Intraductal carcinoma of the prostate

Clinical Management of Intraductal Carcinoma of the Prostate

Intraductal carcinoma of the prostate (IDC-P) has emerged as a distinct entity with significant clinical implications in prostate cancer (PCa) management. Despite historically being considered an extension of invasive PCa, IDC-P shows unique biological characteristics that challenge traditional diagnostic and therapeutic settings. This review explores the clinical management of IDC-P. While the diagnosis of IDC-P relies on specific morphological criteria, its detection remains challenging due to inter-observer variability. Emerging evidence underscores the association of IDC-P with aggressive disease and poor clinical outcomes across various PCa stages. However, standardized management guidelines for IDC-P are lacking. Recent studies suggest considering adjuvant and neoadjuvant therapies in specific patient cohorts to improve outcomes and tailor treatment strategies based on the IDC-P status. However, the current level of evidence regarding this is low. Moving forward, a deeper understanding of the pathogenesis of IDC-P and its interaction with conventional PCa subtypes is crucial for refining risk stratification and therapeutic interventions.

Cribiform and intraductal carcinoma in hereditary prostate cancer: clinical and pathological analysis of 20 cases

Cribiform and intraductal carcinoma are patterns of aggressive prostate carcinoma. This study investigated the clinical and pathological features of hereditary prostate cancer. Twenty cases of hereditary prostate cancer from 11 family lines treated at the First Affiliated Hospital of Zhejiang University School of Medicine between 2016-2022 were included to summarize the clinical and pathological features by analyzing clinical information including follow up the survival of the patients and pathological features. Of the 20 hereditary prostate cancer cases, 19 were radical prostate specimens and 1 was a biopsy specimen. The mean age at diagnosis of the patients was 67.55 years and the mean PSA was 15.44 ng/ml, of which 10 cases had PSA ≥ 10 ng/ml and 5 cases had PSA ≥ 20 ng/ml. Of the 19 radical prostate specimens, Gleason cribriform pattern (Gleason grade 4) of PCa is observed in 15 cases (78.95%), and intraductal carcinoma, usually a rare form, is seen in 9 cases (47.3%). Two cases demonstrated pelvic lymph node metastasis, and 7 cases (35%) belonged to high-risk or very high-risk PCa. One case (5.26%) showed partial deletion of expression of RB1, and 13 cases (68.42%) showed deletion of expression of PTEN. Follow-up was 4-90 months, 2 cases had biochemical recurrence and 1 case died from prostate cancer. The mean age at diagnosis of this group of patients with hereditary prostate cancer was 67.55 years, the mean preoperative PSA was 15.44 ng/ml, and their histomorphology was characterized by a high percentage of intraductal carcinoma and cribriform pattern of the prostate.

Nomogram for predicting the overall survival and cancer-specific survival of patients with intraductal carcinoma of the prostate

PURPOSE

Intraductal carcinoma of the prostate (IDC-P) is a histological subtype that differs from conventional acinar adenocarcinoma in terms of its origin, appearance, and pathological features. For IDC-P, there is currently no recognized best course of action, and its prognosis is unclear. The goal of this study is to analyze independent prognostic factors in IDC-P patients and to develop and validate a nomogram to predict overall survival (OS) and cancer-specific survival (CSS).

METHODS

Clinical data for IDC-P patients were collected from the Surveillance, Epidemiology, and End Results database. To identify the independent variables influencing prognosis, multivariate Cox regression analysis was performed. A nomogram model was created utilizing these variables after comparing the variations in OS and CSS among various subgroups using Kaplan‒Meier curves. Internal validation of the nomograms was verified using the bootstrap resampling method.

RESULTS

The study included 280 IDC-P patients in total. Marital status, summary stage, grade, and the presence of lung metastases were significant factors impacting OS, and CSS was significantly influenced by marital status, summary stage, AJCC stage, the presence of lung metastases, the presence of bone metastases, and PSA according to univariate and multivariate Cox regression models (P < 0.05). Nomogram models were created to estimate OS and CSS using these parameters. The OS prediction model's C-index was 0.744, whereas the CSS prediction model's C-index was 0.831.

CONCLUSION

We developed and verified nomogram models for the prediction of 1-, 3-, and 5-year OS and CSS in patients with IDC-P. These nomograms serve as a resource for evaluating patient prognosis, therapy, and diagnosis, ultimately improving clinical decision-making accuracy.

A systematic review and meta-analysis of artificial intelligence diagnostic accuracy in prostate cancer histology identification and grading

BACKGROUND

Artificial intelligence (AI) is a promising tool in pathology, including cancer diagnosis, subtyping, grading, and prognostic prediction.

METHODS

The aim of the study is to assess AI application in prostate cancer (PCa) histology. We carried out a systematic literature search in 3 databases. Primary outcome was AI accuracy in differentiating between PCa and benign hyperplasia. Secondary outcomes were AI accuracy in determining Gleason grade and agreement among AI and pathologists.

