed higher performance than the WB model in the limited data setting.

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

Objective

To determine prognostic factors including the Bone Scan Index in prostate cancer patients receiving standard hormonal therapy and chemotherapy.

Methods

This multicenter Prostatic Cancer Registry of Standard Hormonal and Chemotherapy Using Bone Scan Index study involved 30 hospitals and enrolled 247 patients (age 71 ± 8 years) with metastatic hormone‐sensitive prostate cancer (n = 148) under hormone therapy and metastatic castration‐resistant prostate cancer (n = 99) under chemotherapy. The Bone Scan Index (%) was determined by whole‐body bone scintigraphy using ^99mTc‐methylenediphosphonate. Patients were classified into tertiles and binary groups, and predictors of all‐cause death including Bone Scan Index, prostate‐specific antigen, and bone metabolic markers were determined using survival and proportional hazard analyses.

Results

During a mean follow‐up period of 716 ± 404 days, 81 (33%) of the patients died, and 3‐year mortality rates were 20% and 52% in the metastatic hormone‐sensitive prostate cancer and metastatic castration‐resistant prostate cancer groups, respectively. Survival analysis showed that a Bone Scan Index >3.5% was a significant determinant of death in the metastatic hormone‐sensitive prostate cancer group, whereas prostate‐specific antigen >55 ng/mL before chemotherapy was a determinant of prognosis in the metastatic castration‐resistant prostate cancer group. A Bone Scan Index >3.5% was also associated with a high incidence of prostate‐specific antigen progression in the metastatic hormone‐sensitive prostate cancer group. Patients with metastatic hormone‐sensitive prostate cancer and a better Bone Scan Index response (>45%) to treatment had lower mortality rates than those without such response.

Conclusion

The Bone Scan Index and hot spot number are significant determinants of 3‐year mortality, and combining the Bone Scan Index with prostate‐specific antigen should contribute to the management of prostate cancer patients with bone metastasis.

Object-oriented classification approach for bone metastasis mapping from whole-body bone scintigraphy

PURPOSE

Whole-body bone scintigraphy is the most widely used method for detecting bone metastases in advanced cancer. However, its interpretation depends on the experience of the radiologist. Some automatic interpretation systems have been developed in order to improve diagnostic accuracy. These systems are pixel-based and do not use spatial or textural information of groups of pixels, which could be very important for classifying images with better accuracy. This paper presents a fast method of object-oriented classification that facilitates easier interpretation of bone scintigraphy images.

METHODS

Nine whole-body images from patients suspected with bone metastases were analyzed in this preliminary study. First, an edge-based segmentation algorithm together with the full lambda-schedule algorithm were used to identify the object in the bone scintigraphy and the textural and spatial attributes of these objects were calculated. Then, a set of objects (224 objects, ~ 46% of the total objects) were selected as training data based on visual examination of the image, and were assigned to various levels of radionuclide accumulation before performing the data classification using both k-nearest-neighbor and support vector machine classifiers. The performance of the proposed method was evaluated using as metric the statistical parameters calculated from error matrix.

RESULTS

The results revealed that the proposed object-oriented classification approach using either k-nearest-neighbor or support vector machine as classification methods performed well in detecting bone metastasis in terms of overall accuracy (86.62 ± 2.163% and 86.81 ± 2.137% respectively) and kappa coefficient (0.6395 ± 0.0143 and 0.6481 ± 0.0218 respectively).

CONCLUSIONS

In conclusion, the described method provided encouraging results in mapping bone metastases in whole-body bone scintigraphy.

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 ^99mtechnetium-hydroxymethylene 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.

Diagnostic software proposal for bone scan follow-up using false color based on the gammagrams analysis from gray tone histograms

In this work we introduce a technique to speed up the interpretation of bone scans with the aim of determining the presence of absence of metastatic disease. We use gray tone histograms resembling the use of pass band filters, in order to ensure a reliable interpretation of the bone scan, providing an accurate diagnosis. We draw particular attention to three cases. The first case corresponds to shifted histograms. If the histogram is shifted toward the origin, the bone scan is free of metastasis. If it is shifted to the right and slightly broadened, indicates the presence of a bone scan anomaly different than metastasis. On the other hand, if the histogram is broadened and shifted to the left, is suggests the presence of metastatic disease. The second case corresponds to a histogram with noticeable fluctuations, indicating the presence of metastasis. Such fluctuations could become local maxima peaks indicating the advance of the metastasis. The third case, corresponds to the false color results displayed in terms of the gray tones observed in the histogram. Such false color is assigned from the construction of a 7-color palette selected in terms of the gray tones range, easing the ad hoc false color assignation for visualization purposes. The final diagnosis is carried out in terms of the color, geometry, extension and location of the region of interest in the images. Our proposed technique has the potential to be used in high-demand oncology centers due to its simplicity and diagnostic efficiency, confirmed and tested by specialists in the Centro Medico Siglo XXI (CDMX-Mexico).

Do various imaging modalities provide potential early detection and diagnosis of medication-related osteonecrosis of the jaw? A review

OBJECTIVE

Patients with medication-related osteonecrosis of the jaw (MRONJ) often visit their dentists at advanced stages and subsequently require treatments that greatly affect quality of life. Currently, no clear diagnostic criteria exist to assess MRONJ, and the definitive diagnosis solely relies on clinical bone exposure. This ambiguity leads to a diagnostic delay, complications, and unnecessary burden. This article aims to identify imaging modalities' usage and findings of MRONJ to provide possible approaches for early detection.

