Peter Gjertsson

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.

Progression of bone metastases in patients with prostate cancer - automated detection of new lesions and calculation of bone scan index

BackgroundThe objective of this study was firstly to develop and evaluate an automated method for the detection of new lesions and changes in bone scan index (BSI) in serial bone scans and secondly to evaluate the prognostic value of the method in a group of patients receiving chemotherapy.MethodsThe automated method for detection of new lesions was evaluated in a group of 266 patients using the classifications by three experienced bone scan readers as a gold standard. The prognostic value of the method was assessed in a group of 31 metastatic hormone-refractory prostate cancer patients who were receiving docetaxel. Cox proportional hazards were used to investigate the association between percentage change in BSI, number of new lesions and overall survival. Kaplan-Meier estimates of the survival function were used to indicate a significant difference between patients with an increase/decrease in BSI or those with two or more new lesions or less than two new lesions.ResultsThe automated method detected progression defined as two or more new lesions with a sensitivity of 93% and a specificity of 87%. In the treatment group, both BSI changes and the number of new metastases were significantly associated with survival. Two-year survival for patients with increasing and decreasing BSI from baseline to follow-up scans were 18% and 57% (p = 0.03), respectively. Two-year survival for patients fulfilling and not fulfilling the criterion of two or more new lesions was 35% and 38% (n.s.), respectively.ConclusionsAn automated method can be used to calculate the number of new lesions and changes in BSI in serial bone scans. These imaging biomarkers contained prognostic information in a small group of patients with prostate cancer receiving chemotherapy.

Bone Scan Index: a prognostic imaging biomarker for high-risk prostate cancer patients receiving primary hormonal therapy.

BackgroundThe objective of this study was to explore the prognostic value of the Bone Scan Index (BSI) obtained at the time of diagnosis in a group of high-risk prostate cancer patients receiving primary hormonal therapy.MethodsThis was a retrospective study based on 130 consecutive prostate cancer patients at high risk, based on clinical stage (T2c/T3/T4), Gleason score (8 to 10) and prostate-specific antigen (PSA) (> 20 ng/mL), who had undergone whole-body bone scans < 3 months after diagnosis and who received primary hormonal therapy. BSI was calculated using an automated method. Cox proportional-hazards regression models were used to investigate the association between clinical stage, Gleason score, PSA, BSI and survival. Discrimination between prognostic models was assessed using the concordance index (C-index).ResultsIn a multivariate analysis, Gleason score (p = 0.01) and BSI (p < 0.001) were associated with survival, but clinical stage (p = 0.29) and PSA (p = 0.57) were not prognostic. The C-index increased from 0.66 to 0.71 when adding BSI to a model including clinical stage, Gleason score and PSA. The 5-year probability of survival was 55% for patients without metastases, 42% for patients with BSI < 1, 31% for patients with BSI = 1 to 5, and 0% for patients with BSI > 5.ConclusionsBSI can be used as a complement to PSA to risk-stratify high-risk prostate cancer patients at the time of diagnosis. This imaging biomarker, reflecting the extent of metastatic disease, can be of value both in clinical trials and in patient management when deciding on treatment.

Important pressure recovery in patients with aortic stenosis and high Doppler gradients.

