Metabolic tumour volume in Hodgkin lymphoma-A comparison between manual and AI-based analysis.

AIM: To compare total metabolic tumour volume (tMTV), calculated using two artificial intelligence (AI)-based tools, with manual segmentation by specialists as the reference. METHODS: Forty-eight consecutive Hodgkin lymphoma (HL) patients staged with [18F] fluorodeoxyglucose positron emission tomography/computed tomography were included. The median age was 35 years (range: 7-75), 46% female. The tMTV was automatically measured using the AI-based tools positron emission tomography assisted reporting system (PARS) (from Siemens) and RECOMIA (recomia.org) without any manual adjustments. A group of eight nuclear medicine specialists manually segmented lesions for tMTV calculations; each patient was independently segmented by two specialists. RESULTS: The median of the manual tMTV was 146 cm3 (interquartile range [IQR]: 79-568 cm3) and the median difference between two tMTV values segmented by different specialists for the same patient was 26 cm3 (IQR: 10-86 cm3). In 22 of the 48 patients, the manual tMTV value was closer to the RECOMIA tMTV value than to the manual tMTV value segmented by the second specialist. In 11 of the remaining 26 patients, the difference between the RECOMIA tMTV and the manual tMTV was small (<26 cm3, which was the median difference between two manual tMTV values from the same patient). The corresponding numbers for PARS were 18 and 10 patients, respectively. CONCLUSION: The results of this study indicate that RECOMIA and Siemens PARS AI tools could be used without any major manual adjustments in 69% (33/48) and 58% (28/48) of HL patients, respectively. This demonstrates the feasibility of using AI tools to support physicians measuring tMTV for assessment of prognosis in clinical practice.

Artificial Intelligence Increases the Agreement among Physicians Classifying Focal Skeleton/Bone Marrow Uptake in Hodgkin's Lymphoma Patients Staged with [18F]FDG PET/CT-a Retrospective Study.

Purpose: Classification of focal skeleton/bone marrow uptake (BMU) can be challenging. The aim is to investigate whether an artificial intelligence-based method (AI), which highlights suspicious focal BMU, increases interobserver agreement among a group of physicians from different hospitals classifying Hodgkin's lymphoma (HL) patients staged with [18F]FDG PET/CT. Methods: Forty-eight patients staged with [18F]FDG PET/CT at Sahlgenska University Hospital between 2017 and 2018 were reviewed twice, 6 months apart, regarding focal BMU. During the second time review, the 10 physicians also had access to AI-based advice regarding focal BMU. Results: Each physician's classifications were pairwise compared with the classifications made by all the other physicians, resulting in 45 unique pairs of comparisons both without and with AI advice. The agreement between the physicians increased significantly when AI advice was available, which was measured as an increase in mean Kappa values from 0.51 (range 0.25-0.80) without AI advice to 0.61 (range 0.19-0.94) with AI advice (p = 0.005). The majority of the physicians agreed with the AI-based method in 40 (83%) of the 48 cases. Conclusion: An AI-based method significantly increases interobserver agreement among physicians working at different hospitals by highlighting suspicious focal BMU in HL patients staged with [18F]FDG PET/CT.

Artificial intelligence could alert for focal skeleton/bone marrow uptake in Hodgkin's lymphoma patients staged with FDG-PET/CT.

To develop an artificial intelligence (AI)-based method for the detection of focal skeleton/bone marrow uptake (BMU) in patients with Hodgkin's lymphoma (HL) undergoing staging with FDG-PET/CT. The results of the AI in a separate test group were compared to the interpretations of independent physicians. The skeleton and bone marrow were segmented using a convolutional neural network. The training of AI was based on 153 un-treated patients. Bone uptake significantly higher than the mean BMU was marked as abnormal, and an index, based on the total squared abnormal uptake, was computed to identify the focal uptake. Patients with an index above a predefined threshold were interpreted as having focal uptake. As the test group, 48 un-treated patients who had undergone a staging FDG-PET/CT between 2017-2018 with biopsy-proven HL were retrospectively included. Ten physicians classified the 48 cases regarding focal skeleton/BMU. The majority of the physicians agreed with the AI in 39/48 cases (81%) regarding focal skeleton/bone marrow involvement. Inter-observer agreement between the physicians was moderate, Kappa 0.51 (range 0.25-0.80). An AI-based method can be developed to highlight suspicious focal skeleton/BMU in HL patients staged with FDG-PET/CT. Inter-observer agreement regarding focal BMU is moderate among nuclear medicine physicians.