METHODS

Literature searches were conducted using PubMed, Web of Science, Scopus, and Cochrane Library to review all diagnostic imaging modalities for MRONJ.

RESULTS

Panoramic radiography offers a fundamental understanding of the lesions. Imaging findings were comparable between non-exposed and exposed MRONJ, showing osteolysis, osteosclerosis, and thickened lamina dura. Mandibular cortex index Class II could be a potential early MRONJ indicator. While three-dimensional modalities, CT and CBCT, were able to show more features unique to MRONJ such as a solid type periosteal reaction, buccal predominance of cortical perforation, and bone-within-bone appearance. MRI signal intensities of vital bones are hypointense on T₁WI and hyperintense on T₂WI and STIR when necrotic bone shows hypointensity on all T₁WI, T₂WI, and STIR. Functional imaging is the most sensitive method but is usually performed in metastasis detection rather than being a diagnostic tool for early MRONJ.

CONCLUSION

Currently, MRONJ-specific imaging features cannot be firmly established. However, the current data are valuable as it may lead to a more efficient diagnostic procedure along with a more suitable selection of imaging modalities.

Assessment of a software for semi-automatically calculating the bone scan index on bone scintigraphy scans

AIMS

The incidence of bone metastases exceeds 85% in patients who die from prostate cancer. Bone scintigraphy is the most widely used method for the early detection of bone metastases in prostate cancer. We developed a software program that semi-automatically calculated the bone scan index (BSI) on technetium-99m hydroxymethylene diphosphonate bone scintigraphy scans with a computer-aided diagnosis system (CAD) and examined whether the BSI calculated using this software could replace the extent of disease (EOD) score.

METHODS

The subjects were 175 patients who were diagnosed with prostate cancer at our hospital and underwent bone scintigraphy. We analyzed receiver operating characteristic curves to determine the BSI cutoff value between EOD groups. The cutoff value was determined based on the maximum value of the sum of the sensitivity and specificity.

RESULTS

BSI cutoff values of 0.20, 1.56, and 4.56 were used to distinguish between EOD 0 and 1-4 (sensitivity [87.2%] and specificity [100.0%]), between EOD 0- and 2-4 (sensitivity [85.2%] and specificity [92.0%]), and between EOD 0-2 and 3-4 (sensitivity [88.4%] and specificity [99.2%]), respectively.

CONCLUSION

Our results suggest that this software can calculate BSI, and the software may play a role in predicting prognosis and selecting an appropriate treatment strategy. If a sufficient number of other nuclear medicine tests are performed, creating a similar CAD system is possible.

Deep neural network based artificial intelligence assisted diagnosis of bone scintigraphy for cancer bone metastasis

Bone scintigraphy (BS) is one of the most frequently utilized diagnostic techniques in detecting cancer bone metastasis, and it occupies an enormous workload for nuclear medicine physicians. So, we aimed to architecture an automatic image interpreting system to assist physicians for diagnosis. We developed an artificial intelligence (AI) model based on a deep neural network with 12,222 cases of ^99mTc-MDP bone scintigraphy and evaluated its diagnostic performance of bone metastasis. This AI model demonstrated considerable diagnostic performance, the areas under the curve (AUC) of receiver operating characteristic (ROC) was 0.988 for breast cancer, 0.955 for prostate cancer, 0.957 for lung cancer, and 0.971 for other cancers. Applying this AI model to a new dataset of 400 BS cases, it represented comparable performance to that of human physicians individually classifying bone metastasis. Further AI-consulted interpretation also improved human diagnostic sensitivity and accuracy. In total, this AI model performed a valuable benefit for nuclear medicine physicians in timely and accurate evaluation of cancer bone metastasis.

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.

Comparison of diagnostic precision for bone metastasis of primary breast cancer between BONENAVI version 1 and BONENAVI version 2

OBJECTIVE

The aim of this study was to compare the diagnostic precision of bone scintigraphy with BONENAVI version 1 and BONENAVI version 2 in bone metastasis of primary breast cancer.

METHODS

The subjects were 56 consecutive patients (all women, mean age 59 ± 12.7 years) who underwent bone scintigraphy with Tc-MDP and were diagnosed with bone metastasis of primary breast cancer from January 2012 to November 2014. Bone scintigraphy was performed with BONENAVI version 1 and BONENAVI version 2 to calculate artificial neural network (ANN), bone scan index (BSI), and hot-spot values, with ANN ≥ 0.5 considered to reflect bone metastasis for the calculation of sensitivity. Mean ANN, BSI, hot-spot values, and sensitivity were compared between BONENAVI version 1 and BONENAVI version 2, with P < 0.05 considered a significant difference.

RESULTS

With BONENAVI version 1, mean ANN was 0.73 ± 0.29, BSI was 1.47 ± 1.85, the hot-spot value was 12.4 ± 12.5, and sensitivity was 76.8% (43/56). With BONENAVI version 2, the mean ANN was 0.86 ± 0.19, BSI was 1.53 ± 2.09, hot-spot value was 12.9 ± 15.6, and sensitivity was 94.6% (53/56). BONENAVI version 2 yielded significantly better ANN and sensitivity than BONENAVI version 1 (both P < 0.01).

