| 1. |
- Anand, Aseem, et al.
(författare)
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Assessing Radiographic Response to 223Ra with an Automated Bone Scan Index in Metastatic Castration-Resistant Prostate Cancer Patients
- 2020
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Ingår i: Journal of Nuclear Medicine. - : Society of Nuclear Medicine. - 0161-5505 .- 2159-662X .- 1535-5667. ; 61:5, s. 671-675
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Tidskriftsartikel (refereegranskat)abstract
- For effective clinical management of patients being treated with 223Ra, there is a need for radiographic response biomarkers to minimize disease progression and to stratify patients for subsequent treatment options. The objective of this study was to evaluate an automated bone scan index (aBSI) as a quantitative assessment of bone scans for radiographic response in patients with metastatic castration-resistant prostate cancer (mCRPC). Methods: In a multicenter retrospective study, bone scans from patients with mCRPC treated with monthly injections of 223Ra were collected from 7 hospitals in Sweden. Patients with available bone scans before treatment with 223Ra and at treatment discontinuation were eligible for the study. The aBSI was generated at baseline and at treatment discontinuation. The Spearman rank correlation was used to correlate aBSI with the baseline covariates: alkaline phosphatase (ALP) and prostate-specific antigen (PSA). The Cox proportional-hazards model and Kaplan-Meier curve were used to evaluate the association of covariates at baseline and their change at treatment discontinuation with overall survival (OS). The concordance index (C-index) was used to evaluate the discriminating strength of covariates in predicting OS. Results: Bone scan images at baseline were available from 156 patients, and 67 patients had both a baseline and a treatment discontinuation bone scan (median, 5 doses; interquartile range, 3-6 doses). Baseline aBSI (median, 4.5; interquartile range, 2.4-6.5) was moderately correlated with ALP (r = 0.60, P < 0.0001) and with PSA (r = 0.38, P = 0.003). Among baseline covariates, aBSI (P = 0.01) and ALP (P = 0.001) were significantly associated with OS, whereas PSA values were not (P = 0.059). After treatment discontinuation, 36% (24/67), 80% (54/67), and 13% (9/67) of patients demonstrated a decline in aBSI, ALP, and PSA, respectively. As a continuous variable, the relative change in aBSI after treatment, compared with baseline, was significantly associated with OS (P < 0.0001), with a C-index of 0.67. Median OS in patients with both aBSI and ALP decline (median, 134 wk) was significantly longer than in patients with ALP decline only (median, 77 wk; P = 0.029). Conclusion: Both aBSI at baseline and its change at treatment discontinuation were significant parameters associated with OS. The study warrants prospective validation of aBSI as a quantitative imaging response biomarker to predict OS in patients with mCRPC treated with 223Ra.
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| 2. |
- Bajc, Marika, et al.
(författare)
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Assessment of Ventilation and Perfusion in Patients with COVID-19 Discloses Unique Information of Pulmonary Function to a Clinician : Case Reports of V/P SPECT
- 2021
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Ingår i: Clinical Medicine Insights: Circulatory, Respiratory and Pulmonary Medicine. - : SAGE Publications. - 1179-5484. ; 15
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Tidskriftsartikel (refereegranskat)abstract
- V/P SPECT from 4 consecutive patients with COVID-19 suggests that ventilation and perfusion images may be applied to diagnose or exclude pulmonary embolism, verify nonsegmental diversion of perfusion from the ventilated areas (dead space ventilation) that may represent inflammation of the pulmonary vasculature, detect the reversed mismatch of poor ventilation and better preserved perfusion (shunt perfusion) in bilateral pulmonary inflammation and indicate redistribution of lung perfusion (antigravitational hyperperfusion) due to cardiac congestion. V/P mismatch and reversed mismatch may be extensive enough to diminish dramatically preserved matching ventilation/perfusion and to induce severe hypoxemia in COVID-19.
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| 3. |
- Borrelli, P., et al.
