May Sadik

A Novel Automated Platform for Quantifying the Extent of Skeletal Tumour Involvement in Prostate Cancer Patients Using the Bone Scan Index

BACKGROUND There is little consensus on a standard approach to analysing bone scan images. The Bone Scan Index (BSI) is predictive of survival in patients with progressive prostate cancer (PCa), but the popularity of this metric is hampered by the tedium of the manual calculation. OBJECTIVE Develop a fully automated method of quantifying the BSI and determining the clinical value of automated BSI measurements beyond conventional clinical and pathologic features. DESIGN, SETTING, AND PARTICIPANTS We conditioned a computer-assisted diagnosis system identifying metastatic lesions on a bone scan to automatically compute BSI measurements. A training group of 795 bone scans was used in the conditioning process. Independent validation of the method used bone scans obtained ≤3 mo from diagnosis of 384 PCa cases in two large population-based cohorts. An experienced analyser (blinded to case identity, prior BSI, and outcome) scored the BSI measurements twice. We measured prediction of outcome using pretreatment Gleason score, clinical stage, and prostate-specific antigen with models that also incorporated either manual or automated BSI measurements. MEASUREMENTS The agreement between methods was evaluated using Pearsons correlation coefficient. Discrimination between prognostic models was assessed using the concordance index (C-index). RESULTS AND LIMITATIONS Manual and automated BSI measurements were strongly correlated (ρ=0.80), correlated more closely (ρ=0.93) when excluding cases with BSI scores≥10 (1.8%), and were independently associated with PCa death (p<0.0001 for each) when added to the prediction model. Predictive accuracy of the base model (C-index: 0.768; 95% confidence interval [CI], 0.702-0.837) increased to 0.794 (95% CI, 0.727-0.860) by adding manual BSI scoring, and increased to 0.825 (95% CI, 0.754-0.881) by adding automated BSI scoring to the base model. CONCLUSIONS Automated BSI scoring, with its 100% reproducibility, reduces turnaround time, eliminates operator-dependent subjectivity, and provides important clinical information comparable to that of manual BSI scoring.

Quality of planar whole-body bone scan interpretations - a nationwide survey

PurposeThe purpose of this study was to investigate, in a nationwide study, the inter-observer variation and performance in interpretations of bone scans regarding the presence or absence of bone metastases.MethodsBone scan images from 59 patients with breast or prostate cancer, who had undergone scintigraphy due to suspected bone metastatic disease, were studied. The patients were selected to reflect the spectrum of pathology found in everyday clinical work. Whole body images, anterior and posterior views, were sent to all 30 hospitals in Sweden that perform bone scans. Thirty-seven observers from 18 hospitals agreed to participate in the study. They were asked to classify each of the patient studies regarding the presence of bone metastasis, using a four-point scale. Each observer’s classifications were pairwise compared with the classifications made by all the other observers, resulting in 666 pairs of comparisons. The interpretations of the 37 observers were also compared with the final clinical assessment, which was based on follow-up scans and other clinical data.ResultsOn average, two observers agreed on 64% of the bone scan classifications. Kappa values ranged between 0.16 and 0.82, with a mean of 0.48. Sensitivity and specificity for the observers compared with the final clinical assessment were 77% and 96%, respectively, for detecting bone metastases in planar whole-body bone scanning.ConclusionModerate inter-observer agreement was found when observers were compared pairwise. False-negative errors seem to be the major problem in the interpretations of bone scan images, whilst the specificities for the observers were high.

Computer-Assisted Interpretation of Planar Whole-Body Bone Scans

The purpose of this study was to develop a computer-assisted diagnosis (CAD) system based on image-processing techniques and artificial neural networks for the interpretation of bone scans performed to determine the presence or absence of metastases. Methods: A training group of 810 consecutive patients who had undergone bone scintigraphy due to suspected metastatic disease were included in the study. Whole-body images, anterior and posterior views, were obtained after an injection of 99mTc-methylene diphosphonate. The image-processing techniques included algorithms for automatic segmentation of the skeleton and automatic detection and feature extraction of hot spots. Two sets of artificial neural networks were used to classify the images, 1 classifying each hot spot separately and the other classifying the whole bone scan. A test group of 59 patients with breast or prostate cancer was used to evaluate the CAD system. The patients in the test group were selected to reflect the spectrum of pathology found in everyday clinical work. As the gold standard for the test group, we used the final clinical assessment of each case. This assessment was based on follow-up scans and other clinical data, including the results of laboratory tests, and available diagnostic images, such as from MRI, CT, and radiography, from a mean follow-up period of 4.8 y. Results: The CAD system correctly identified 19 of the 21 patients with metastases in the test group, showing a sensitivity of 90%. False-positive classification of metastases was made in 4 of the 38 patients not classified as having metastases by the gold standard, resulting in a specificity of 89%. Conclusion: A completely automated CAD system can be used to detect metastases in bone scans. Application of the method as a clinical decision support tool appears to have significant potential.