CONCLUSION

BONENAVI version 2 has improved sensitivity for detecting bone metastasis of primary breast cancer compared to BONENAVI version 1.

Diagnostic performance of a computer-assisted diagnostic system: sensitivity of BONENAVI for bone scintigraphy in patients with disseminated skeletal metastasis is not so high

Purpose

Bone scintigraphy (BS) of disseminated skeletal metastasis is sometimes misinterpreted as normal. The use of computer-assisted diagnosis (CAD) may resolve this problem. We investigated the performance of a CAD system, BONENAVI, in the diagnosis of disseminated skeletal metastasis.

Methods

Cases of disseminated skeletal metastasis were selected from a BS log. These patients’ BSs were analyzed by BONENAVI to obtain an artificial neural network (ANN) and bone scan index (BSI). Clinical features (type of primary cancer, CT type, and BS type) were compared with the BONENAVI (ANN and BSI) results. The BS findings (diffuse increased axial skeleton uptake, inhomogeneity of uptake, proximal extremity contrast, and degree of renal uptake) and ANN or BSI were evaluated. Then, negative ANN patients were presented.

Results

Fifty-four patients were diagnosed as having disseminated skeletal metastasis. Regarding the primary cancers, 12 had prostate cancer, 16 gastric cancers, 16 breast cancers, and 10 miscellaneous cancers. Total sensitivity of ANN (≥ 0.5) was 76% (41/54). ANN values correlated with the BS type among clinical features. Diffuse increased axial skeleton uptake was mostly correlated with ANN of the BS findings.

Conclusion

The BONENAVI CAD system was partially helpful in diagnosing disseminated skeletal metastasis, but the sensitivity of BONENAVI was not sufficient and underestimated the disseminated skeletal metastasis. Further improvement of this CAD system is necessary to improve the detectability of disseminated skeletal metastasis.

Automated measurement of bone scan index from a whole-body bone scintigram

Purpose

We propose a deep learning-based image interpretation system for skeleton segmentation and extraction of hot spots of bone metastatic lesion from a whole-body bone scintigram followed by automated measurement of a bone scan index (BSI), which will be clinically useful.

Methods

The proposed system employs butterfly-type networks (BtrflyNets) for skeleton segmentation and extraction of hot spots of bone metastatic lesions, in which a pair of anterior and posterior images are processed simultaneously. BSI is then measured using the segmented bones and extracted hot spots. To further improve the networks, deep supervision (DSV) and residual learning technologies were introduced.

Results

We evaluated the performance of the proposed system using 246 bone scintigrams of prostate cancer in terms of accuracy of skeleton segmentation, hot spot extraction, and BSI measurement, as well as computational cost. In a threefold cross-validation experiment, the best performance was achieved by BtrflyNet with DSV for skeleton segmentation and BtrflyNet with residual blocks. The cross-correlation between the measured and true BSI was 0.9337, and the computational time for a case was 112.0 s.

Conclusion

We proposed a deep learning-based BSI measurement system for a whole-body bone scintigram and proved its effectiveness by threefold cross-validation study using 246 whole-body bone scintigrams. The automatically measured BSI and computational time for a case are deemed clinically acceptable and reliable.

Computer-aided detection of bone metastasis in bone scintigraphy images using parallelepiped classification method

OBJECTIVE

Accurate diagnosis of metastatic tissue on bone scintigraphy images is of paramount importance in making treatment decisions. Although several automated systems have developed, more and better interpretation methods are still being sought. In the present study, a new modality for bone metastasis detection from bone scintigraphy images using parallelepiped classification (PC) as method for mapping the radionuclide distribution is presented.

METHODS

Bone scintigraphy images from 12 patients with bone metastases were analyzed using the parallelepiped classifier that generated color maps of scintigraphic images. Seven classes of radionuclide accumulation have been identified and fed into machine learning software. The accuracy of the proposed method was evaluated by statistical measurements in a confusion matrix. Overall accuracy, producer's and user's accuracies and κ coefficient were computed from each confusion matrix associated with the individual case.

RESULTS

The results revealed that the method is sufficiently precise to differentiate the metastatic bone from normal tissue (overall classification accuracy = 87.58 ± 2.25% and κ coefficient = 0.8367 ± 0.0252). The maps are easier to read (due to better contrast) and can detect even slightest differences in accumulation levels among pixels.

CONCLUSIONS

In conclusion, these preliminary data suggest that bone scintigraphy combined with PC method could play an important role in the detection of bone metastasis, allowing for an easier but correct interpretation of the images, with effects on the diagnosis accuracy and decision making on the treatment to be applied.

Fully automated analysis for bone scintigraphy with artificial neural network: usefulness of bone scan index (BSI) in breast cancer

OBJECTIVE

Artificial neural network (ANN) technology has been developed for clinical use to analyze bone scintigraphy with metastatic bone tumors. It has been reported to improve diagnostic accuracy and reproducibility especially in cases of prostate cancer. The aim of this study was to evaluate the diagnostic usefulness of quantitative bone scintigraphy with ANN in patients having breast cancer.