Deep learning-based quantification of PET/CT prostate gland uptake: association with overall survival.

AIM: To validate a deep-learning (DL) algorithm for automated quantification of prostate cancer on positron emission tomography/computed tomography (PET/CT) and explore the potential of PET/CT measurements as prognostic biomarkers. MATERIAL AND METHODS: Training of the DL-algorithm regarding prostate volume was performed on manually segmented CT images in 100 patients. Validation of the DL-algorithm was carried out in 45 patients with biopsy-proven hormone-naïve prostate cancer. The automated measurements of prostate volume were compared with manual measurements made independently by two observers. PET/CT measurements of tumour burden based on volume and SUV of abnormal voxels were calculated automatically. Voxels in the co-registered 18 F-choline PET images above a standardized uptake value (SUV) of 2·65, and corresponding to the prostate as defined by the automated segmentation in the CT images, were defined as abnormal. Validation of abnormal voxels was performed by manual segmentation of radiotracer uptake. Agreement between algorithm and observers regarding prostate volume was analysed by Sørensen-Dice index (SDI). Associations between automatically based PET/CT biomarkers and age, prostate-specific antigen (PSA), Gleason score as well as overall survival were evaluated by a univariate Cox regression model. RESULTS: The SDI between the automated and the manual volume segmentations was 0·78 and 0·79, respectively. Automated PET/CT measures reflecting total lesion uptake and the relation between volume of abnormal voxels and total prostate volume were significantly associated with overall survival (P = 0·02), whereas age, PSA, and Gleason score were not. CONCLUSION: Automated PET/CT biomarkers showed good agreement to manual measurements and were significantly associated with overall survival.

Correction to: 3D skeletal uptake of 18F sodium fluoride in PET/CT images is associated with overall survival in patients with prostate cancer.

Following publication of the original article [1], the authors flagged the that the Kaplan-Meier curve in Fig. 6 is a duplication of the Kaplan-Meier curve in Fig. 5, which is not correct.

Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET/CT-based 3D quantification of skeletal metastases.

PURPOSE: The aim of this study was to develop a deep learning-based method for segmentation of bones in CT scans and test its accuracy compared to manual delineation, as a first step in the creation of an automated PET/CT-based method for quantifying skeletal tumour burden. METHODS: Convolutional neural networks (CNNs) were trained to segment 49 bones using manual segmentations from 100 CT scans. After training, the CNN-based segmentation method was tested on 46 patients with prostate cancer, who had undergone 18F-choline-PET/CT and 18F-NaF PET/CT less than three weeks apart. Bone volumes were calculated from the segmentations. The network's performance was compared with manual segmentations of five bones made by an experienced physician. Accuracy of the spatial overlap between automated CNN-based and manual segmentations of these five bones was assessed using the Sørensen-Dice index (SDI). Reproducibility was evaluated applying the Bland-Altman method. RESULTS: The median (SD) volumes of the five selected bones were by CNN and manual segmentation: Th7 41 (3.8) and 36 (5.1), L3 76 (13) and 75 (9.2), sacrum 284 (40) and 283 (26), 7th rib 33 (3.9) and 31 (4.8), sternum 80 (11) and 72 (9.2), respectively. Median SDIs were 0.86 (Th7), 0.85 (L3), 0.88 (sacrum), 0.84 (7th rib) and 0.83 (sternum). The intraobserver volume difference was less with CNN-based than manual approach: Th7 2% and 14%, L3 7% and 8%, sacrum 1% and 3%, 7th rib 1% and 6%, sternum 3% and 5%, respectively. The average volume difference measured as ratio volume difference/mean volume between the two CNN-based segmentations was 5-6% for the vertebral column and ribs and ≤3% for other bones. CONCLUSION: The new deep learning-based method for automated segmentation of bones in CT scans provided highly accurate bone volumes in a fast and automated way and, thus, appears to be a valuable first step in the development of a clinical useful processing procedure providing reliable skeletal segmentation as a key part of quantification of skeletal metastases.

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.

