Implementation of a Precision Pathology Program Focused on Oncology-Based Prognostic and Predictive Outcomes

Applications of artificial intelligence in prostate cancer histopathology

The diagnosis of prostate cancer (PCa) depends on the evaluation of core needle biopsies by trained pathologists. Artificial intelligence (AI) derived models have been created to address the challenges posed by pathologists' increasing workload, workforce shortages, and variability in histopathology assessment. These models with histopathological parameters integrated into sophisticated neural networks demonstrate remarkable ability to identify, grade, and predict outcomes for PCa. Though the fully autonomous diagnosis of PCa remains elusive, recently published data suggests that AI has begun to serve as an initial screening tool, an assistant in the form of a real-time interactive interface during histological analysis, and as a second read system to detect false negative diagnoses. Our article aims to describe recent advances and future opportunities for AI in PCa histopathology.

Development and validation of an AI-enabled digital breast cancer assay to predict early-stage breast cancer recurrence within 6 years

BACKGROUND

Breast cancer (BC) grading plays a critical role in patient management despite the considerable inter- and intra-observer variability, highlighting the need for decision support tools to improve reproducibility and prognostic accuracy for use in clinical practice. The objective was to evaluate the ability of a digital artificial intelligence (AI) assay (PDxBr) to enrich BC grading and improve risk categorization for predicting recurrence.

METHODS

In our population-based longitudinal clinical development and validation study, we enrolled 2075 patients from Mount Sinai Hospital with infiltrating ductal carcinoma of the breast. With 3:1 balanced training and validation cohorts, patients were retrospectively followed for a median of 6 years. The main outcome was to validate an automated BC phenotyping system combined with clinical features to produce a binomial risk score predicting BC recurrence at diagnosis.

RESULTS

The PDxBr training model (n = 1559 patients) had a C-index of 0.78 (95% CI, 0.76-0.81) versus clinical 0.71 (95% CI, 0.67-0.74) and image feature models 0.72 (95% CI, 0.70-0.74). A risk score of 58 (scale 0-100) stratified patients as low or high risk, hazard ratio (HR) 5.5 (95% CI 4.19-7.2, p < 0.001), with a sensitivity 0.71, specificity 0.77, NPV 0.95, and PPV 0.32 for predicting BC recurrence within 6 years. In the validation cohort (n = 516), the C-index was 0.75 (95% CI, 0.72-0.79) versus clinical 0.71 (95% CI 0.66-0.75) versus image feature models 0.67 (95% CI, 0.63-071). The validation cohort had an HR of 4.4 (95% CI 2.7-7.1, p < 0.001), sensitivity of 0.60, specificity 0.77, NPV 0.94, and PPV 0.24 for predicting BC recurrence within 6 years. PDxBr also improved Oncotype Recurrence Score (RS) performance: RS 31 cutoff, C-index of 0.36 (95% CI 0.26-0.45), sensitivity 37%, specificity 48%, HR 0.48, p = 0.04 versus Oncotype RS plus AI-grade C-index 0.72 (95% CI 0.67-0.79), sensitivity 78%, specificity 49%, HR 4.6, p < 0.001 versus Oncotype RS plus PDxBr, C-index 0.76 (95% CI 0.70-0.82), sensitivity 67%, specificity 80%, HR 6.1, p < 0.001.

CONCLUSIONS

PDxBr is a digital BC test combining automated AI-BC prognostic grade with clinical-pathologic features to predict the risk of early-stage BC recurrence. With future validation studies, we anticipate the PDxBr model will enrich current gene expression assays and enhance treatment decision-making.