Pressure recovery has been described in aortic stenosis and may explain the difference occasionally observed between Doppler- and catheter-measured gradients. A narrow ascending aorta (AA) and moderately severe stenosis favors pressure recovery. The aims of this study were to investigate the degree to which these conditions are present in patients with aortic stenosis and high Doppler gradients and to evaluate the magnitude of pressure recovery. One hundred sixteen patients were examined with Doppler echocardiography before aortic valve replacement. Patients with a maximum gradient >70 mm Hg (n = 81) were included. The diameter of the AA was measured and compared with the diameter in an age- and body size-matched group of normal controls (n = 23). Pressure recovery was estimated from a previously validated equation by measuring the maximum Doppler gradient, the effective orifice area (EOA), and the diameter of the AA. The diameter of the AA was similar for patients (mean 3.0 cm, range 2.1 to 4.1) and normal controls (mean 3.0 cm, range 2.3 to 3.5). The maximum Doppler gradient was 107 mm Hg (range 71 to 170) and the EOA was 0.6 cm(2) (range 0.2 to 1.3). The calculated pressure recovery was 18 mm Hg (range 6 to 37), which gives a net gradient of 89 mm Hg (range 51 to 151). Twenty-three percent had a net gradient <70 mm Hg. A cutoff of EOA/AA diameter at >0.2 cm identified 84% of patients (16 of 19) with a net gradient <70 mm Hg. In conclusion, we found that important pressure recovery can be expected in most patients with aortic stenosis and high Doppler gradients. Pressure recovery may explain why some patients with high Doppler gradients are asymptomatic. Also, pressure recovery is a factor to consider in patients with atypical symptomatology and high Doppler gradients when one must decide on valvular replacement.

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.

Normal limits for left ventricular ejection fraction and volumes determined by gated single photon emission computed tomography - a comparison between two quantification methods

To compare gender‐related normal limits for left ventricular (LV) ejection fraction (EF), end‐diastolic and end‐systolic volumes (EDV and ESV), obtained using two myocardial perfusion‐gated single photon emission computed tomography (SPECT) quantification methods. A total of 185 patients were retrospectively selected from a consecutive series of patients examined for coronary artery disease (CAD) or for management of known CAD. Patients were included in the study group if they had normal or probably normal results with stress and rest perfusion imaging and if the combined interpretation of perfusion studies and gated rest studies showed no signs or suspicion of myocardial infarction. The gated SPECT studies were performed using a 2‐day stress/gated rest Tc‐99m sestamibi protocol. All patient studies were processed using CAFU and quantitative‐gated SPECT (QGS), the two software packages for quantification of gated SPECT images. The lower normal limits for EF were higher for CAFU compared with QGS for both women (59% versus 53%) and men (54% versus 47%). The upper normal limits for EDV were also higher for CAFU compared with QGS for both women (133 versus 107 ml) and men (182 versus 161 ml). The differences between the software packages were small for ESV (women 44 versus 44 ml; men 69 versus 74 ml). Gender‐specific normal limits need to be applied for LV EF and volumes determined by gated SPECT. Separate criteria for abnormal LV EF and EDV need to be used for women and men depending on the software package used.

Evaluation of a decision support system for interpretation of myocardial perfusion gated SPECT.

PurposeWe have recently presented a decision support system for interpreting myocardial perfusion scintigraphy (MPS). In this study, we wanted to evaluate the system in a separate hospital from where it was trained and to compare it with a quantification software package.MethodsA completely automated method based on neural networks was trained for the interpretation of MPS regarding myocardial ischaemia and infarction using 418 MPS from one hospital. Features from each examination describing rest and stress perfusion, regional and global function were used as inputs to different neural networks. After the training session, the system was evaluated using 532 MPS from another hospital. The test images were also processed with the quantification software package Emory Cardiac Toolbox (ECTb). The images were interpreted by experienced clinicians at both the training and the test hospital, regarding the presence or absence of myocardial ischaemia and/or infarction and these interpretations were used as gold standard.ResultsThe neural network showed a sensitivity of 90% and a specificity of 85% for myocardial ischaemia. The specificity for the ECTb was 46% (p < 0.001), measured at the same sensitivity. The neural network sensitivity for myocardial infarction was 89% and the specificity 96%. The corresponding specificity for the ECTb was 54% (p < 0.001).ConclusionA decision support system based on neural networks presents interpretations more similar to experienced clinicians compared to a conventional automated quantification software package. This study shows the feasibility of disseminating the expertise of experienced clinicians to less experienced physicians by the use of neural networks.

Heterogeneity of microsphere distribution in resected liver and tumour tissue following selective intrahepatic radiotherapy.