(författare)
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AI-based detection of lung lesions in F-18 FDG PET-CT from lung cancer patients
- 2021
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Ingår i: Ejnmmi Physics. - : Springer Science and Business Media LLC. - 2197-7364. ; 8:1
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Tidskriftsartikel (refereegranskat)abstract
- Background[F-18]-fluorodeoxyglucose (FDG) positron emission tomography with computed tomography (PET-CT) is a well-established modality in the work-up of patients with suspected or confirmed diagnosis of lung cancer. Recent research efforts have focused on extracting theragnostic and textural information from manually indicated lung lesions. Both semi-automatic and fully automatic use of artificial intelligence (AI) to localise and classify FDG-avid foci has been demonstrated. To fully harness AI's usefulness, we have developed a method which both automatically detects abnormal lung lesions and calculates the total lesion glycolysis (TLG) on FDG PET-CT.MethodsOne hundred twelve patients (59 females and 53 males) who underwent FDG PET-CT due to suspected or for the management of known lung cancer were studied retrospectively. These patients were divided into a training group (59%; n = 66), a validation group (20.5%; n = 23) and a test group (20.5%; n = 23). A nuclear medicine physician manually segmented abnormal lung lesions with increased FDG-uptake in all PET-CT studies. The AI-based method was trained to segment the lesions based on the manual segmentations. TLG was then calculated from manual and AI-based measurements, respectively and analysed with Bland-Altman plots.ResultsThe AI-tool's performance in detecting lesions had a sensitivity of 90%. One small lesion was missed in two patients, respectively, where both had a larger lesion which was correctly detected. The positive and negative predictive values were 88% and 100%, respectively. The correlation between manual and AI TLG measurements was strong (R-2 = 0.74). Bias was 42 g and 95% limits of agreement ranged from -736 to 819 g. Agreement was particularly high in smaller lesions.ConclusionsThe AI-based method is suitable for the detection of lung lesions and automatic calculation of TLG in small- to medium-sized tumours. In a clinical setting, it will have an added value due to its capability to sort out negative examinations resulting in prioritised and focused care on patients with potentially malignant lesions.
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| 4. |
- Borrelli, P., et al.
(författare)
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Artificial intelligence-aided CT segmentation for body composition analysis: a validation study
- 2021
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Ingår i: European Radiology Experimental. - : Springer Science and Business Media LLC. - 2509-9280. ; 5:1
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundBody composition is associated with survival outcome in oncological patients, but it is not routinely calculated. Manual segmentation of subcutaneous adipose tissue (SAT) and muscle is time-consuming and therefore limited to a single CT slice. Our goal was to develop an artificial-intelligence (AI)-based method for automated quantification of three-dimensional SAT and muscle volumes from CT images.MethodsEthical approvals from Gothenburg and Lund Universities were obtained. Convolutional neural networks were trained to segment SAT and muscle using manual segmentations on CT images from a training group of 50 patients. The method was applied to a separate test group of 74 cancer patients, who had two CT studies each with a median interval between the studies of 3days. Manual segmentations in a single CT slice were used for comparison. The accuracy was measured as overlap between the automated and manual segmentations.ResultsThe accuracy of the AI method was 0.96 for SAT and 0.94 for muscle. The average differences in volumes were significantly lower than the corresponding differences in areas in a single CT slice: 1.8% versus 5.0% (p <0.001) for SAT and 1.9% versus 3.9% (p < 0.001) for muscle. The 95% confidence intervals for predicted volumes in an individual subject from the corresponding single CT slice areas were in the order of 20%.Conclusions The AI-based tool for quantification of SAT and muscle volumes showed high accuracy and reproducibility and provided a body composition analysis that is more relevant than manual analysis of a single CT slice.
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| 5. |
- Borrelli, Pablo, et al.
