| 1. |
- Bratulic, Sinisa, 1981, et al.
(författare)
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Noninvasive detection of any-stage cancer using free glycosaminoglycans.
- 2022
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Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 1091-6490 .- 0027-8424. ; 119:50
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Tidskriftsartikel (refereegranskat)abstract
- Cancer mortality is exacerbated by late-stage diagnosis. Liquid biopsies based on genomic biomarkers can noninvasively diagnose cancers. However, validation studies have reported ~10% sensitivity to detect stage I cancer in a screening population and specific types, such as brain or genitourinary tumors, remain undetectable. We investigated urine and plasma free glycosaminoglycan profiles (GAGomes) as tumor metabolism biomarkers for multi-cancer early detection (MCED) of 14 cancer types using 2,064 samples from 1,260 cancer or healthy subjects. We observed widespread cancer-specific changes in biofluidic GAGomes recapitulated in an in vivo cancer progression model. We developed three machine learning models based on urine (Nurine = 220 cancer vs. 360 healthy) and plasma (Nplasma = 517 vs. 425) GAGomes that can detect any cancer with an area under the receiver operating characteristic curve of 0.83-0.93 with up to 62% sensitivity to stage I disease at 95% specificity. Undetected patients had a 39 to 50% lower risk of death. GAGomes predicted the putative cancer location with 89% accuracy. In a validation study on a screening-like population requiring ≥ 99% specificity, combined GAGomes predicted any cancer type with poor prognosis within 18 months with 43% sensitivity (21% in stage I; N = 121 and 49 cases). Overall, GAGomes appeared to be powerful MCED metabolic biomarkers, potentially doubling the number of stage I cancers detectable using genomic biomarkers.
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| 2. |
- Polymeri, Erini, et al.
(författare)
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Artificial Intelligence-Based Organ Delineation for Radiation Treatment Planning of Prostate Cancer on Computed Tomography
- 2024
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Ingår i: Advances in Radiation Oncology. - 2452-1094. ; 9:3
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Meticulous manual delineations of the prostate and the surrounding organs at risk are necessary for prostate cancer radiation therapy to avoid side effects to the latter. This process is time consuming and hampered by inter- and intraobserver variability, all of which could be alleviated by artificial intelligence (AI). This study aimed to evaluate the performance of AI compared with manual organ delineations on computed tomography (CT) scans for radiation treatment planning. Methods and Materials: Manual delineations of the prostate, urinary bladder, and rectum of 1530 patients with prostate cancer who received curative radiation therapy from 2006 to 2018 were included. Approximately 50% of those CT scans were used as a training set, 25% as a validation set, and 25% as a test set. Patients with hip prostheses were excluded because of metal artifacts. After training and fine-tuning with the validation set, automated delineations of the prostate and organs at risk were obtained for the test set. Sørensen-Dice similarity coefficient, mean surface distance, and Hausdorff distance were used to evaluate the agreement between the manual and automated delineations. Results: The median Sørensen-Dice similarity coefficient between the manual and AI delineations was 0.82, 0.95, and 0.88 for the prostate, urinary bladder, and rectum, respectively. The median mean surface distance and Hausdorff distance were 1.7 and 9.2 mm for the prostate, 0.7 and 6.7 mm for the urinary bladder, and 1.1 and 13.5 mm for the rectum, respectively. Conclusions: Automated CT-based organ delineation for prostate cancer radiation treatment planning is feasible and shows good agreement with manually performed contouring.
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| 3. |
- Abuhasanein, Suleiman, et al.