BackgroundSelective arterial radioembolisation of liver tumours has increased, because of encouraging efficacy reports; however, therapeutic parameters used in external beam therapy are not applicable for understanding and predicting potential toxicity and efficacy, necessitating further studies of the physical and biological characteristics of radioembolisation. The aim was to characterise heterogeneity in the distribution of microspheres on a therapeutically relevant geometric scale considering the range of yttrium-90 (90Y) β-particles.MethodsTwo patients with intrahepatic cholangiocarcinoma, marginally resectable, were treated by selective arterial embolisation with 90Y resin microspheres (SIRTEX®), followed 9 days post-infusion by resection, including macroscopic tumour tissue and surrounding normal liver parenchyma. Formalin-fixed, sectioned resected tissues were exposed to autoradiographic films, or tissue biopsies of various dimensions were punched out for activity measurements and microscopy.ResultsAutoradiography and activity measurements revealed a higher activity in tumour tissue compared to normal liver parenchyma. Heterogeneity in activity distribution was evident in both normal liver and tumour tissue. Activity measurements were analysed in relation to the sample mass (5 to 422 mg), and heterogeneities were detected by statistical means; the larger the tissue biopsies, the smaller was the coefficient of variation. The skewness of the activity distributions increased with decreasing biopsy mass.ConclusionsThe tissue activity distributions in normal tissue were heterogeneous on a relevant geometric scale considering the range of the ionising electrons. Given the similar and repetitive structure of the liver parenchyma, this finding could partly explain the tolerance of a relatively high mean absorbed dose to the liver parenchyma from β-particles.

A novel statistical analysis method to improve the detection of hepatic foci of 111 In-octreotide in SPECT/CT imaging

BackgroundLow uptake ratios, high noise, poor resolution, and low contrast all combine to make the detection of neuroendocrine liver tumours by 111In-octreotide single photon emission tomography (SPECT) imaging a challenge. The aim of this study was to develop a segmentation analysis method that could improve the accuracy of hepatic neuroendocrine tumour detection.MethodsOur novel segmentation was benchmarked by a retrospective analysis of patients categorized as either 111In-octreotide positive (111In-octreotide(+)) or 111In-octreotide negative (111In-octreotide(−)) for liver tumours. Following a 3-year follow-up period, involving multiple imaging modalities, we further segregated 111In-octreotide-negative patients into two groups: one with no confirmed liver tumours (111In-octreotide(−)/radtech(−)) and the other, now diagnosed with liver tumours (111In-octreotide(−)/radtech(+)). We retrospectively applied our segmentation analysis to see if it could have detected these previously missed tumours using 111In-octreotide. Our methodology subdivided the liver and determined normalized numbers of uptake foci (nNUF), at various threshold values, using a connected-component labelling algorithm. Plots of nNUF against the threshold index (ThI) were generated. ThI was defined as follows: ThI = (cmax − cthr)/cmax, where cmax is the maximal threshold value for obtaining at least one, two voxel sized, uptake focus; cthr is the voxel threshold value. The maximal divergence between the nNUF values for 111In-octreotide(−)/radtech(−), and 111In-octreotide(+) livers, was used as the optimal nNUF value for tumour detection. We also corrected for any influence of the mean activity concentration on ThI. The nNUF versus ThI method (nNUFTI) was then used to reanalyze the 111In-octreotide(−)/radtech(−) and 111In-octreotide(−)/radtech(+) groups.ResultsOf a total of 53 111In-octreotide(−) patients, 40 were categorized as 111In-octreotide(−)/radtech(−) and 13 as 111In-octreotide(−)/radtech(+) group. Optimal separation of the nNUF values for 111In-octreotide(−)/radtech(−) and 111In-octreotide(+) groups was defined at the nNUF value of 0.25, to the right of the bell shaped nNUFTI curve. ThIs at this nNUF value were dependent on the mean activity concentration and therefore normalized to generate nThI; a significant difference in nThI values was found between the 111In-octreotide(−)/radtech(−) and the 111In-octreotide(−)/radtech(+) groups (P < 0.01). As a result, four of the 13 111In-octreotide(−)/radtech(+) livers were redesigned as 111In-octreotide(+).ConclusionsThe nNUFTI method has the potential to improve the diagnosis of liver tumours using 111In-octreotide.

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.