BACKGROUND: 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. METHODS: The AI-based method was trained to segment the liver and the mediastinal blood pool in CT images from 80 lymphoma patients, who had undergone 18F-FDG-PET/CT, and apply this to a validation group of six lymphoma patients. CT segmentations were transferred to the PET images to obtain automatic standardized uptake values (SUV). The AI-based analysis was compared to corresponding manual segmentations performed by two radiologists. RESULTS: The mean difference for the comparison between the AI-based liver SUV quantifications and those of the two radiologists in the validation group was 0·02 and 0·02, respectively, and 0·02 and 0·02 for mediastinal blood pool respectively. CONCLUSIONS: An AI-based method for the automated quantification of reference levels in the liver and mediastinal blood pool shows good agreement with results obtained by experienced radiologists who had manually segmented the CT images. This is a first, promising step towards objective treatment response evaluation in patients with lymphoma based on 18F-FDG-PET/CT.

A prospective study to evaluate the intra-individual reproducibility of bone scans for quantitative assessment in patients with metastatic prostate cancer.

BACKGROUND: The Bone Scan Index (BSI) is used to quantitatively assess the total tumour burden in bone scans of patients with metastatic prostate cancer. The clinical utility of BSI has recently been validated as a prognostic imaging biomarker. However, the clinical utility of the on-treatment change in BSI is dependent on the reproducibility of bone scans. The objective of this prospective study is to evaluate the intra-patient reproducibility of two bone scan procedures performed at a one-week interval. METHODS: We prospectively studied prostate cancer patients who were referred for bone scintigraphy at our centres according to clinical routine. All patients underwent two whole-body bone scans: one for clinical routine purposes and a second one as a repeated scan after approximately one week. BSI values were obtained for each bone scintigraph using EXINI boneBSI software. RESULTS: A total of 20 patients were enrolled. There was no statistical difference between the BSI values of the first (median = 0.66, range 0-40.77) and second (median = 0.63, range 0-22.98) bone scans (p = 0.41). The median difference in BSI between the clinical routine and repeated scans was - 0.005 (range - 17.79 to 0). The 95% confidence interval for the median value was - 0.1 to 0. A separate analysis was performed for patients with BSI ≤ 10 (n = 17). Differences in BSI were smaller for patients with BSI ≤ 10 compared to the whole cohort (median - 0.1, range - 2.2-0, 95% confidence interval - 0.1 to 0). CONCLUSIONS: The automated BSI demonstrated high intra-individual reproducibility for BSI ≤ 10 in the two repeated bone scans of patients with prostate cancer. The study supports the use of BSI as a quantitative parameter to evaluate the change in total tumour burden in bone scans.

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.

3D skeletal uptake of 18F sodium fluoride in PET/CT images is associated with overall survival in patients with prostate cancer.

BACKGROUND: Sodium fluoride (NaF) positron emission tomography combined with computer tomography (PET/CT) has shown to be more sensitive than the whole-body bone scan in the detection of skeletal uptake due to metastases in prostate cancer. We aimed to calculate a 3D index for NaF PET/CT and investigate its correlation to the bone scan index (BSI) and overall survival (OS) in a group of patients with prostate cancer. METHODS: NaF PET/CT and bone scans were studied in 48 patients with prostate cancer. Automated segmentation of the thoracic and lumbar spines, sacrum, pelvis, ribs, scapulae, clavicles, and sternum were made in the CT images. Hotspots in the PET images were selected using both a manual and an automated method. The volume of each hotspot localized in the skeleton in the corresponding CT image was calculated. Two PET/CT indices, based on manual (manual PET index) and automatic segmenting using a threshold of SUV 15 (automated PET15 index), were calculated by dividing the sum of all hotspot volumes with the volume of all segmented bones. BSI values were obtained using a software for automated calculations. RESULTS: BSI, manual PET index, and automated PET15 index were all significantly associated with OS and concordance indices were 0.68, 0.69, and 0.70, respectively. The median BSI was 0.39 and patients with a BSI >0.39 had a significantly shorter median survival time than patients with a BSI <0.39 (2.3 years vs not reached after 5 years of follow-up [p = 0.01]). The median manual PET index was 0.53 and patients with a manual PET index >0.53 had a significantly shorter median survival time than patients with a manual PET index <0.53 (2.5 years vs not reached after 5 years of follow-up [p < 0.001]). The median automated PET15 index was 0.11 and patients with an automated PET15 index >0.11 had a significantly shorter median survival time than patients with an automated PET15 index <0.11 (2.3 years vs not reached after 5 years of follow-up [p < 0.001]). CONCLUSIONS: PET/CT indices based on NaF PET/CT are correlated to BSI and significantly associated with overall survival in patients with prostate cancer.