A new computer-based decision-support system for the interpretation of bone scans.

ObjectiveTo develop a completely automated method, based on image processing techniques and artificial neural networks, for the interpretation of bone scans regarding the presence or absence of metastases. MethodsA total of 200 patients, all of whom had the diagnosis of breast or prostate cancer and had undergone bone scintigraphy, were studied retrospectively. Whole-body images, anterior and posterior, were obtained after injection of 99mTc-methylene diphosphonate. The study material was randomly divided into a training group and a test group, with 100 patients in each group. The training group was used in the process of developing the image analysis techniques and to train the artificial neural networks. The test group was used to evaluate the automated method. The image processing techniques included algorithms for segmentation of the head, chest, spine, pelvis and bladder, automatic thresholding and detection of hot spots. Fourteen features from each examination were used as input to artificial neural networks trained to classify the images. The interpretations by an experienced physician were used as the ‘gold standard’. ResultsThe automated method correctly identified 28 of the 31 patients with metastases in the test group, i.e., a sensitivity of 90%. A false positive classification of metastases was made in 18 of the 69 patients not classified as having metastases by the experienced physician, resulting in a specificity of 74%. ConclusionA completely automated method can be used to detect metastases in bone scans. Future developments in this field may lead to clinically valuable decision-support tools.

Analytical Validation of the Automated Bone Scan Index as an Imaging Biomarker to Standardize the Quantitative Changes in Bone Scans of Patients with Metastatic Prostate Cancer.

A reproducible and quantitative imaging biomarker is needed to standardize the evaluation of changes in bone scans of prostate cancer patients with skeletal metastasis. We performed a series of analytic validation studies to evaluate the performance of the automated bone scan index (BSI) as an imaging biomarker in patients with metastatic prostate cancer. Methods: Three separate analytic studies were performed to evaluate the accuracy, precision, and reproducibility of the automated BSI. Simulation study: bone scan simulations with predefined tumor burdens were created to assess accuracy and precision. Fifty bone scans were simulated with a tumor burden ranging from low to high disease confluence (0.10–13.0 BSI). A second group of 50 scans was divided into 5 subgroups, each containing 10 simulated bone scans, corresponding to BSI values of 0.5, 1.0, 3.0, 5.0, and 10.0. Repeat bone scan study: to assess the reproducibility in a routine clinical setting, 2 repeat bone scans were obtained from metastatic prostate cancer patients after a single 600-MBq 99mTc-methylene diphosphonate injection. Follow-up bone scan study: 2 follow-up bone scans of metastatic prostate cancer patients were analyzed to determine the interobserver variability between the automated BSIs and the visual interpretations in assessing changes. The automated BSI was generated using the upgraded EXINI boneBSI software (version 2). The results were evaluated using linear regression, Pearson correlation, Cohen κ measurement, coefficient of variation, and SD. Results: Linearity of the automated BSI interpretations in the range of 0.10–13.0 was confirmed, and Pearson correlation was observed at 0.995 (n = 50; 95% confidence interval, 0.99–0.99; P < 0.0001). The mean coefficient of variation was less than 20%. The mean BSI difference between the 2 repeat bone scans of 35 patients was 0.05 (SD = 0.15), with an upper confidence limit of 0.30. The interobserver agreement in the automated BSI interpretations was more consistent (κ = 0.96, P < 0.0001) than the qualitative visual assessment of the changes (κ = 0.70, P < 0.0001) was in the bone scans of 173 patients. Conclusion: The automated BSI provides a consistent imaging biomarker capable of standardizing quantitative changes in the bone scans of patients with metastatic prostate cancer.

