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- Beijert, Irene J., et al.
(författare)
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International Opinions on Grading of Urothelial Carcinoma : A Survey Among European Association of Urology and International Society of Urological Pathology Members
- 2023
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Ingår i: European Urology Open Science. - 2666-1691. ; 52, s. 154-165
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Tidskriftsartikel (refereegranskat)abstract
- Background: Grade of non–muscle-invasive bladder cancer (NMIBC) is an important prognostic factor for progression. Currently, two World Health Organization (WHO) classification systems (WHO1973, categories: grade 1–3, and WHO2004 categories: papillary urothelial neoplasm of low malignant potential [PUNLMP], low-grade [LG], high-grade [HG] carcinoma) are used. Objective: To ask the European Association of Urology (EAU) and International Society of Urological Pathology (ISUP) members regarding their current practice and preferences of grading systems. Design, setting, and participants: A web-based, anonymous questionnaire with ten questions on grading of NMIBC was created. The members of EAU and ISUP were invited to complete an online survey by the end of 2021. Thirteen experts had previously answered the same questions. Outcome measurements and statistical analysis: The submitted answers from 214 ISUP members, 191 EAU members, and 13 experts were analyzed. Results and limitations: Currently, 53% use only the WHO2004 system and 40% use both systems. According to most respondents, PUNLMP is a rare diagnosis with management similar to Ta-LG carcinoma. The majority (72%) would consider reverting back to WHO1973 if grading criteria were more detailed. Separate reporting of WHO1973-G3 within WHO2004-HG would influence clinical decisions for Ta and/or T1 tumors according the majority (55%). Most respondents preferred a two-tier (41%) or a three-tier (41%) grading system. The current WHO2004 grading system is supported by a minority (20%), whereas nearly half (48%) supported a hybrid three- or four-tier grading system composed of both WHO1973 and WHO2004. The survey results of the experts were comparable with ISUP and EAU respondents. Conclusions: Both the WHO1973 and the WHO2004 grading system are still widely used. Even though opinions on the future of bladder cancer grading were strongly divided, there was limited support for WHO1973 and WHO2004 in their current formats, while the hybrid (three-tier) grading system with LG, HG-G2, and HG-G3 as categories could be considered the most promising alternative. Patient summary: Grading of non–muscle-invasive bladder cancer (NMIBC) is a matter of ongoing debate and lacks international consensus. We surveyed urologists and pathologists of European Association of Urology and International Society of Urological Pathology on their preferences regarding NMIBC grading to generate a multidisciplinary dialogue. Both the “old” World Health Organization (WHO) 1973 and the “new” WHO2004 grading schemes are still used widely. However, continuation of both the WHO1973 and the WHO2004 system showed limited support, while a hybrid grading system composed of both the WHO1973 and the WHO2004 classification system may be considered a promising alternative.
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| 2. |
- Egevad, Lars, et al.
(författare)
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Interobserver reproducibility of perineural invasion of prostatic adenocarcinoma in needle biopsies
- 2021
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Ingår i: Virchows Archiv. - : Springer Nature. - 0945-6317 .- 1432-2307. ; 478:6, s. 1109-1116
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Tidskriftsartikel (refereegranskat)abstract
- Numerous studies have shown a correlation between perineural invasion (PNI) in prostate biopsies and outcome. The reporting of PNI varies widely in the literature. While the interobserver variability of prostate cancer grading has been studied extensively, less is known regarding the reproducibility of PNI. A total of 212 biopsy cores from a population-based screening trial were included in this study (106 with and 106 without PNI according to the original pathology reports). The glass slides were scanned and circulated among four pathologists with a special interest in urological pathology for assessment of PNI. Discordant cases were stained by immunohistochemistry for S-100 protein. PNI was diagnosed by all four observers in 34.0% of cases, while 41.5% were considered to be negative for PNI. In 24.5% of cases, there was a disagreement between the observers. The kappa for interobserver variability was 0.67-0.75 (mean 0.73). The observations from one participant were compared with data from the original reports, and a kappa for intraobserver variability of 0.87 was achieved. Based on immunohistochemical findings among discordant cases, 88.6% had PNI while 11.4% did not. The most common diagnostic pitfall was the presence of bundles of stroma or smooth muscle. It was noted in a few cases that collagenous micronodules could be mistaken for a nerve. The distance between cancer and nerve was another cause of disagreement. Although the results suggest that the reproducibility of PNI may be greater than that of prostate cancer grading, there is still a need for improvement and standardization.
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| 5. |
- Kartasalo, Kimmo, et al.