PATIENTS AND METHODS

We retrospectively evaluated 88 patients having breast cancer who underwent both bone scintigraphy and ¹⁸F-fluorodeoxyglucose (FDG) positron-emission computed tomography/X-ray computed tomography (PET/CT) within an interval of 8 weeks between both examinations for comparison. The whole-body bone images were analyzed with fully automated software that was customized according to a Japanese multicenter database. The region of interest for FDG-PET was set to bone lesions in patients with bone metastasis, while the bone marrow of the ilium and the vertebra was used in patients without bone metastasis.

RESULTS

Thirty of 88 patients had bone metastasis. Extent of disease, bone scan index (BSI) which indicate severity of bone metastasis, the maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and serum tumor markers in patients with bone metastasis were significantly higher than those in patients without metastasis. The Kaplan-Meier survival curve showed that the overall survival of the lower BSI group was longer than that with the higher BSI group in patients with visceral metastasis. In the multivariate Cox proportional hazard model, BSI (hazard ratio (HR): 19.15, p = 0.0077) and SUVmax (HR: 10.12, p = 0.0068) were prognostic factors in patients without visceral metastasis, while the BSI was only a prognostic factor in patients with visceral metastasis (HR: 7.88, p = 0.0084), when dividing the sample into two groups with each mean value in patients with bone metastasis.

CONCLUSION

BSI, an easily and automatically calculated parameter, was a well prognostic factor in patients with visceral metastasis as well as without visceral metastasis from breast cancer.

Bone scan index can be a useful biomarker of survival outcomes in patients with metastatic castration-resistant prostate cancer treated with radium-223

BACKGROUND

Bone metastasis of prostate cancer is associated with pain and reduced overall survival (OS). Radium-223, which is expected to reduce bone pain and prolong OS, was recently approved in Japan.

AIM

The aim of this study was to investigate the usefulness of the bone scan index by comparing the outcomes and factors according to response in Japanese patients treated with radium-223.

METHODS AND RESULTS

Twenty patients receiving radium-223 were divided into two groups according to whether they did or did not achieve a bone scan index decrease of at least one point (beneficial and non-beneficial groups, respectively). The clinical characteristics at baseline and after three and six treatment cycles were compared using χ² tests and Student's t-tests or Mann-Whitney U tests, and survival was estimated and compared using the Kaplan-Meier method and log-rank test, respectively. Fourteen (70%) and six patients (30%) were categorized into the beneficial and non-beneficial groups, respectively. Patients in the former group were significantly more likely to have a higher Eastern Cooperative Oncology Group performance status score and receive a greater number of radium-223 injections (P < 0.05). Furthermore, patients in the beneficial group had a significantly longer OS (P < 0.05). Regarding safety, one and three patients in the beneficial and non-beneficial groups, respectively, prematurely discontinued radium-223 because of an increased prostate-specific antigen level, decreased hemoglobin level, or femoral fracture.

CONCLUSION

Radium-223 appears generally safe in this population. Patients with good bone scan index response have better performance status, receive more injections of radium-223, and achieve OS prolongation. Bone scan index is a useful biomarker of survival outcomes and can be a valuable assessment tool in patients with metastatic castration-resistant prostate cancer who are treated with radium-223.

A novel biomarker, active whole skeletal total lesion glycolysis (WS-TLG), as a quantitative method to measure bone metastatic activity in breast cancer patients

Objective

There is no good response evaluation method for skeletal metastasis. We aimed to develop a novel quantitative method to evaluate the response of skeletal metastasis, especially lytic lesions, for treatment.

Methods

A method to measure active bone metastatic burden quantitatively using F-18 fluorodeoxyglucose positron emission tomography with computed tomography (FDG–PET/CT) in breast cancer patients, whole skeletal total lesion glycolysis (WS-TLG), a summation of each skeletal lesion’s TLG, was developed. To identify active bone lesions, a tentative cutoff value was decided using FDG–PET/CT in 85 breast cancer patients without skeletal metastasis and 35 with skeletal metastasis by changing the cutoff value. Then, the WS-TLG method was evaluated by comparing to PET Response Criteria in Solid Tumor (PERCIST) or European Organization for Research and Treatment of Cancer (EORTC) criteria for only bone in 15 breast cancer patients with skeletal metastasis who were treated.

Results

A cutoff value of the standardized uptake value (SUV) = 4.0 gave 91% (77/85) specificity and 97% (34/35) sensitivity. We decided on SUV = 4.0 as a tentative cutoff value. Skeletal metastases of lytic and mixed types showed higher WS-TLG values than those of blastic or intertrabecular types, although statistical significance was not tested. All 15 patients showed agreement with PERCIST or EORTC in the therapeutic bone response.

Conclusion

This quantitative WS-TLG method appears to be a good biomarker to evaluate skeletal metastasis in breast cancer patients, especially lytic or mixed types. Further clinical studies are warranted to assess the clinical values of this new WS-TLG method.