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

UNLABELLED: 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 (99m)Tc-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 bone(BSI) 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.

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

BACKGROUND: A 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. METHODS: The 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. RESULTS: A 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. CONCLUSION: The 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.

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 Pearson's 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.

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.

Improved classifications of planar whole-body bone scans using a computer-assisted diagnosis system: a multicenter, multiple-reader, multiple-case study.

UNLABELLED: The aim of this multicenter study was to investigate whether a computer-assisted diagnosis (CAD) system could improve performance and reduce interobserver variation in bone-scan interpretations of the presence or absence of bone metastases. METHODS: The whole-body bone scans (anterior and posterior views) of 59 patients with breast or prostate cancer who had undergone scintigraphy for suspected bone metastatic disease were studied. The patients were selected to reflect the spectrum of pathology found in everyday clinical work. Thirty-five physicians working at 18 of the 30 nuclear medicine departments in Sweden agreed to participate. The physicians were asked to classify each case for the presence or absence of bone metastasis, without (baseline) and with the aid of the CAD system (1 y later), using a 4-point scale. The final clinical assessments, based on follow-up scans and other clinical data including the results of laboratory tests and available diagnostic images (such as MRI, CT, and radiographs from a mean follow-up period of 4.8 y), were used as the gold standard. Each physician's classification was pairwise compared with the classifications made by all the other physicians, resulting in 595 pairs of comparisons, both at baseline and after using the CAD system. RESULTS: The physicians increased their sensitivity from 78% without to 88% with the aid of the CAD system (P < 0.001). The specificity did not change significantly with CAD. Percentage agreement and kappa-values between paired physicians on average increased from 64% to 70% and from 0.48 to 0.55, respectively, with the CAD system. CONCLUSION: A CAD system improved physicians' sensitivity in detecting metastases and reduced interobserver variation in planar whole-body bone scans. The CAD system appears to have significant potential in assisting physicians in their clinical routine.

Computer-assisted interpretation of planar whole-body bone scans.

UNLABELLED: 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 (99m)Tc-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.

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

PURPOSE: The 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. METHODS: Bone 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. RESULTS: On 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. CONCLUSION: Moderate 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.

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

OBJECTIVE: To 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. METHODS: A 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'. RESULTS: The 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%. CONCLUSION: A 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.

Improved stroke volume assessment in the aortic and mitral valves with a new method in subjects without regurgitation.

BACKGROUND: Echocardiography combining Doppler and two-dimensional data is recommended for quantitative assessments of valvular regurgitation. We applied a new method to calculate the mitral annulus (MA) area in combination with multiple sample sites. Individuals without regurgitation in whom the valvular and left ventricular stroke volumes (SV) should be identical were investigated in order to evaluate the feasibility in quantitative assessments of valvular regurgitation. METHODS AND RESULTS: Twenty subjects were included. Flow velocity was registered with pulsed Doppler in different positions in the left ventricular outflow tract (LVOT) and in the MA. The MA area was assumed to be either circular, using the diameter from a four-chamber projection, or elliptic, using the major diameter from a parasternal short axis and a minor diameter from an apical long axis. Left ventricular (LV) SV was measured from LV volumes using the biplane method. The overall difference between LVOT SV and mitral SV using one centrally located measurement and elliptic MA was 3.2+/-15.6 ml (P=0.38), 0.9+/-15.7 ml between LVOT SV and LV SV (P=0.80) and -2.2+/-15.2 ml between mitral SV and LV SV (P=0.54). The corresponding standard deviation of the differences as a percentage of the mean value was 24%, 25% and 23%. A circular shaped MA overestimated the mitral SV compared with LVOT SV (P=0.009) and LV SV (P=0.004). Increasing the number of sample sites in the LVOT or MA did not further improve the results. CONCLUSION: Doppler and two-dimensional echocardiography can be used to quantify regurgitation in groups of patients. In individual patients the wide distribution of differences between valves and LV SV implies that the method should be used in conjunction with other Doppler echocardiographic parameters.