(författare)
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Artificial Intelligence for Diagnosis and Gleason Grading of Prostate Cancer in Biopsies-Current Status and Next Steps
- 2021
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Ingår i: European Urology Focus. - : Elsevier. - 2405-4569. ; 7:4, s. 687-691
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Forskningsöversikt (refereegranskat)abstract
- Diagnosis and Gleason grading of prostate cancer in biopsies are critical for the clinical management of men with prostate cancer. Despite this, the high grading variability among pathologists leads to the potential for under-and overtreatment. Artificial intelligence (AI) systems have shown promise in assisting pathologists to perform Gleason grading, which could help address this problem. In this mini-review, we highlight studies reporting on the development of AI systems for cancer detection and Gleason grading, and discuss the progress needed for widespread clinical implementation, as well as anticipated future developments. Patient summary: This mini-review summarizes the evidence relating to the validation of artificial intelligence (AI)-assisted cancer detection and Gleason grading of prostate cancer in biopsies, and highlights the remaining steps required prior to its widespread clinical implementation. We found that, although there is strong evidence to show that AI is able to perform Gleason grading on par with experienced uropathologists, more work is needed to ensure the accuracy of results from AI systems in diverse settings across different patient populations, digitization platforms, and pathology laboratories.
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| 6. |
- Strom, Peter, et al.
(författare)
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Artificial intelligence for diagnosis and grading of prostate cancer in biopsies : a population-based, diagnostic study
- 2020
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Ingår i: The Lancet Oncology. - : Elsevier. - 1470-2045 .- 1474-5488. ; 21:2, s. 222-232
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundAn increasing volume of prostate biopsies and a worldwide shortage of urological pathologists puts a strain on pathology departments. Additionally, the high intra-observer and inter-observer variability in grading can result in overtreatment and undertreatment of prostate cancer. To alleviate these problems, we aimed to develop an artificial intelligence (AI) system with clinically acceptable accuracy for prostate cancer detection, localisation, and Gleason grading.MethodsWe digitised 6682 slides from needle core biopsies from 976 randomly selected participants aged 50–69 in the Swedish prospective and population-based STHLM3 diagnostic study done between May 28, 2012, and Dec 30, 2014 (ISRCTN84445406), and another 271 from 93 men from outside the study. The resulting images were used to train deep neural networks for assessment of prostate biopsies. The networks were evaluated by predicting the presence, extent, and Gleason grade of malignant tissue for an independent test dataset comprising 1631 biopsies from 246 men from STHLM3 and an external validation dataset of 330 biopsies from 73 men. We also evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology. We assessed discriminatory performance by receiver operating characteristics and tumour extent predictions by correlating predicted cancer length against measurements by the reporting pathologist. We quantified the concordance between grades assigned by the AI system and the expert urological pathologists using Cohen's kappa.FindingsThe AI achieved an area under the receiver operating characteristics curve of 0·997 (95% CI 0·994–0·999) for distinguishing between benign (n=910) and malignant (n=721) biopsy cores on the independent test dataset and 0·986 (0·972–0·996) on the external validation dataset (benign n=108, malignant n=222). The correlation between cancer length predicted by the AI and assigned by the reporting pathologist was 0·96 (95% CI 0·95–0·97) for the independent test dataset and 0·87 (0·84–0·90) for the external validation dataset. For assigning Gleason grades, the AI achieved a mean pairwise kappa of 0·62, which was within the range of the corresponding values for the expert pathologists (0·60–0·73).InterpretationAn AI system can be trained to detect and grade cancer in prostate needle biopsy samples at a ranking comparable to that of international experts in prostate pathology. Clinical application could reduce pathology workload by reducing the assessment of benign biopsies and by automating the task of measuring cancer length in positive biopsy cores. An AI system with expert-level grading performance might contribute a second opinion, aid in standardising grading, and provide pathology expertise in parts of the world where it does not exist.
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| 7. |
- Ström, Peter, et al.
(författare)
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Pathologist-Level Grading of Prostate Biospies with Artificial intelligence
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Background: An increasing volume of prostate biopsies and a world-wide shortage of uro-pathologists puts a strain on pathology departments. Additionally, the high intra- and inter-observer variability in grading can result in over- and undertreatment of prostate cancer. Artificial intelligence (AI) methods may alleviate these problems by assisting pathologists to reduce workload and harmonize grading. Methods: We digitized 6,682 needle biopsies from 976 participants in the population based STHLM3 diagnostic study to train deep neural networks for assessing prostate biopsies. The networks were evaluated by predicting the presence, extent, and Gleason grade of malignant tissue for an independent test set comprising 1,631 biopsies from 245 men. We additionally evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology. We assessed discriminatory performance by receiver operating characteristics (ROC) and tumor extent predictions by correlating predicted millimeter cancer length against measurements by the reporting pathologist. We quantified the concordance between grades assigned by the AI and the expert urological pathologists using Cohen's kappa. Results: The performance of the AI to detect and grade cancer in prostate needle biopsy samples was comparable to that of international experts in prostate pathology. The AI achieved an area under the ROC curve of 0.997 for distinguishing between benign and malignant biopsy cores, and 0.999 for distinguishing between men with or without prostate cancer. The correlation between millimeter cancer predicted by the AI and assigned by the reporting pathologist was 0.96. For assigning Gleason grades, the AI achieved an average pairwise kappa of 0.62. This was within the range of the corresponding values for the expert pathologists (0.60 to 0.73).
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