(författare)
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Artificial intelligence-based detection of lymph node metastases by PET/CT predicts prostate cancer-specific survival
- 2021
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Ingår i: Clinical Physiology and Functional Imaging. - : Wiley. - 1475-0961 .- 1475-097X. ; 41:1, s. 62-67
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Tidskriftsartikel (refereegranskat)abstract
- Introduction Lymph node metastases are a key prognostic factor in prostate cancer (PCa), but detecting lymph node lesions from PET/CT images is a subjective process resulting in inter-reader variability. Artificial intelligence (AI)-based methods can provide an objective image analysis. We aimed at developing and validating an AI-based tool for detection of lymph node lesions. Methods A group of 399 patients with biopsy-proven PCa who had undergone(18)F-choline PET/CT for staging prior to treatment were used to train (n = 319) and test (n = 80) the AI-based tool. The tool consisted of convolutional neural networks using complete PET/CT scans as inputs. In the test set, the AI-based lymph node detections were compared to those of two independent readers. The association with PCa-specific survival was investigated. Results The AI-based tool detected more lymph node lesions than Reader B (98 vs. 87/117;p = .045) using Reader A as reference. AI-based tool and Reader A showed similar performance (90 vs. 87/111;p = .63) using Reader B as reference. The number of lymph node lesions detected by the AI-based tool, PSA, and curative treatment was significantly associated with PCa-specific survival. Conclusion This study shows the feasibility of using an AI-based tool for automated and objective interpretation of PET/CT images that can provide assessments of lymph node lesions comparable with that of experienced readers and prognostic information in PCa patients.
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| 6. |
- Frennered, Anna, et al.
(författare)
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Patterns of pathologic lymph nodes in anal cancer : a PET-CT-based analysis with implications for radiotherapy treatment volumes
- 2021
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Ingår i: BMC Cancer. - : Springer Science and Business Media LLC. - 1471-2407. ; 21:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: This study investigates the patterns of PET-positive lymph nodes (LNs) in anal cancer. The aim was to provide information that could inform future anal cancer radiotherapy contouring guidelines. Methods: The baseline [18F]-FDG PET-CTs of 190 consecutive anal cancer patients were retrospectively assessed. LNs with a Deauville score (DS) of ≥3 were defined as PET-positive. Each PET-positive LN was allocated to a LN region and a LN sub-region; they were then mapped on a standard anatomy reference CT. The association between primary tumor localization and PET-positive LNs in different regions were analyzed. Results: PET-positive LNs (n = 412) were identified in 103 of 190 patients (54%). Compared to anal canal tumors with extension into the rectum, anal canal tumors with perianal extension more often had inguinal (P < 0.001) and less often perirectal (P < 0.001) and internal iliac (P < 0.001) PET-positive LNs. Forty-two patients had PET-positive LNs confined to a solitary region, corresponding to first echelon nodes. The most common solitary LN region was inguinal (25 of 42; 60%) followed by perirectal (26%), internal iliac (10%), and external iliac (2%). No PET-positive LNs were identified in the ischiorectal fossa or in the inguinal area located posterolateral to deep vessels. Skip metastases above the bottom of the sacroiliac joint were quite rare. Most external iliac PET-positive LNs were located posterior to the external iliac vein; only one was located in the lateral external iliac sub-region. Conclusions: The results support some specific modifications to the elective clinical target volume (CTV) in anal cancer. These changes would lead to reduced volumes of normal tissue being irradiated, which could contribute to a reduction in radiation side-effects.
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| 7. |
- Lindgren Belal, Sarah, et al.
(författare)
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Deep learning-based evaluation of normal bone marrow activity in 18F-NaF PET/CT in patients with prostate cancer
- 2020
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Ingår i: Insights into Imaging. - : Springer Science and Business Media LLC. - 1869-4101. ; 11:Suppl. 1, s. 349-350
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Konferensbidrag (refereegranskat)abstract
- Purpose: Bone marrow is the primary site of skeletal metastases in prostate cancer. 18F-sodium fluoride (NaF) can be used to detect malignant activity, but also identifies irrelevant degenerative cortical uptake. Normal radiotracer activity in solely the marrow has yet to be described and could be a first step towards automated tumor burden calculation as SUV thresholds. We aimed to investigate normal activity of 18F-NaF in whole bone and bone marrow in patients with localized prostate cancer.Methods and materials: 18F-NaF PET/CT scans from 87 patients with high-risk prostate cancer from two centers were retrospectively analyzed. All patients had a recent negative or inconclusive bone scan. In the first center, PET scan was acquired 1-1.5 hours after i.v. injection of 4 MBq/kg 18F-NaF on an integrated PET/CT system (Gemini TF, Philips Medical Systems) (53/87). In the second center, scanning was performed 1 hour after i.v. injection of 3 MBq/kg 18F-NaF on an integrated PET/CT system (Discovery ST, GE Healthcare) (34/87). CT scans were obtained in immediate connection to the PET scan. Automated segmentations of vertebrae, pelvis, femora, humeri and sternum were performed in the CT scans using a deep learning-based method. Bone <7 mm from skeletal surfaces was removed to isolate the marrow. SUV was measured within the remaining area in the PET scan.Results: SUVmax and SUVmean in the whole bone and bone marrow of the different regions were presented.Conclusion: We present a deep-learning approach for evaluation of normal radiotracer activity in whole bone and bone marrow. Knowledge about radiotracer uptake in the normal bone prior to cancerous involvement is a necessary first step for subsequent tumor assessment and could be of value in the implementation of future tracers.