RESULTS

Our final sample consists of 24 studies conducted from 2007 to 2021. They aggregate data from roughly 8000 cases of prostate biopsy and 458 cases of radical prostatectomy (RP). Sensitivity for PCa diagnostic exceeded 90% and ranged from 87% to 100%, and specificity varied from 68% to 99%. Overall accuracy ranged from 83.7% to 98.3% with AUC reaching 0.99. The meta-analysis using the Mantel-Haenszel method showed pooled sensitivity of 0.96 with I2 = 80.7% and pooled specificity of 0.95 with I2 = 86.1%. Pooled positive likehood ratio was 15.3 with I2 = 87.3% and negative - was 0.04 with I2 = 78.6%. SROC (symmetric receiver operating characteristics) curve represents AUC = 0.99. For grading the accuracy of AI was lower: sensitivity for Gleason grading ranged from 77% to 87%, and specificity from 82% to 90%.

CONCLUSIONS

The accuracy of AI for PCa identification and grading is comparable to expert pathologists. This is a promising approach which has several possible clinical applications resulting in expedite and optimize pathology reports. AI introduction into common practice may be limited by difficult and time-consuming convolutional neural network training and tuning.

Cribriform pattern in prostate tissues: Predictor for intraductal carcinoma of the prostate based on biopsy and radical prostatectomy pathology

Objectives

This study aims to investigate whether a cribriform pattern on prostate biopsy may be a factor in suspicion of intraductal carcinoma of the prostate after radical prostatectomy.

Methods

This retrospective study assessed 100 men who underwent prostatectomy from 2015 to 2019. Participants were grouped as 76 patients with Gleason pattern 4 and 24 patients without this pattern. All 100 participants underwent retrograde radical prostatectomy and limited lymph node dissection. The same pathologist evaluated all specimens. The cribriform pattern was evaluated with haematoxylin and eosin counterstaining, and intraductal carcinoma of the prostate was evaluated with immunohistochemical analysis of cytokeratin 34βE12.

Results

Patients with intraductal carcinoma of the prostate on immunohistochemical analysis showed a significant tendency to relapse in the postoperative period, and those with the cribriform pattern on biopsy had a significant recurrence rate. In univariate and multivariate analyses, intraductal carcinoma of the prostate confirmed in biopsy tissue was an independent predictor of biochemical recurrence after prostatectomy. The rate of intraductal carcinoma of the prostate confirmation was 28% of cases with a cribriform pattern in biopsy tissue, which was increased to 62% in prostatectomy tissues.

Conclusion

The cribriform pattern in the biopsy tissue may be a predictor for intraductal carcinoma of the prostate.

Imaging Recommendations for Diagnosis, Staging, and Management of Prostate Cancer

The Prostate Carcinoma Guidelines Panel have formulated these guidelines to assist medical professionals in the evidence-based management of prostate cancer. These have been formulated by a panel consisting of Indian multidisciplinary group of radiologists, uro-oncologists, urologists, radiation oncologists, medical oncologists, and pathologists. These recommendations present the best evidence available to the clinicians; however, using these recommendations will not always result in the best outcome. They aid in decision making for individual patients; however, these will never replace clinical expertise when making treatment decisions. Taking personal values and preferences or individual circumstances of patients into account is necessary for final treatment decision. Guidelines are not mandatory and should not to be referred as a legal standard of care.

Construction and validation of a clinical predictive nomogram for intraductal carcinoma of the prostate based on Chinese multicenter clinical data

Introduction

Intraductal carcinoma of the prostate (IDC-P) is a special pathological type of prostate cancer that is highly aggressive with poor prognostic outcomes.

Objective

To establish an effective predictive model for predicting IDC-P.

Methods

Data for 3185 patients diagnosed with prostate cancer at three medical centers in China from October 2012 to April 2022 were retrospectively analyzed. One cohort (G cohort) consisting of 2384 patients from Zhejiang Provincial People's Hospital was selected for construction (Ga cohort) and internal validate (Gb cohort)of the model. Another cohort (I cohort) with 344 patients from Quzhou People's Hospital and 430 patients from Jiaxing Second People's Hospital was used for external validation. Univariate and multivariate binary logistic regression analyses were performed to identify the independent predictors. Then, the selected predictors were then used to establish the predictive nomogram. The apparent performance of the model was evaluated via externally validated. Decision curve analysis was also performed to assess the clinical utility of the developed model.

Results

Univariate and multivariate logistic regression analyses showed that alkaline phosphatase (ALP), total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), prostate specific antigen (PSA) and lactate dehydrogenase were independent predictors of IDC-P. Therefore, a predictive nomogram of IDC-P was constructed. The nomogram had a good discriminatory power (AUC = 0.794). Internal validation (AUC = 0.819)and external validation (AUC = 0.903) also revealed a good predictive ability. Calibration curves showed good agreement between the predicted and observed incidences of IDC-P.