☆Symposium: Imaging modalities for drug-related osteonecrosis of the jaw (5), utility of bone scintigraphy and 18F-FDG PET/CT in early detection and risk assessment of medication-related osteonecrosis of the jaw (secondary publication)

Medication-related osteonecrosis of the jaw (MRONJ) is a significant side effect of antiresorptive and antiangiogenic drugs. Since MRONJ is intractable, early detection is the best way to limit progression. Bone scintigraphy and ¹⁸F- fluorodeoxyglucose positron-emission tomography can detect minimal and subclinical changes in bones earlier than conventional radiological modalities. A differential diagnosis including MRONJ is recommended when abnormally high uptakes are incidentally detected in the jaws of patients who have bone metastases. Quantitative analysis of uptakes, such as bone scan index of the jaw using neural network analysis and maximum standardized uptake value, could differentiate MRONJ from common dental diseases and be useful for the early detection and risk assessment of MRONJ.

Fluorine-18-fluorocholine PET/CT parameters predictive for hematological toxicity to radium-223 therapy in castrate-resistant prostate cancer patients with bone metastases: a pilot study

PURPOSE

This study aims to predict hematological toxicity induced by Ra therapy. We investigated the value of metabolically active bone tumor volume (MBTV) and total bone lesion activity (TLA) calculated on pretreatment fluorine-18-fluorocholine (F-FCH) PET/CT in castrate-resistant prostate cancer (CRPC) patients with bone metastases treated with Ra radionuclide therapy.

PATIENTS AND METHODS

F-FCH PET/CT imaging was performed in 15 patients with CRPC before treatment with Ra. Bone metastatic disease was quantified on the basis of the maximum standardized uptake value (SUV), total lesion activity (TLA=MBTV×SUVmean), or MBTV/height (MBTV/H) and TLA/H. F-FCH PET/CT bone tumor burden and activity were analyzed to identify which parameters could predict hematological toxicity [on hemoglobin (Hb), platelets (PLTs), and lymphocytes] while on Ra therapy. Pearson's correlation was used to identify the correlations between age, prostate-specific antigen, and F-FCH PET parameters.

RESULTS

MBTV ranged from 75 to 1259 cm (median: 392 cm). TLA ranged from 342 to 7198 cm (median: 1853 cm). Patients benefited from two to six cycles of Ra (n=56 cycles in total). At the end of Ra therapy, five of the 15 (33%) patients presented grade 2/3 toxicity on Hb and lymphocytes, whereas three of the 15 (20%) patients presented grade 2/3 PLT toxicity.Age was correlated negatively with both MBTV (r=-0.612, P=0.015) and TLA (r=-0.596, P=0.018). TLA, TLA/H, and MBTV/H predicted hematological toxicity on Hb, whereas TLA/H and MBTV/H predicted toxicity on PLTs at the end of Ra cycles. Receiver operating characteristic curve analysis allowed to define the cutoffs for MBTV (915 cm) and TLA (4198 cm) predictive for PLT toxicity, with an accuracy of 0.92 and 0.99.

CONCLUSION

Tumor bone burden calculation is feasible with F-FCH PET/CT with freely available open-source software. In this pilot study, baseline F-FCH PET/CT markers (TLA, MBTV) have shown abilities to predict Hb and PLT toxicity after Ra therapy and could be explored for patient selection and treatment optimization.

Evaluation of bone metastatic burden by bone SPECT/CT in metastatic prostate cancer patients: defining threshold value for total bone uptake and assessment in radium-223 treated patients

Objectives

To establish a new three-dimensional quantitative evaluation method for bone metastasis, we applied bone single photon emission tomography with computed tomography (SPECT/CT). The total bone uptake (TBU), which measures active bone metastatic burden, was calculated as the sum of [mean uptake obtained as standardized uptake value (SUV) above a cut-off level] × (the volume of the lesion) in the trunk using bone SPECT/CT. We studied the threshold value and utility of TBU in prostate cancer patients treated with radium-223 (Ra-223) therapy.

Methods

To establish the threshold value of TBU, we compared bone metastatic and non-metastatic regions in 61 prostate cancer patients with bone metastasis and 69 without. Five fixed sites in each patient were selected as evaluation points and divided into bone metastatic and non-metastatic sites. Sensitivity and specificity analysis was applied to establish the threshold level. Using the obtained threshold value, we then calculated the TBU in nine prostate cancer patients who received Ra-223 therapy, and compared the results with the bone scan index (BSI) by BONENAVI^® and visual evaluation of bone scintigraphy.

Results

Uptake was significantly lower in non-metastatic sites in patients with bone metastasis than in patients without metastasis. Sensitivity and specificity analysis revealed SUV = 7.0 as the threshold level. There was a discrepancy between TBU and BSI change in two of the nine patients, in whom TBU change correlated with visual judgement, but BSI change did not. In two patients, BSI was nearly 0 throughout the course, but the TBU was positive and changed, although the change was not large. These results suggest that TBU may be more accurate and sensitive than BSI for quantitative evaluation of active bone metastatic burden.

Conclusion

We established a threshold value (SUV > 7.0) for three-dimensional TBU for evaluating active bone metastatic burden in prostate cancer patients using bone SPECT/CT. Despite the small number of patients, we expect the change in TBU could be more accurate and sensitive than the change in BSI among patients who received Ra-223.

Skeletal standardized uptake values obtained by quantitative SPECT/CT as an osteoblastic biomarker for the discrimination of active bone metastasis in prostate cancer

Purpose

To investigate the improvement of prognostication of active bone metastatic burden by discriminating bone metastases from degenerative changes in hot foci, using skeletal standardized uptake values (SUVs) by quantitative bone single photon emission tomography/computed tomography (SPECT/CT) in patients with prostate cancer.

