Intraductal carcinoma of the prostate: a critical re-appraisal

Intraductal carcinoma of the prostate gland (IDCP), which is now categorised as a distinct entity by WHO 2016, includes two biologically distinct diseases. IDCP associated with invasive carcinoma (IDCP-inv) generally represents a growth pattern of invasive prostatic adenocarcinoma while the rarely encountered pure IDCP is a precursor of prostate cancer. This review highlights issues that require further discussion and clarification. The diagnostic criterion “nuclear size at least 6 times normal” is ambiguous as “size” could refer to either nuclear area or diameter. If area, then this criterion could be re-defined as nuclear diameter at least three times normal as it is difficult to visually compare area of nuclei. It is also unclear whether IDCP could also include tumours with ductal morphology. There is no consensus whether pure IDCP in needle biopsies should be managed with re-biopsy or radical therapy. A pragmatic approach would be to recommend radical therapy only for extensive pure IDCP that is morphologically unequivocal for high-grade prostate cancer. Active surveillance is not appropriate when low-grade invasive cancer is associated with IDCP, as such patients usually have unsampled high-grade prostatic adenocarcinoma. It is generally recommended that IDCP component of IDCP-inv should be included in tumour extent but not grade. However, there are good arguments in favour of grading IDCP associated with invasive cancer. All historical as well as contemporary Gleason outcome data are based on morphology and would have included an associated IDCP component in the tumour grade. WHO 2016 recommends that IDCP should not be graded, but it is unclear whether this applies to both pure IDCP and IDCP-inv

Biomarkers in renal cancer

Treatment options for primary and metastatic renal cancer are increasing. Accurate data from the pathological examination of renal cancer specimens aid clinicians in stratifying patients for surveillance and adjuvant therapies. This review focuses on biomarkers in diagnosis, prognosis and prediction of the biologic behavior of renal tumors which should be recorded in pathology reports and which are under investigation. Special emphasis is given to the use of immunohistochemical markers in differential diagnosis of various renal tumor subtypes. The relevance of cytogenetic and molecular findings is also discussed. The review includes the 2012 International Society for Urological Pathology Consensus conference recommendations

Detection of perineural invasion in prostate needle biopsies with deep neural networks

The presence of perineural invasion (PNI) by carcinoma in prostate biopsies has been shown to be associated with poor prognosis. The assessment and quantification of PNI are, however, labor intensive. To aid pathologists in this task, we developed an artificial intelligence (AI) algorithm based on deep neural networks. We collected, digitized, and pixel-wise annotated the PNI findings in each of the approximately 80,000 biopsy cores from the 7406 men who underwent biopsy in a screening trial between 2012 and 2014. In total, 485 biopsy cores showed PNI. We also digitized more than 10% (n = 8318) of the PNI negative biopsy cores. Digitized biopsies from a random selection of 80% of the men were used to build the AI algorithm, while 20% were used to evaluate its performance. For detecting PNI in prostate biopsy cores, the AI had an estimated area under the receiver operating characteristics curve of 0.98 (95% CI 0.97-0.99) based on 106 PNI positive cores and 1652 PNI negative cores in the independent test set. For a pre-specified operating point, this translates to sensitivity of 0.87 and specificity of 0.97. The corresponding positive and negative predictive values were 0.67 and 0.99, respectively. The concordance of the AI with pathologists, measured by mean pairwise Cohen's kappa (0.74), was comparable to inter-pathologist concordance (0.68 to 0.75). The proposed algorithm detects PNI in prostate biopsies with acceptable performance. This could aid pathologists by reducing the number of biopsies that need to be assessed for PNI and by highlighting regions of diagnostic interest.</p

Detection of perineural invasion in prostate needle biopsies with deep neural networks

