The Banff Digital Pathology Working Group (DPWG) was established with the goal to establish a digital pathology repository; develop, validate, and share models for image analysis; and foster collaborations using regular videoconferencing. During the calls, a variety of artificial intelligence (AI)-based support systems for transplantation pathology were presented. Potential collaborations in a competition/trial on AI applied to kidney transplant specimens, including the DIAGGRAFT challenge (staining of biopsies at multiple institutions, pathologists’ visual assessment, and development and validation of new and pre-existing Banff scoring algorithms), were also discussed. To determine the next steps, a survey was conducted, primarily focusing on the feasibility of establishing a digital pathology repository and identifying potential hosts. Sixteen of the 35 respondents (46%) had access to a server hosting a digital pathology repository, with 2 respondents that could serve as a potential host at no cost to the DPWG. The 16 digital pathology repositories collected specimens from various organs, with the largest constituent being kidney ( n = 12,870 specimens). A DPWG pilot digital pathology repository was established, and there are plans for a competition/trial with the DIAGGRAFT project. Utilizing existing resources and previously established models, the Banff DPWG is establishing new resources for the Banff community.

Digital Pathology

Digital Image Analysis

Surgical pathology

Telepathology

10.3389/ti.2023.11783

Cite

Citations (5)

Pretreatment Nomogram for Prostate-Specific Antigen Recurrence After Radical Prostatectomy or External-Beam Radiation Therapy for Clinically Localized Prostate Cancer

Journal of Clinical Oncology (1999)

Anthony V. D’Amico Richard Whittington S. Bruce Malkowicz Julie Fondurulia Ming‐Hui Chen

PURPOSE: To present nomograms providing estimates of prostate-specific antigen (PSA) failure–free survival after radical prostatectomy (RP) or external-beam radiation therapy (RT) for men diagnosed during the PSA era with clinically localized disease. PATIENTS AND METHODS: A Cox regression multivariable analysis was used to determine the prognostic significance of the pretreatment PSA level, 1992 American Joint Committee on Cancer (AJCC) clinical stage, and biopsy Gleason score in predicting the time to posttherapy PSA failure in 1,654 men with T1c,2 prostate cancer managed with either RP or RT. RESULTS: Pretherapy PSA, AJCC clinical stage, and biopsy Gleason score were independent predictors (P < .0001) of time to posttherapy PSA failure in patients managed with either RP or RT. Two-year PSA failure rates derived from the Cox regression model and bootstrap estimates of the 95% confidence intervals are presented in the format of a nomogram stratified by the pretreatment PSA, AJCC clinical stage, biopsy Gleason score, and local treatment modality. CONCLUSION: Men at high risk (> 50%) for early (≤ 2 years) PSA failure could be identified on the basis of the type of local therapy received and the clinical information obtained as part of the routine work-up for localized prostate cancer. Selection of these men for trials evaluating adjuvant systemic and improved local therapies may be justified.

Nomogram

Localized disease

10.1200/jco.1999.17.1.168

Cite

Citations (399)

Expression of Prolactin and Its Receptor in Human Breast Carcinoma¹

Endocrinology (1997)

Carol Reynolds Kathleen T. Montone Colleen M. Powell John E. Tomaszewski Charles V. Clevenger

The growth regulatory effects of PRL on the human breast are mediated by its receptor (PRLr), a member of the cytokine receptor family. Recent reverse transcriptase-PCR studies by our laboratory and others have shown PRL expression within breast tissues at the RNA level. To confirm the role of this growth factor-receptor complex in normal and malignant breast tissues, the expression of PRL and PRLr was examined in parallel with the estrogen receptor (ER) and progesterone receptor (PR). Sixty-nine cases of primary invasive breast carcinoma were examined for PRL and PRLr expression by in situ hybridization and immunohistochemical technique, respectively. These data revealed widespread expression of PRL and its receptor in the breast cancers studied (>95%) and in the normal breast tissues (>93%), with no association between the expression of PRL-PRLr and ER or PR. These findings stand in contrast to prior RIA-based studies that detected the PRLr in only 20–60% of breast carcinomas, most commonly in ER-PR-positive cells. These results confirm prior data indicating the presence of an autocrine/paracrine loop for the PRL-PRLr complex within human breast tissues. Given the widespread expression of PRL-PRLr in breast cancer, pharmacological interventions aimed at the inhibition of function of this growth regulatory receptor complex may be of considerable utility in the therapy of this disease.

Prolactin receptor

Gynecomastia

Progesterone receptor

10.1210/endo.138.12.5605

Cite

Citations (191)

Supervised Multi-View Canonical Correlation Analysis (sMVCCA): Integrating Histologic and Proteomic Features for Predicting Recurrent Prostate Cancer

IEEE Transactions on Medical Imaging (2014)

George Lee Asha Singanamalli Haibo Wang Michael D. Feldman Stephen R. Master

In this work, we present a new methodology to facilitate prediction of recurrent prostate cancer (CaP) following radical prostatectomy (RP) via the integration of quantitative image features and protein expression in the excised prostate. Creating a fused predictor from high-dimensional data streams is challenging because the classifier must 1) account for the "curse of dimensionality" problem, which hinders classifier performance when the number of features exceeds the number of patient studies and 2) balance potential mismatches in the number of features across different channels to avoid classifier bias towards channels with more features. Our new data integration methodology, supervised Multi-view Canonical Correlation Analysis (sMVCCA), aims to integrate infinite views of highdimensional data to provide more amenable data representations for disease classification. Additionally, we demonstrate sMVCCA using Spearman's rank correlation which, unlike Pearson's correlation, can account for nonlinear correlations and outliers. Forty CaP patients with pathological Gleason scores 6-8 were considered for this study. 21 of these men revealed biochemical recurrence (BCR) following RP, while 19 did not. For each patient, 189 quantitative histomorphometric attributes and 650 protein expression levels were extracted from the primary tumor nodule. The fused histomorphometric/proteomic representation via sMVCCA combined with a random forest classifier predicted BCR with a mean AUC of 0.74 and a maximum AUC of 0.9286. We found sMVCCA to perform statistically significantly (p <; 0.05) better than comparative state-of-the-art data fusion strategies for predicting BCR. Furthermore, Kaplan-Meier analysis demonstrated improved BCR-free survival prediction for the sMVCCA-fused classifier as compared to histology or proteomic features alone.

Canonical correlation

Biochemical recurrence

Rank correlation

10.1109/tmi.2014.2355175

Cite

Citations (95)