Methods

We investigated 170 patients with prostate cancer who underwent skeletal quantitative SPECT/CT using ^99mTc-methylene-diphosphonate (MDP), through conjugate gradient reconstruction with tissue zoning, attenuation, and scatter corrections applied, called as CGZAS reconstruction, in a retrospective cohort study. The maximum, peak, and average SUVs (SUVmax, SUVpeak, and SUVave, respectively) were obtained for visually normal thoracic (T; n = 100) and lumbar (L; n = 140) vertebral bodies as controls, as well as for bone metastases (n = 126) and degenerative changes (n = 114) as hot foci. They were also correlated with age, body-weight, height, biochemistry data, and extent of disease (EOD). Discrimination accuracy of the SUVs for bone metastases in hot foci was evaluated by a patient-based and lesion-based receiver-operator characteristic curve (ROC) analysis.

Results

The skeletal SUVmax was 7.58 ± 2.42 for T, 8.12 ± 12.24 for L, 16.73 ± 6.74 for degenerative changes, and 40.90 ± 33.46 for bone metastases. The SUVs of the bone metastasis group were significantly (p < 0.001) greater than of the other three groups. With disease extent, serum alkaline phosphatase and prostate specific antigen were increased, while SUVs for bone metastases were decreased in EOD grade 4. In ROC analyses for bone metastases by skeletal SUVs demonstrating the diagnostic accuracy of skeletal SUVs for discriminating bone metastasis from degenerative changes in hot foci, area under curves were 0.840, 0.817, and 0.845 in patient-based mode, and 0.932, 0.920, and 0.930 in lesion-based mode.

Conclusions

The skeletal SUVs by ^99mTc-MDP SPECT/CT for active bone metastases were greater than those for degenerative changes in patients with prostate cancer, with a feasible discrimination accuracy in the hot foci. Therefore, skeletal SUVs, especially SUVmax, in quantitative bone SPECT/CT may be helpful indices for the prognostication of bone metastatic burden, improving discrimination of active bone osteoblastic metastases in patients with prostate cancer from frequently coexisting degenerative changes.

Bone Scan Index Is an Independent Predictor of Time to Castration-resistant Prostate Cancer in Newly Diagnosed Prostate Cancer: A Prospective Study

OBJECTIVE

To prospectively determine the prognostic value of the bone scan index (BSI) for time to development of castration-resistant prostate cancer (CRPC) in consecutive, hormone-naïve patients with newly diagnosed prostate cancer.

PATIENTS AND METHODS

Eligible patients participated in a prospective, observational, multicenter study of the value of bone scintigraphy (BS) at staging. BSI was determined using the EXINI Bone^BSI software in 208 consecutive patients undergoing androgen deprivation therapy. The presence or absence of bone metastases at staging was classified by BS with or without supplementary imaging. Follow-up was performed >5 years after including the last patient.

RESULTS

During follow-up, 149 of the 208 patients (72%) were diagnosed with CRPC. Median time to CRPC was 20 months. Median follow-up time was 4.4 years in patients without CRPC. In univariate analyses, presence of bone metastases (M1) (hazard ratio [HR] 3.00, 95% confidence interval [CI] 2.10-4.30), Gleason grade (HR 1.53, 95% CI 1.31-1.79), and BSI (HR 1.17, 95% CI 1.12-1.23) but not PSA significantly predicted time to CRPC (all, P < .001). The predictive values of M1 (HR 2.06), Gleason grade (HR 1.47), and BSI (HR 1.10) were confirmed in multivariate analyses. Log-rank test for equality of time to CRPC showed the significant predictive value of BSI (BSI = 0 vs 0 < BSI ≤ 1 vs BSI > 1, P < .001). In addition to routine assessment of M1 vs M0 status, BSI contributed to the predictive power.

CONCLUSIONS

BSI is an independent risk factor for the time from initiation of androgen deprivation therapy to CRPC in hormone-naïve patients. The significant prognostic factors, in rank order, were M1 status, Gleason grade, and BSI.

Therapies for castration-resistant prostate cancer in a new era: The indication of vintage hormonal therapy, chemotherapy and the new medicines

When advanced prostate cancer recurred during hormonal therapy and became the castration-resistant prostate cancer, "vintage hormonal therapy," such as antiandrogen alternating therapy or estrogen-related hormonal therapy, was widely carried out in Japan until 2013. This vintage hormonal therapy controlled the progression of castration-resistant prostate cancer. When castration-resistant prostate cancer relapses during these therapies, chemotherapy using docetaxel has been carried out subsequently. Since new hormonal therapies using abiraterone acetate and enzalutamide, which improve the prognosis of castration-resistant prostate cancer, became available in Japan from 2014, therapeutic options for castration-resistant prostate cancer have increased. Furthermore, the improvement of the further prognosis is promising by using cabazitaxel for docetaxel-resistant castration-resistant prostate cancer and radium-223 for castration-resistant prostate cancer with bone metastasis. An increase in therapeutic options gives rise to many questions, including best timing to use them and the indication. Furthermore, physicians have to consider the treatment for the recurrence after having carried out chemotherapy. We want to argue the difference in hormonal therapy between Japan and Western countries, and problems when carrying out new treatments, and the importance of imaging in the present review article.

