| 1. |
- 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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| 2. |
- 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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| 3. |
- Ying, Thomas, et al.
(författare)
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AI-based fully automatic image analysis: Optimal abdominal and thoracic segmentation volumes for estimating total muscle volume on computed tomography scans
- 2024
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Ingår i: Osteoporosis and Sarcopenia. - 2405-5255 .- 2405-5263. ; 10:2, s. 78-83
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Tidskriftsartikel (refereegranskat)abstract
- Evaluation of sarcopenia from computed tomography (CT) is often based on measuring skeletal muscle area on a single transverse slice. Automatic segmentation of muscle volume has a lower variance and may be a better proxy for the total muscle volume than single-slice areas. The aim of the study was to determine which abdominal and thoracic anatomical volumes were best at predicting the total muscle volume. Methods: A cloud-based artificial intelligence tool (recomia.org) was used to segment all skeletal muscle of the torso of 994 patients who had performed whole-torso CT 2008-2020 for various clinical indications. Linear regression models for several anatomical volumes and single-slice areas were compared with regard to predicting the total torso muscle volume. Results: The muscle volume from the tip of the coccyx and 25 cm cranially was the best of the abdominal volumes and was significantly better than the L3 slice muscle area (R-2 0.935 vs 0.830, P < 0.0001). For thoracic volumes, the muscle volume between the top of the sternum to the lower bound of the Th12 vertebra showed the best correlation with the total volume, significantly better than the Th12 slice muscle area (R-2 0.892 vs 0.775, P < 0.0001). Adjusting for body height improved the correlation slightly for all measurements but did not significantly change the ordering. Conclusions: We identified muscle volumes that can be reliably segmented by automated image analysis which is superior to single slice areas in predicting total muscle volume.
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| 4. |
- Abuhasanein, Suleiman, et al.
(författare)
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Diagnostic value of repeated comprehensive investigation with CT urography and cystoscopy for recurrent macroscopic haematuria
- 2024
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Ingår i: BJUI Compass. - : John Wiley & Sons. - 2688-4526. ; 5:2, s. 253-260
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: To perform a descriptive analysis of a series of patients with recurrent macroscopic haematuria after a primary standard evaluation including computed tomography urography (CTU) and cystoscopy negative for urinary bladder cancer (UBC) and upper tract urothelial cancer (UTUC) and to identify potential factors associated with occurrence of recurrent macroscopic haematuria.Methods: All patients older than 50 years who underwent urological investigation for macroscopic haematuria with both cystoscopy and CTU 2015-2017 were retrospectively reviewed. A descriptive analysis of the primary and later investigations for recurrent macroscopic haematuria was performed. To investigate the association between explanatory variables and the occurrence of recurrent macroscopic haematuria, a Poisson regression analysis was performed.Results: A total of 1395 eligible individuals with primary standard investigation negative for UBC and UTUC were included. During a median follow-up of 6.2 (IQR 5.3-7) years, 248 (18%) patients had recurrent macroscopic haematuria, of whom six patients were diagnosed with UBC, two with prostate cancer, one with renal cell carcinoma and one had a suspected UTUC at the repeated investigation. Within 3 years, 148 patients (11%) experienced recurrent macroscopic haematuria, of whom two patients were diagnosed with low-grade UBC (TaG1-2), one with T2G3 UBC and one with low-risk prostate cancer. The presence of an indwelling catheter, use of antithrombotic medication, pathological findings at CTU or cystoscopy or history of pelvic radiotherapy were all statistically significant independent predictors for increased risk for recurrent macroscopic haematuria.Conclusion: In the case of recurrent macroscopic haematuria within 3 years of primary standard evaluation for urinary tract cancer, there was a low risk of later urological malignancies in patients initially negative for UBC and UTUC. Therefore, waiting 3 years before conducting another complete investigation in cases of recurrent macroscopic haematuria might be appropriate.
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| 5. |
- Abuhasanein, Suleiman, et al.
(författare)
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Shortened time to diagnosis for patients suspected of urinary bladder cancer managed in a standardized care pathway was associated with an improvement in tumour characteristics
- 2024
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Ingår i: BJUI COMPASS. - : WILEY. - 2688-4526. ; 5:2, s. 261-268
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Tidskriftsartikel (refereegranskat)abstract
- Objectives To evaluate whether the implementation of standardized care pathway (SCP) for patients with suspected urinary bladder cancer (UBC) was associated with changes in tumour characteristics. Additionally, the study aims to explore whether there was a shift in the selection of patients prioritized for immediate evaluation regarding suspicion of UBC.Materials and Methods The study included all patients diagnosed with UBC in the NU Hospital Group between 2010 and 2019. To evaluate changes associated with SCP, patients were divided into two diagnostic time periods, either before (2010-2015) or during (2016-2019) the implementation of the SCP. To evaluate which patients were prioritized for prompt evaluation within 13 days, logistic regression analysis was performed on all patients before and during SCP.Results Median time to transurethral resection of the tumour in urinary bladder (TURBT) decreased from 29 days (interquartile range [IQR] 16-48) before SCP to 12 days (IQR 8-19) during SCP (p < 0.001) with a clear break from 2016. The proportion of cT2 + tumours decreased during SCP from 26% to 20% (p = 0.035). In addition, tumours detected during SCP were smaller (p = 0.023), but with more multiple lesions (p = 0.055) and G3 tumours (p = 0.007). During SCP, there was no statistically significant difference between the groups of patients with TURBT within or after 13 days. In contrast, before SCP, a majority of the patients treated within 13 days had advanced tumours and were admitted from the emergency ward.Conclusions The implementation of an SCP for suspected UBC was associated with improved tumour characteristics. Interestingly, during SCP, there were no substantial differences in patients' or tumours' characteristics among those who underwent TURBT within or after 13 days. This indicates that the 13-day timeframe for TURBT might be prolonged, especially in less urgent cases in order to facilitate a prioritization of more severe cases with treatable disease.
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