(författare)
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A novel model of artificial intelligence based automated image analysis of CT urography to identify bladder cancer in patients investigated for macroscopic hematuria
- 2024
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Ingår i: Scandinavian Journal of Urology. - : Medical Journal Sweden AB. - 2168-1805 .- 2168-1813. ; 59, s. 90-97
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: To evaluate whether artificial intelligence (AI) based automatic image analysis utilising convolutional neural networks (CNNs) can be used to evaluate computed tomography urography (CTU) for the presence of urinary bladder cancer (UBC) in patients with macroscopic hematuria. METHODS: Our study included patients who had undergone evaluation for macroscopic hematuria. A CNN-based AI model was trained and validated on the CTUs included in the study on a dedicated research platform (Recomia.org). Sensitivity and specificity were calculated to assess the performance of the AI model. Cystoscopy findings were used as the reference method. RESULTS: The training cohort comprised a total of 530 patients. Following the optimisation process, we developed the last version of our AI model. Subsequently, we utilised the model in the validation cohort which included an additional 400 patients (including 239 patients with UBC). The AI model had a sensitivity of 0.83 (95% confidence intervals [CI], 0.76-0.89), specificity of 0.76 (95% CI 0.67-0.84), and a negative predictive value (NPV) of 0.97 (95% CI 0.95-0.98). The majority of tumours in the false negative group (n = 24) were solitary (67%) and smaller than 1 cm (50%), with the majority of patients having cTaG1-2 (71%). CONCLUSIONS: We developed and tested an AI model for automatic image analysis of CTUs to detect UBC in patients with macroscopic hematuria. This model showed promising results with a high detection rate and excessive NPV. Further developments could lead to a decreased need for invasive investigations and prioritising patients with serious tumours.
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| 4. |
- Abuhasanein, Suleiman, et al.
(författare)
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Computed tomography urography with corticomedullary phase can exclude urinary bladder cancer with high accuracy
- 2022
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Ingår i: Bmc Urology. - : Springer Science and Business Media LLC. - 1471-2490. ; 22:1
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Tidskriftsartikel (refereegranskat)abstract
- Background To evaluate the diagnostic accuracy of computed tomography-urography (CTU) to rule out urinary bladder cancer (UBC) and whether patients thereby could omit cystoscopy. Methods All patients evaluated for macroscopic hematuria with CTU with cortico-medullary phase (CMP) and cystoscopy at our institute between 1(st) November 2016 and 31(st) December 2019 were included. From this study cohort a study group consisting of all UBC patients and a control group of 113 patients randomly selected from all patients in the study cohort without UBC. Two radiologists blinded to all clinical data reviewed the CTUs independently. CTUs were categorized as positive, negative or indeterminate. Diagnostic accuracy and proportion of potential omittable cystoscopies were calculated for the study cohort by generalizing the results from the study group. Results The study cohort consisted of 2195 patients, 297 of which were in the study group (UBC group, n = 207 and control group, n = 90). Inter-rater reliability was high (kappa 0.84). Evaluation of CTUs showed that 174 patients were assesessed as positive (showing UBC), 46 patients as indeterminate (not showing UBC but with limited quality of CTU), and 77 patients as negative (not showing UBC with good quality of CTU). False negative rate was 0.07 (95%, CI 0.04-0.12), false positive rate was 0.01 (95% CI 0.0-0.07) and negative predictive value was 0.99 (95% CI 0.92-1.0). The area under the curve was 0.93 (95% CI 0.90-0.96). Only 2.9% (3/102) with high-risk tumors and 11% (12/105) with low- or intermediate-risk tumors had a false negative CTU. Cystoscopy could potentially have been omitted in 57% (1260/2195) of all evaluations. Conclusions CTU with CMP can exclude UBC with high accuracy. In case of negative CTU, it might be reasonable to omit cystoscopy, but future confirmative studies with possibly refined technique are needed.
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| 5. |
- 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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| 6. |
- 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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| 7. |
- Godtman, Rebecka Arnsrud, 1981, et al.
(författare)
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Development and validation of a prediction model for identifying men with intermediate- or high-risk prostate cancer for whom bone imaging is unnecessary: a nation-wide population-based study
- 2019
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Ingår i: Scandinavian Journal of Urology. - : Medical Journals Sweden AB. - 2168-1805 .- 2168-1813. ; 53:6, s. 378-384
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Tidskriftsartikel (refereegranskat)abstract
- Objective: To develop and validate a nomogram that identifies men for whom bone scan is unnecessary. Material and methods: The study datasets were derived from the National Prostate Cancer Register (NPCR) of Sweden. All men in the NPCR <= 80 years of age who were diagnosed with intermediate- or high-risk prostate cancer and who had pretreatment bone imaging (Tc-99m MDP scintigraphy, plain x-ray, computed tomography, magnetic resonance imaging, and/or positron emission tomography fused with computed tomography) were included. Men diagnosed from 2015-2016 formed a development dataset and men diagnosed in 2017 formed a validation dataset. Outcome was metastasis on bone imaging as registered in NPCR. Multivariable logistic regression was used to develop a nomogram. Results: In the development dataset 482/5084 men (10%) had bone metastasis, the corresponding percentage in the validation dataset was 282/2554 (11%). Gleason grade group, clinical T stage, and prostate-specific antigen were included in the final model. Discrimination and calibration were satisfactory in both the development (AUC 0.80, 95% CI 0.78-0.82) and validation dataset (AUC 0.80, 95% CI, 0.77-0.82). Compared with using the EAU guidelines' recommendation for selecting men for imaging, using the nomogram with a cut-off at 4% chance of bone metastasis, would have avoided imaging in 519/2068 men (25%) and miss bone metastasis in 10/519 (2%) men in the validation dataset. Conclusion: By use of our nomogram, bone scans of men with prostate cancer can be avoided in a large proportion of men.