The presence of perineural invasion (PNI) by carcinoma in prostate biopsies has been shown to be associated with poor prognosis. The assessment and quantification of PNI are, however, labor intensive. To aid pathologists in this task, we developed an artificial intelligence (AI) algorithm based on deep neural networks. We collected, digitized, and pixel-wise annotated the PNI findings in each of the approximately 80,000 biopsy cores from the 7406 men who underwent biopsy in a screening trial between 2012 and 2014. In total, 485 biopsy cores showed PNI. We also digitized more than 10% (n = 8318) of the PNI negative biopsy cores. Digitized biopsies from a random selection of 80% of the men were used to build the AI algorithm, while 20% were used to evaluate its performance. For detecting PNI in prostate biopsy cores, the AI had an estimated area under the receiver operating characteristics curve of 0.98 (95% CI 0.97–0.99) based on 106 PNI positive cores and 1652 PNI negative cores in the independent test set. For a pre-specified operating point, this translates to sensitivity of 0.87 and specificity of 0.97. The corresponding positive and negative predictive values were 0.67 and 0.99, respectively. The concordance of the AI with pathologists, measured by mean pairwise Cohen’s kappa (0.74), was comparable to inter-pathologist concordance (0.68 to 0.75). The proposed algorithm detects PNI in prostate biopsies with acceptable performance. This could aid pathologists by reducing the number of biopsies that need to be assessed for PNI and by highlighting regions of diagnostic interest.publishedVersionPeer reviewe

Interobserver reproducibility of perineural invasion of prostatic adenocarcinoma in needle biopsies

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

Validation of 34betaE12 immunoexpression in clear cell papillary renal cell carcinoma as a sensitive biomarker

Clear cell papillary renal cell carcinoma (CCPRCC) is a recently recognised neoplasm with a broad spectrum of morphological characteristics, thus representing a challenging differential diagnosis, especially with the low malignant potential multicystic renal cell neoplasms and clear cell renal cell carcinoma. We selected 14 cases of CCPRCC with a wide spectrum of morphological features diagnosed on morphology and CK7 immunoreactivity and analysed them using a panel of immunohistochemical markers, focusing on 34 beta E12 and related CKs 1,5,10 and 14 and several molecular analyses such as fluorescence in situ hybridisation (FISH), array comparative genomic hybridisation (aCGH), VHL methylation, VHL and TCEB1 sequencing and multiplex ligation-dependent probe amplification (MLPA). Twelve of 13 (92%) CCPRCC tumours were positive for 34 beta E12. One tumour without 3p alteration by FISH revealed VHL mutation and 3p deletion at aCGH; thus, it was re-classified as clear cell RCC. We concluded that: (1) immunohistochemical expression of CK7 is necessary for diagnostic purposes, but may not be sufficient to identify CCPRCC, while 34 beta E12, in part due to the presence of CK14 antigen expression, can be extremely useful for the recognition of this tumour; and (2) further molecular analysis of chromosome 3p should be considered to support of CCPRCC diagnosis, when FISH analysis does not evidence the common loss of chromosome 3p.Peer reviewe

Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction

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.publishedVersionPeer reviewe

Gleason Grade 4 Prostate Adenocarcinoma Patterns: An Inter-observer Agreement Study among Genitourinary Pathologists

Aims To assess the interobserver reproducibility of individual Gleason grade 4 growth patterns. Methods and results Twenty-three genitourinary pathologists participated in the evaluation of 60 selected high-magnification photographs. The selection included 10 cases of Gleason grade 3, 40 of Gleason grade 4 (10 per growth pattern), and 10 of Gleason grade 5. Participants were asked to select a single predominant Gleason grade per case (3, 4, or 5), and to indicate the predominant Gleason grade 4 growth pattern, if present. ‘Consensus’ was defined as at least 80% agreement, and ‘favoured’ as 60–80% agreement. Consensus on Gleason grading was reached in 47 of 60 (78%) cases, 35 of which were assigned to grade 4. In the 13 non-consensus cases, ill-formed (6/13, 46%) and fused (7/13, 54%) patterns were involved in the disagreement. Among the 20 cases where at least one pathologist assigned the ill-formed growth pattern, none (0%, 0/20) reached consensus. Consensus for fused, cribriform and glomeruloid glands was reached in 2%, 23% and 38% of cases, respectively. In nine of 35 (26%) consensus Gleason grade 4 cases, participants disagreed on the growth pattern. Six of these were characterized by large epithelial proliferations with delicate intervening fibrovascular cores, which were alternatively given the designation fused or cribriform growth pattern (‘complex fused’). Conclusions Consensus on Gleason grade 4 growth pattern was predominantly reached on cribriform and glomeruloid patterns, but rarely on ill-formed and fused glands. The complex fused glands seem to constitute a borderline pattern of unknown prognostic significance on which a consensus could not be reached

Artificial Intelligence for Diagnosis and Gleason Grading of Prostate Cancer in Biopsies-Current Status and Next Steps

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 summaryThis 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.</p