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- Danneman, Daniela, et al.
(author)
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Accuracy of prostate biopsies for predicting Gleason score in radical prostatectomy specimens : nationwide trends 2000-2012
- 2017
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In: BJU International. - : Wiley. - 1464-4096 .- 1464-410X. ; 119:1, s. 50-56
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Journal article (peer-reviewed)abstract
- Objectives To investigate how well the Gleason score in diagnostic needle biopsies predicted the Gleason score in a subsequent radical prostatectomy (RP) specimen before and after the 2005 International Society of Urological Pathology (ISUP) revision of Gleason grading, and if the recently proposed ISUP grades 1-5 (corresponding to Gleason scores 6, 3 + 4, 4 + 3, 8 and 9-10) better predict the RP grade. Patients and Methods All prostate cancers diagnosed in Sweden are reported to the National Prostate Cancer Register (NPCR). We analysed the Gleason scores and ISUP grades from the diagnostic biopsies and the RP specimens in 15 598 men in the NPCR who: were diagnosed between 2000 and 2012 with clinical stage T1-2 M0/X prostate cancer on needle biopsy; were aged <= 70 years; had serum PSA concentration of < 20 ng/mL; and underwent a RP < 6 months after diagnosis as their primary treatment. Results Prediction of RP Gleason score increased from 55 to 68% between 2000 and 2012. Most of the increase occurred before 2005 (nine percentage points; P < 0.001); however, when adjusting for Gleason score and year of diagnosis in a multivariable analysis, the prediction of RP Gleason score decreased over time (odds ratio [OR] 0.98; P < 0.002). A change in the ISUP grades would have led to a decreasing agreement between biopsy and RP grades over time, from 68% in 2000 to 57% in 2012, with an OR of 0.95 in multivariable analysis (P < 0.001). Conclusion Agreement between biopsy and RP Gleason score improved from 2000 to 2012, with most of the improvement occurring before the 2005 ISUP grading revision. Had ISUP grades been used instead of Gleason score, the agreement between biopsy and RP grade would have decreased, probably because of its separation of Gleason score 7 into ISUP grades 2 and 3 (Gleason score 3 + 4 vs 4 + 3).
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- Egevad, Lars, et al.
(author)
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Interobserver reproducibility of perineural invasion of prostatic adenocarcinoma in needle biopsies
- 2021
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In: Virchows Archiv. - : Springer Nature. - 0945-6317 .- 1432-2307. ; 478:6, s. 1109-1116
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Journal article (peer-reviewed)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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3. |
- Egevad, Lars, et al.
(author)
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Prognostic Significance of Prostate Cancer with Seminal Vesicle Invasion on Radical Prostatectomy : A National Registry Study
- 2017
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In: Modern Pathology. - Karolinska Inst, Stockholm, Sweden. Univ Uppsala Hosp, Uppsala, Sweden. Univ Otago, Wellington Sch Med & Hlth Sci, Wellington, New Zealand. Aquesta Pathol, Brisbane, Qld, Australia. : NATURE PUBLISHING GROUP. - 0893-3952 .- 1530-0285. ; 97, s. 222A-222A
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Journal article (other academic/artistic)
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6. |
- Kartasalo, Kimmo, et al.
(author)
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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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In: European Urology Focus. - : Elsevier. - 2405-4569. ; 7:4, s. 687-691
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Research review (peer-reviewed)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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7. |
- Kristiansen, Anna, et al.
(author)
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Prognostic significance and biopsy characteristics of prostate cancer with seminal vesicle invasion on radical prostatectomy : a nationwide population-based study
- 2017
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In: Pathology. - : ELSEVIER SCIENCE BV. - 0031-3025 .- 1465-3931. ; 49:7, s. 715-720
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Journal article (peer-reviewed)abstract
- The objective of this study was to evaluate the prognostic significance of seminal vesicle invasion (SVI, pT3b) compared with extraprostatic extension (EPE) alone (pT3a) after radical prostatectomy, and to correlate pre-operative biopsy pathology with SVI and EPE. The National Prostate Cancer Register includes all prostate cancers diagnosed in Sweden. We analysed 4063 cases with stage category pT3a and 1371 cases with pT3b at radical prostatectomy between 2000 and 2012. Associations between pT3a and pT3b and progression were evaluated and adjusted for year, age, biopsy grade and s-PSA. Needle biopsy findings in these stages were compared. Patients with pT3b (n = 1371) had a higher risk of death from prostate cancer (HR 2.3, 95% CI 1.5-3.3, p < 0.001) and death from any cause (HR 1.5, 95% CI 1.2-1.8, p < 0.001) than those with pT3a (n = 4063). They were also more likely to be treated with post-operative radiotherapy (HR 1.5, 95% CI 1.4-1.7, p < 0.001) or androgen deprivation therapy (HR 3.0, 95% CI 2.5-3.7, p < 0.001), indicating clinical progression. Yet, disease-specific survival of patients with stage pT3b was 94% after 6 years. Median cancer extent in pre-operative biopsies of pT3a and pT3b was 14 and 24 mm (p < 0.001), number of positive cores was four and five, (p < 0.001) and biopsy Gleason score was 8-10 in 11.6% and 27.3%, respectively (p < 0.001). SVI of prostate cancer is associated with worse outcome after radical prostatectomy than EPE alone. However, few patients with SVI die within 6 years from surgery, suggesting that radical prostatectomy may be curative in locally advanced cancers.
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8. |
- Olsson, Henrik, et al.
(author)
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Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction
- 2022
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In: Nature Communications. - : Springer Nature. - 2041-1723. ; 13:1
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Journal article (peer-reviewed)abstract
- Unreliable predictions can occur when an artificial intelligence (AI) system is presented with data it has not been exposed to during training. We demonstrate the use of conformal prediction to detect unreliable predictions, using histopathological diagnosis and grading of prostate biopsies as example. We digitized 7788 prostate biopsies from 1192 men in the STHLM3 diagnostic study, used for training, and 3059 biopsies from 676 men used for testing. With conformal prediction, 1 in 794 (0.1%) predictions is incorrect for cancer diagnosis (compared to 14 errors [2%] without conformal prediction) while 175 (22%) of the predictions are flagged as unreliable when the AI-system is presented with new data from the same lab and scanner that it was trained on. Conformal prediction could with small samples (N = 49 for external scanner, N = 10 for external lab and scanner, and N = 12 for external lab, scanner and pathology assessment) detect systematic differences in external data leading to worse predictive performance. The AI-system with conformal prediction commits 3 (2%) errors for cancer detection in cases of atypical prostate tissue compared to 44 (25%) without conformal prediction, while the system flags 143 (80%) unreliable predictions. We conclude that conformal prediction can increase patient safety of AI-systems.
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9. |
- Strom, Peter, et al.
(author)
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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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In: The Lancet Oncology. - : Elsevier. - 1470-2045 .- 1474-5488. ; 21:2, s. 222-232
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Journal article (peer-reviewed)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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10. |
- Ström, Peter, et al.
(author)
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Pathologist-Level Grading of Prostate Biospies with Artificial intelligence
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Other publication (other academic/artistic)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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