Diagnostic performance of a computer-assisted diagnosis system for bone scintigraphy of newly developed skeletal metastasis in prostate cancer patients: search for low-sensitivity subgroups

Purpose

The computer-assisted diagnostic system for bone scintigraphy (BS) BONENAVI is used to evaluate skeletal metastasis. We investigated its diagnostic performance in prostate cancer patients with and without skeletal metastasis and searched for the problems.

Methods

An artificial neural network (ANN) value was calculated in 226 prostate cancer patients (124 with skeletal metastasis and 101 without) using BS. Receiver operating characteristic curve analysis was performed and the sensitivity and specificity determined (cutoff ANN = 0.5). Patient’s situation at the time of diagnosis of skeletal metastasis, computed tomography (CT) type, extent of disease (EOD), and BS uptake grade were analyzed. False-negative and false-positive results were recorded.

Results

BONENAVI showed 82% (102/124) of sensitivity and 83% (84/101) specificity for metastasis detection. There were no significant differences among CT types, although low EOD and faint BS uptake were associated with low ANN values and low sensitivity. Patients showed lower sensitivity during the follow-up period than staging work-up. False-negative lesions were often located in the pelvis or adjacent to it. They comprised not only solitary, faint BS lesions but also overlaying to urinary excretion.

Conclusions

BONENAVI with BS has good sensitivity and specificity for detecting prostate cancer’s osseous metastasis. Low EOD and faint BS uptake are associated with low sensitivity but not the CT type. Prostate cancer patients likely to have false-negative results during the follow-up period had a solitary lesion in the pelvis with faint BS uptake or lesions overlaying to urinary excretion.

Bone Scan Index predicts skeletal-related events in patients with metastatic breast cancer

Background

Bone Scan Index (BSI) expresses tumor burden in bone as a percentage of total skeletal mass, but its significance for metastatic breast cancer patients is unknown. We investigated whether baseline BSI is associated with skeletal-related events (SREs) or survival and identified the cut-off BSI score for predicting SREs in metastatic breast cancer patients.

Methods

We retrospectively reviewed 144 patients with bone metastatic breast cancer. Bone scan examinations were performed and BSI was calculated using the Bonenavi^® automated method. All patients received standard medical treatment for metastatic breast cancer. For bone metastasis prophylaxis, bisphosphonates were infused initially with analgesics as needed. We defined SRE as either bony, requiring intervention (surgery and/or radiotherapy) for pain or prevention of fracture, or spinal cord compression. The rates of SRE and overall survival (OS) were evaluated according to baseline BSI, and the cut-off score of BSI for predicting SRE in metastatic breast cancer patients was identified.

Results

Thirty-three patients (25.6 %) had SREs. The median BSI was 1.08 % (inter-quartile range 0.50–3.23 %). To identify the cut-off BSI score for predicting SRE, we performed sensitivity analysis to check P-value at every 0.1 BSI interval (0.4–2.4) by multiple-variable proportional hazard analysis. A BSI cut-off point of 1.4 % showed the lowest P value. Patients with BSI scores ≥1.4 had a significantly higher rate of SRE than those with lower BSI (P = 0.022). However there was no significant difference in OS.

Conclusion

BSI may predict SRE in patients with metastatic breast cancer. A high BSI value (≥1.4) at diagnosis of bone metastasis may be a predictor of SREs in bone metastatic breast cancer patients.

Prognostic value of the bone scan index using a computer-aided diagnosis system for bone scans in hormone-naive prostate cancer patients with bone metastases

BACKGROUND

The bone scan index (BSI) using a computer-aided diagnosis system for bone scans is expected to be an objective and quantitative clinical tool for evaluating bone metastatic prostate cancer. This study aimed to evaluate the pretreatment BSI as a prognostic factor in hormone-naive prostate cancer patients with bone metastases.

METHODS

The study included 60 patients with hormone-naive, bone metastatic prostate cancer that was initially treated with combined androgen blockade therapy. The BONENAVI system was used for calculating the BSI. We evaluated the correlation between overall survival (OS) and pretreatment clinicopathological characteristics, including patients' age, initial prostate-specific antigen (PSA) value, Gleason scores, clinical TNM stage, and the BSI. Cox proportional hazards regression models were used for statistical analysis.

RESULTS

The median follow-up duration was 21.4 months. Clinical or PSA progression occurred in 37 (61.7%) patients and 18 (30.0%) received docetaxel. Death occurred in 16 (26.7%) patients. Of these deaths, 15 (25.0%) were due to prostate cancer. The median OS was not reached. In multivariate analysis, age and the BSI were independent prognostic factors for OS. We evaluated the discriminatory ability of our models, including or excluding BSI by quantifying the C-index. The BSI improved the C-index from 0.751 to 0.801 for OS. Median OS was not reached in patients with a BSI ≤ 1.9 and median OS was 34.8 months in patients with a BSI >1.9 (p = 0.039).

CONCLUSIONS

The pretreatment BSI and patients' age are independent prognostic factors for patients with hormone-naive, bone metastatic prostate cancer.

