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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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| 3. |
- 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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| 4. |
- 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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| 5. |
- van der Kwast, Theo, et al.
(författare)
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International Society of Urological Pathology Expert Opinion on Grading of Urothelial Carcinoma
- 2022
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Ingår i: European Urology Focus. - : Elsevier BV. - 2405-4569. ; 8:2, s. 438-446
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Forskningsöversikt (refereegranskat)abstract
- Context: Grading is the mainstay for treatment decisions for patients with non–muscle-invasive bladder cancer (NMIBC). Objective: To determine the requirements for an optimal grading system for NMIBC via expert opinion. Evidence acquisition: A multidisciplinary working group established by the International Society of Urological Pathology reviewed available clinical, histopathological, and molecular evidence for an optimal grading system for bladder cancer. Evidence synthesis: Bladder cancer grading is a continuum and five different grading systems based on historical grounds could be envisaged. Splitting of the World Health Organization (WHO) 2004 low-grade class for NMIBC lacks diagnostic reproducibility and molecular-genetic support, while showing little difference in progression rate. Subdividing the clinically heterogeneous WHO 2004 high-grade class for NMIBC into intermediate and high risk categories using the WHO 1973 grading is supported by both clinical and molecular-genetic findings. Grading criteria for the WHO 1973 scheme were detailed on the basis of literature findings and expert opinion. Conclusions: Splitting of the WHO 2004 high-grade category into WHO 1973 grade 2 and 3 subsets is recommended. Provision of more detailed histological criteria for the WHO 1973 grading might facilitate the general acceptance of a hybrid four-tiered grading system or—as a preferred option—a more reproducible three-tiered system distinguishing low-, intermediate (high)-, and high-grade NMIBC. Patient summary: Improvement of the current systems for grading bladder cancer may result in better informed treatment decisions for patients with bladder cancer. A three-tiered grading system for non–muscle invasive bladder cancer derived by splitting the heterogeneous World Health Organization (WHO) 2004 high-grade category into WHO 1973 grade 2 and 3 subsets is recommended, as this may result in more informed treatment decisions.
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