Relation between pain and skeletal metastasis in patients with prostate or breast cancer

The aim of this study was to examine the relation between pain and bone metastases in a group of patients with prostate or breast cancer that had been referred for bone scintigraphy. Whole‐body bone scans, anterior and posterior views obtained with a dual detector gamma camera were studied from 101 consecutive patients who had undergone scintigraphy (600 MBq Tc‐99m MDP) because of suspected bone metastatic disease. At the time of the examination, all patients were asked whether they felt any pain or had recently a trauma. This information was correlated with the classifications regarding the presence or absence of bone metastases made by a group of three experienced physicians. In patients with prostate cancer, we found metastases in 47% (18/38) of the patients with pain, but only in 12% (2/17) of the patients without pain (p = 0·01). In patients with breast cancer, on the other hand, metastases were more common in patients without pain (71%; 10/14) than in patients with pain (34%; 11/32) (p = 0·02). In conclusion, a significant relation between pain and skeletal metastases could be found in patients with prostate cancer and a reverse relation in patients with breast cancer.

Analysis of regional bone scan index measurements for the survival of patients with prostate cancer.

BackgroundA bone scan is a common method for monitoring bone metastases in patients with advanced prostate cancer. The Bone Scan Index (BSI) measures the tumor burden on the skeleton, expressed as a percentage of the total skeletal mass. Previous studies have shown that BSI is associated with survival of prostate cancer patients. The objective in this study was to investigate to what extent regional BSI measurements, as obtained by an automated method, can improve the survival analysis for advanced prostate cancer.MethodsThe automated method for analyzing bone scan images computed BSI values for twelve skeletal regions, in a study population consisting of 1013 patients diagnosed with prostate cancer. In the survival analysis we used the standard Cox proportional hazards model and a more advanced non-linear method based on artificial neural networks. The concordance index (C-index) was used to measure the performance of the models.ResultsA Cox model with age and total BSI obtained a C-index of 70.4%. The best Cox model with regional measurements from Costae, Pelvis, Scapula and the Spine, together with age, got a similar C-index (70.5%). The overall best single skeletal localisation, as measured by the C-index, was Costae. The non-linear model performed equally well as the Cox model, ruling out any significant non-linear interactions among the regional BSI measurements.ConclusionThe present study showed that the localisation of bone metastases obtained from the bone scans in prostate cancer patients does not improve the performance of the survival models compared to models using the total BSI. However a ranking procedure indicated that some regions are more important than others.

Automated decision support for bone scintigraphy

A quantitative analysis of metastatic bone involvement can be an important prognostic indicator of survival or a tool in monitoring treatment response in patients with cancer. The purpose of this study was to develop a completely automated decision support system for whole-body bone scans using image analysis and artificial neural networks. The study population consisted of 795 whole-body bone scans. The decision support system first detects and classifies individual hotspots as being metastatic or not. A second prediction model then classifies the scan regarding metastatic disease on a patient level. The test set sensitivity and specificity was 95% and 64% respectively, corresponding to 95% area under the receiver operating characteristics curve.

Evaluation of changes in Bone Scan Index at different acquisition time-points in bone scintigraphy

Bone Scan Index (BSI) is a validated imaging biomarker to objectively assess tumour burden in bone in patients with prostate cancer, and can be used to monitor treatment response. It is not known if BSI is significantly altered when images are acquired at a time difference of 1 h. The aim of this study was to investigate if automatic calculation of BSI is affected when images are acquired 1 hour apart, after approximately 3 and 4 h. We prospectively studied patients with prostate cancer who were referred for bone scintigraphy according to clinical routine. The patients performed a whole‐body bone scan at approximately 3 h after injection of radiolabelled bisphosphonate and a second 1 h after the first. BSI values for each bone scintigraphy were obtained using EXINI boneBSI software. A total of 25 patients were included. Median BSI for the first acquisition was 0·05 (range 0–11·93) and for the second acquisition 0·21 (range 0–13·06). There was a statistically significant increase in BSI at the second image acquisition compared to the first (P<0·001). In seven of 25 patients (28%) and in seven of 13 patients with BSI > 0 (54%), a clinically significant increase (>0·3) was observed. The time between injection and scanning should be fixed when changes in BSI are important, for example when monitoring therapeutic efficacy.

Automated quantification of reference levels in liver and mediastinal blood pool for the Deauville therapy response classification using FDG-PET/CT in Hodgkin and non-Hodgkin lymphomas

18F‐FDG‐PET/CT has become a standard for assessing treatment response in patients with lymphoma. A subjective interpretation of the scan based on the Deauville 5‐point scale has been widely adopted. However, inter‐observer variability due to the subjectivity of the interpretation is a limitation. Our main goal is to develop an objective and automated method for evaluating response. The first step is to develop and validate an artificial intelligence (AI)‐based method, for the automated quantification of reference levels in the liver and mediastinal blood pool in patients with lymphoma.