Conclusion

We developed a clinical predictive model composed of alkaline phosphatase (ALP), total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), prostate specific antigen (PSA) and lactate dehydrogenase (LDH) with a high precision and universality. This model provides a novel calculator for predicting the diagnosis of IDC-P and different treatment options for patients at an early stage.

Radiomic Machine Learning and External Validation Based on 3.0 T mpMRI for Prediction of Intraductal Carcinoma of Prostate With Different Proportion

Purpose

To assess the association of radiomics features based on multiparametric MRI (mpMRI) with the proportion of intraductal carcinoma of prostate (IDC-P) and validate the predictive models.

Materials and Methods

We retrospectively included pre-treatment MR images of prostate cancer (PCa) with IDC components of high proportion (≥10%, hpIDC-P), low proportion (<10%, lpIDC-P), and pure acinar adenocarcinoma (PAC) from our institution for training and internal validation and cooperated cohort for external validation. Normalized images of T2WI, diffusion weighted imaging (DWI) and apparent diffusion coefficient (ADC) map, and dynamic contrast enhanced (DCE) sequences were used for radiomics modeling. The clinical model was built based on serum total prostate specific antigen (tPSA) and Gleason score (GS), and the integrated model was the combination of Rad-score and clinicopathological data. The discrimination ability was assessed by area under the receiver operating characteristic curve (ROC-AUC) in the internal and external validation sets and compared by DeLong test.

Results

Overall, 97 patients with hpIDC-P, 87 lpIDC-P, and 78 PAC were included for training and internal validation, and 11, 16, and 19 patients for external validation. The integrated model for predicting hpIDC-P got the best ROC-AUC of 0.88 (95%CI = 0.83-0.93) in internal and 0.86 (95%CI = 0.72-1.0) in external validation, which both outperformed clinical models (AUC=0.78, 95% CI = 0.72-0.85, AUC=0.69, 95% CI = 0.5-0.85, respectively) based solely on GS, and the radiomics model (AUC=0.85, 95% CI = 0.79-0.91) was slightly inferior to the integrated model and better than the clinical model in internal dataset. The integrated model for predicting lpIDC-P outperformed both radiomics and clinical models in the internal dataset, while slightly inferior to the integrated model for predicting hpIDC-P.

Conclusions

Radiomics signature improved differentiation of both hpIDC-P and lpIDC-P versus PAC when compared with the clinical model based on Gleason score, and was validated in an external cohort.

Differential diagnosis of uncommon prostate diseases: combining mpMRI and clinical information

The differential diagnosis of abnormalities in the prostate is broad, covering common (acinar adenocarcinoma, benign prostatic hyperplasia, chronic prostatitis, hemorrhage, cysts, calcifications, atrophy and fibrosis) and less common conditions (tumors other than acinar adenocarcinoma, granulomatous prostatitis containing tuberculosis, abscesses and other conditions, and idiopathic disorders such as amyloidosis and exophytic benign prostatic hyperplasia). Recent advances in magnetic resonance imaging (MRI) of the prostate gland and imaging guidelines, such as the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1), have dramatically improved the ability to distinguish common abnormalities, especially the ability to detect clinically significant prostate cancer (csPCa). Overlap can exist in the clinical history and imaging features associated with various common/uncommon prostate abnormalities, and biopsy is often required but is invasive. Prostate abnormalities can be divided into two categories: category 1, diseases for which PI-RADS scores are suitable for use, and category 2, diseases for which PI-RADS scores are unsuitable for use. Radiologists must have an intimate knowledge of other diseases, especially uncommon conditions. Past relevant history, symptoms, age, serum prostate-specific antigen (PSA) levels, MRI manifestations, and the applicability of the PI-RADS assessment should be considered when diagnosing prostate abnormalities.

Circular RNA UBAP2 promotes the proliferation of prostate cancer cells via the miR-1244/MAP3K2 axis

Prostate cancer (PCa) is a common male malignant disease with a high incidence, which can seriously affect the quality of life of patients. The survival rate of patients with PCa has improved to 98.6%; however, new insights for the molecular mechanism are still urgently required. Circular RNA (circ)UBAP2 is a tumor-associated circRNA that has been demonstrated to promote the progression of various types of cancer. CircUBAP2 has been demonstrated to be significantly upregulated in PCa, but its role in the progression of PCa remains unclear. The present study aimed to provide an improved understanding of the regulatory mechanism of circUBAP2 in PCa. circUBAP2 expression was identified to be upregulated in four PCa cell lines and clinical tissues by using reverse transcription-quantitative PCR analysis. Binding sites analysis and luciferase reporter gene assay indicated that the microRNA(miR)-1244/MAP3K2 axis was the target of circUBAP2. Gain-of-function assays revealed that circUBAP2 promoted the proliferation of PCa cells by sponging miR-1244 and promoting the MAP3K2 axis. The present findings may be essential for providing new strategies in the diagnosis and targeted therapy of PCa.