2b or Not 2b: How Opposing FGF Receptor Splice Variants Are Blocking Progress in Precision Oncology

More than ten thousand peer-reviewed studies have assessed the role of fibroblast growth factors (FGFs) and their receptors (FGFRs) in cancer, but few patients have yet benefited from drugs targeting this molecular family. Strategizing how best to use FGFR-targeted drugs is complicated by multiple variables, including RNA splicing events that alter the affinity of ligands for FGFRs and hence change the outcomes of stromal-epithelial interactions. The effects of splicing are most relevant to FGFR2; expression of the FGFR2b splice isoform can restore apoptotic sensitivity to cancer cells, whereas switching to FGFR2c may drive tumor progression by triggering epithelial-mesenchymal transition. The differentiating and regulatory actions of wild-type FGFR2b contrast with the proliferative actions of FGFR1 and FGFR3, and may be converted to mitogenicity either by splice switching or by silencing of tumor suppressor genes such as CDH1 or PTEN. Exclusive use of small-molecule pan-FGFR inhibitors may thus cause nonselective blockade of FGFR2 isoforms with opposing actions, undermining the rationale of FGFR2 drug targeting. This splice-dependent ability of FGFR2 to switch between tumor-suppressing and -driving functions highlights an unmet oncologic need for isoform-specific drug targeting, e.g., by antibody inhibition of ligand-FGFR2c binding, as well as for more nuanced molecular pathology prediction of FGFR2 actions in different stromal-tumor contexts.

The implementation of a commercially available multi-gene profile test for breast cancer characterization in a department of pathology: what have we learned from the first 100 cases?

Analysis of breast cancer prognostic and predictive factors is still nowadays poorly accurate and standardized. The advent of multi-gene expression profiles (MGEPs) has improved the prediction of breast cancer outcome, particularly regarding early luminal breast cancers (LBCs). The availability in our Institute of EndoPredict® (EP), a last-generation prognostic gene signature assay, has prompted us to study a series of LBCs, firstly verifying its reproducibility on six routine representative cases, either presenting non-optimal preanalytical conditions or different tumor samples from the same patient; secondly, correlating EP results on 8 retrospectively recruited samples with patients' follow-up; thirdly, applying prospectively EP on 100 routinely diagnosed cases, assessing the oncologists' and pathologists' attitude toward it. The complete reproducibility of EP on all the samples investigated in the first phase allowed to state that EP overcomes the detrimental effects of an inaccurate pre-analytic phase, determining the most appropriate prognostic and predictive parameters of breast cancer. The second phase confirmed EP as a fundamental tool in guiding therapeutic decision, improving the classical bio-pathological characterization and recovering 38% patients' inadequately managed. Finally, the study disclosed how oncologists sometimes inadequately requested EP, but also how it allows a better stratification of breast cancer otherwise considered poorly aggressive and not requiring an EP test, such as G1 neoplasms or tubular histotype. In conclusion, the introduction of EP test in an Anatomic Pathology Department emerges as a useful tool in routine breast cancer diagnosis, both for the characterization of individual cases and, as a result, for more appropriate therapeutic choices.

Pretreatment albumin-to-alkaline phosphatase ratio as a prognostic indicator in solid cancers: A meta-analysis with trial sequential analysis

BACKGROUND

Albumin-to-alkaline phosphatase ratio (AAPR), a novel and economic serum biomarker, is associated with survival in patients with cancer. This study aimed to evaluate the potential role of AAPR as a prognostic indicator of solid cancers.

METHODS

This meta-analysis with trial sequential analysis of retrospective studies was designed to investigate the relationship between AAPR and overall survival (OS) in solid cancers. The meta-analysis included 5951 patients from 20 cohorts. The main predictor variable was AAPR, and the main outcome was OS. Statistical tests were performed using Stata 12.0, Revman 5.3, and R 3.6.1.

RESULTS

Compared to patients with a lower AAPR, those with a higher AAPR had a better OS (hazard ratio [HR]: 0.50; 95% confidence interval [CI]: 0.43-0.58; p < 0.001). Subgroup analysis by tumor type indicated that a higher AAPR was associated with a better OS in non-small cell lung cancer (HR: 0.45; 95% CI: 0.26-0.78; p < 0.001), small cell lung cancer (HR: 0.60; 95% CI: 0.44-0.82; p < 0.001), hepatocellular carcinoma (HR: 0.49; 95% CI: 0.34-0.69; p < 0.001), pancreatic ductal adenocarcinoma (HR: 0.47; 95% CI: 0.31-0.71; p < 0.001), and nasopharyngeal carcinoma (HR: 0.42; 95% CI: 0.21-0.85; p = 0.016).