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| 8. |
- Polymeri, Erini, et al.
(författare)
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Artificial intelligence-based measurements of PET/CT imaging biomarkers are associated with disease-specific survival of high-risk prostate cancer patients
- 2021
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Ingår i: Scandinavian Journal of Urology. - : Medical Journals Sweden AB. - 2168-1805 .- 2168-1813. ; 55:6, s. 427-433
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Tidskriftsartikel (refereegranskat)abstract
- Objective Artificial intelligence (AI) offers new opportunities for objective quantitative measurements of imaging biomarkers from positron-emission tomography/computed tomography (PET/CT). Clinical image reporting relies predominantly on observer-dependent visual assessment and easily accessible measures like SUVmax, representing lesion uptake in a relatively small amount of tissue. Our hypothesis is that measurements of total volume and lesion uptake of the entire tumour would better reflect the disease`s activity with prognostic significance, compared with conventional measurements. Methods An AI-based algorithm was trained to automatically measure the prostate and its tumour content in PET/CT of 145 patients. The algorithm was then tested retrospectively on 285 high-risk patients, who were examined using F-18-choline PET/CT for primary staging between April 2008 and July 2015. Prostate tumour volume, tumour fraction of the prostate gland, lesion uptake of the entire tumour, and SUVmax were obtained automatically. Associations between these measurements, age, PSA, Gleason score and prostate cancer-specific survival were studied, using a Cox proportional-hazards regression model. Results Twenty-three patients died of prostate cancer during follow-up (median survival 3.8 years). Total tumour volume of the prostate (p = 0.008), tumour fraction of the gland (p = 0.005), total lesion uptake of the prostate (p = 0.02), and age (p = 0.01) were significantly associated with disease-specific survival, whereas SUVmax (p = 0.2), PSA (p = 0.2), and Gleason score (p = 0.8) were not. Conclusion AI-based assessments of total tumour volume and lesion uptake were significantly associated with disease-specific survival in this patient cohort, whereas SUVmax and Gleason scores were not. The AI-based approach appears well-suited for clinically relevant patient stratification and monitoring of individual therapy.
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| 9. |
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| 10. |
- Ying, T. M., et al.
(författare)
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Automated artificial intelligence-based analysis of skeletal muscle volume predicts overall survival after cystectomy for urinary bladder cancer
- 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
- Background Radical cystectomy for urinary bladder cancer is a procedure associated with a high risk of complications, and poor overall survival (OS) due to both patient and tumour factors. Sarcopenia is one such patient factor. We have developed a fully automated artificial intelligence (AI)-based image analysis tool for segmenting skeletal muscle of the torso and calculating the muscle volume. Methods All patients who have undergone radical cystectomy for urinary bladder cancer 2011-2019 at Sahlgrenska University Hospital, and who had a pre-operative computed tomography of the abdomen within 90 days of surgery were included in the study. All patients CT studies were analysed with the automated AI-based image analysis tool. Clinical data for the patients were retrieved from the Swedish National Register for Urinary Bladder Cancer. Muscle volumes dichotomised by the median for each sex were analysed with Cox regression for OS and logistic regression for 90-day high-grade complications. The study was approved by the Swedish Ethical Review Authority (2020-03985). Results Out of 445 patients who underwent surgery, 299 (67%) had CT studies available for analysis. The automated AI-based tool failed to segment the muscle volume in seven (2%) patients. Cox regression analysis showed an independent significant association with OS (HR 1.62; 95% CI 1.07-2.44; p = 0.022). Logistic regression did not show any association with high-grade complications. Conclusion The fully automated AI-based CT image analysis provides a low-cost and meaningful clinical measure that is an independent biomarker for OS following radical cystectomy.
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