Evaluation of bone scan index change over time on automated calculation in bone scintigraphy

OBJECTIVE

Bone scintigraphy (bone scan) is useful in detecting metastatic bone lesions through visual assessment of hot spots. A semi-quantitative analysis method that evaluates bone scan images has been eagerly anticipated. BONENAVI is software that enables automatic assessment of bone scan index (BSI). BSI is useful for stratifying cancer patients and monitoring their therapeutic response. The purpose of this study was to evaluate the BONENAVI reading in determining BSI and hot spots at different time intervals after radioisotope injection.

METHODS

We evaluated 32 patients, including 22 males and 10 females. Ten patients had breast cancer, 20 patients had prostate cancer, and 2 had malignant pheochromocytoma. Patients were injected with 740 MBq of (99m)Tc-methylene diphosphonate and bone scintigraphy was performed at 2, 4, and 6 h after injection on each patient. The BSI and the number of hot spots were obtained from BONENAVI software. Bone scan images were also visually assessed to exclude false positives due to artifacts. Analyses were performed in all lesions, selected true lesions, segment based and cancer type based. Non-parametric statistical analyses for pairwise multiple group comparison were performed using Friedman test followed with post hoc analysis.

RESULTS

The BSIs and the number of hot spots were significantly increased with time, with significant differences between each of time points (P < 0.001). Analysis of regional BSI (rBSI) and hot spot number changes of selected 15 true lesions also showed similar increase (P < 0.001). In general, the pelvic segment was the most prone to rBSI changes and the chest segment was the most prone to hot spot number changes. Visual assessment showed that BONENAVI diagnosed some typical artifacts as metastases (hot spots).

CONCLUSION

BONENAVI reading of BSIs and hot spot numbers was highly affected by acquisition time. In serial or follow-up examinations (in particular, for monitoring therapeutic efficacy), acquisition time should be fixed for each patient. Cautious interpretation should be made on segments with high physiological uptake. BONENAVI reading was prone to misinterpretation of artifacts. Visual assessment is necessary to rule out this possibility.

Current status of nuclear cardiology in Japan: Ongoing efforts to improve clinical standards and to establish evidence

Nuclear cardiology imaging tests are widely performed in Japan as clinical practice. The Japanese nuclear cardiology community has developed new diagnostic imaging tests using (123)I-beta-methyl-p-iodophenyl-pentadecanoic acid, (123)I-metaiodobenzylguanidine, and (18)F-fluorodeoxyglucose PET for detecting cardiac involvement in sarcoidosis. These tests have become popular worldwide. The Japanese Circulation Society and the Japanese Society of Nuclear Cardiology have published clinical imaging guidelines showing indications and standards for the new imaging tests. JSNC is currently striving to improve the standard of clinical practice and is promoting research activities.

Diagnostic Performance of Artificial Neural Network for Detecting Ischemia in Myocardial Perfusion Imaging

BACKGROUND

The purpose of this study was to apply an artificial neural network (ANN) in patients with coronary artery disease (CAD) and to characterize its diagnostic ability compared with conventional visual and quantitative methods in myocardial perfusion imaging (MPI).

METHODS AND RESULTS

A total of 106 patients with CAD were studied with MPI, including multiple vessel disease (49%), history of myocardial infarction (27%) and coronary intervention (30%). The ANN detected abnormal areas with a probability of stress defect and ischemia. The consensus diagnosis based on expert interpretation and coronary stenosis was used as the gold standard. The left ventricular ANN value was higher in the stress-defect group than in the no-defect group (0.92±0.11 vs. 0.25±0.32, P<0.0001) and higher in the ischemia group than in the no-ischemia group (0.70±0.40 vs. 0.004±0.032, P<0.0001). Receiver-operating characteristics curve analysis showed comparable diagnostic accuracy between ANN and the scoring methods (0.971 vs. 0.980 for stress defect, and 0.882 vs. 0.937 for ischemia, both P=NS). The relationship between the ANN and defect scores was non-linear, with the ANN rapidly increased in ranges of summed stress score of 2-7 and summed defect score of 2-4.

CONCLUSIONS

Although the diagnostic ability of ANN was similar to that of conventional scoring methods, the ANN could provide a different viewpoint for judging abnormality, and thus is a promising method for evaluating abnormality in MPI.

Reconsideration of progression to CRPC during androgen deprivation therapy

Androgen blockade-naïve prostate cancer (PCa) develops into CRPC during androgen deprivation therapy (ADT) by various genetic actions. The androgen-AR signaling axis plays a key role in this development. PCa cells mainly adapt themselves to the environment of lower androgen concentrations and change into androgen-hypersensitive cells or androgen-independent cells. Androgens of adrenal origin and their metabolites synthesized in the microenvironment in an intracrine/paracrine fashion act on surviving PCa cells and secrete prostate specific antigen (PSA). Total androgen deprivation (TAD) (castration, antiandrogen, and CYP17A1 inhibitor) can become an effective therapeutic strategy concerning the androgen signaling axis-related pathway. However, it is important to ascertain whether elevation of serum PSA results from AR activation or from an androgen-independent tumor volume effect. Then, clinicians can judge it adequately using the imaging studies such as CT or bone scan as well as PSA and bone metabolic markers, an approach which is necessary to judge which treatment is most suitable for the CRPC patients. This article is part of a Special Issue entitled 'Essential role of DHEA'.