CONCLUSION

Pretreatment AAPR may be a useful prognostic indicator in solid cancers.

Development and validation of a novel automated Gleason grade and molecular profile that define a highly predictive prostate cancer progression algorithm-based test

BACKGROUND

Postoperative risk assessment remains an important variable in the effective treatment of prostate cancer. There is an unmet clinical need for a test with the potential to enhance the Gleason grading system with novel features that more accurately reflect a personalized prediction of clinical failure.

METHODS

A prospectively designed retrospective study utilizing 892 patients, post radical prostatectomy, followed for a median of 8 years. In training, using digital image analysis to combine microscopic pattern analysis/machine learning with biomarkers, we evaluated Precise Post-op model results to predict clinical failure in 446 patients. The derived prognostic score was validated in 446 patients. Eligible subjects required complete clinical-pathologic variables and were excluded if they had received neoadjuvant treatment including androgen deprivation, radiation or chemotherapy prior to surgery. No patients were enrolled with metastatic disease prior to surgery. Evaluate the assay using time to event concordance index (C-index), Kaplan-Meier, and hazards ratio.

RESULTS

In the training cohort (n = 306), the Precise Post-op test predicted significant clinical failure with a C-index of 0.82, [95% CI: 0.76-0.86], HR:6.7, [95% CI: 3.59-12.45], p < 0.00001. Results were confirmed in validation (n = 284) with a C-index 0.77 [95% CI: 0.72-0.81], HR = 5.4, [95% CI: 2.74-10.52], p < 0.00001. By comparison, a clinical feature base model had a C-index of 0.70 with a HR = 3.7. The Post-Op test also re-classified 58% of CAPRA-S intermediate risk patients as low risk for clinical failure.

CONCLUSIONS

Precise Post-op tissue-based test discriminates low from intermediate high risk prostate cancer disease progression in the postoperative setting. Guided by machine learning, the test enhances traditional Gleason grading with novel features that accurately reflect the biology of personalized risk assignment.

Clinical utility of real-time ultrasound-multimodality fusion guidance for percutaneous biopsy of focal liver lesions

OBJECTIVES

To prospectively evaluate the clinical value of real-time ultrasonography (US)-computed tomography (CT)/magnetic resonance imaging (MRI) fusion imaging for percutaneous needle biopsy of focal liver lesions (FLLs), and to compare its biopsy success rate with that of conventional US-guided biopsy in a propensity-score matched group.

METHODS

This study was approved by our Institutional Review Board and informed consent was obtained from all patients enrolled in the prospective study group. Ninety patients referred to the Department of Radiology for percutaneous biopsy of FLLs were enrolled in this study. Tumor visibility, attainment of a safe access route, and technical feasibility were assessed on conventional US first and later on real-time fusion imaging by one of four abdominal radiologists. Thereafter, differences in scores between real-time fusion imaging and conventional US were determined. In addition, overall diagnostic success rates of a real-time fusion imaging-guided biopsy group and a propensity-score matched, conventional US-guided biopsy group, consisting of 100 patients used as historical control, were compared.

RESULTS

With real-time fusion imaging, tumor visibility, attainment of a safe access route, and operator's technical feasibility were significantly improved compared with conventional US (P < .001). In addition, all invisible (n = 13) and not feasible (n = 10) FLLs on conventional US became visible and feasible for percutaneous US-guided biopsy after applying the fusion system. The diagnostic success rate of real-time fusion-guided biopsy was 94.4% (85/90), which was significantly better than that obtained with the conventional US-guided biopsy (94.4% vs. 83%, P < .03), with reduced biopsy procedure times (7.1 ± 3.5 vs. 9.7 ± 2.8, P < .02).

CONCLUSIONS

Real-time US-CT/MR fusion imaging guidance was able to provide clinical value for percutaneous needle biopsy of FLLs by improving the diagnostic success rate of biopsy and by reducing procedure time.