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| 8. |
- Ly, John, et al.
(författare)
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Post-reconstruction enhancement of [18F]FDG PET images with a convolutional neural network
- 2021
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Ingår i: EJNMMI Research. - : Springer Science and Business Media LLC. - 2191-219X. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: The aim of the study was to develop and test an artificial intelligence (AI)-based method to improve the quality of [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET) images. Methods: A convolutional neural network (CNN) was trained by using pairs of excellent (acquisition time of 6 min/bed position) and standard (acquisition time of 1.5 min/bed position) or sub-standard (acquisition time of 1 min/bed position) images from 72 patients. A test group of 25 patients was used to validate the CNN qualitatively and quantitatively with 5 different image sets per patient: 4 min/bed position, 1.5 min/bed position with and without CNN, and 1 min/bed position with and without CNN. Results: Difference in hotspot maximum or peak standardized uptake value between the standard 1.5 min and 1.5 min CNN images fell short of significance. Coefficient of variation, the noise level, was lower in the CNN-enhanced images compared with standard 1 min and 1.5 min images. Physicians ranked the 1.5 min CNN and the 4 min images highest regarding image quality (noise and contrast) and the standard 1 min images lowest. Conclusions: AI can enhance [18F]FDG-PET images to reduce noise and increase contrast compared with standard images whilst keeping SUVmax/peak stability. There were significant differences in scoring between the 1.5 min and 1.5 min CNN image sets in all comparisons, the latter had higher scores in noise and contrast. Furthermore, difference in SUVmax and SUVpeak fell short of significance for that pair. The improved image quality can potentially be used either to provide better images to the nuclear medicine physicians or to reduce acquisition time/administered activity.
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| 9. |
- Minarik, David, et al.
(författare)
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Denoising of Scintillation Camera Images Using a Deep Convolutional Neural Network: A Monte Carlo Simulation Approach
- 2020
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Ingår i: Journal of Nuclear Medicine. - : Society of Nuclear Medicine. - 0161-5505 .- 2159-662X. ; 61:2, s. 298-303
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Tidskriftsartikel (refereegranskat)abstract
- Scintillation camera images contain a large amount of Poisson noise. We have investigated whether noise can be removed in whole-body bone scans using convolutional neural networks (CNNs) trained with sets of noisy and noiseless images obtained by Monte Carlo simulation. Methods: Three CNNs were generated using 3 different sets of training images: simulated bone scan images, images of a cylindric phantom with hot and cold spots, and a mix of the first two. Each training set consisted of 40,000 noiseless and noisy image pairs. The CNNs were evaluated with simulated images of a cylindric phantom and simulated bone scan images. The mean squared error between filtered and true images was used as difference metric, and the coefficient of variation was used to estimate noise reduction. The CNNs were compared with gaussian and median filters. A clinical evaluation was performed in which the ability to detect metastases for CNN- and gaussian-filtered bone scans with half the number of counts was compared with standard bone scans. Results: The best CNN reduced the coefficient of variation by, on average, 92%, and the best standard filter reduced the coefficient of variation by 88%. The best CNN gave a mean squared error that was on average 68% and 20% better than the best standard filters, for the cylindric and bone scan images, respectively. The best CNNs for the cylindric phantom and bone scans were the dedicated CNNs. No significant differences in the ability to detect metastases were found between standard, CNN-, and gaussian-filtered bone scans. Conclusion: Noise can be removed efficiently regardless of noise level with little or no resolution loss. The CNN filter enables reducing the scanning time by half and still obtaining good accuracy for bone metastasis assessment.
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| 10. |
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