Artificial Intelligence Helps Pathologists Increase Diagnostic Accuracy and Efficiency in the Detection of Breast Cancer Lymph Node Metastases.

The detection of lymph node metastases is essential for breast cancer staging, although it is a tedious and time-consuming task where the sensitivity of pathologists is suboptimal. Artificial intelligence (AI) can help pathologists detect lymph node metastases, which could help alleviate workload issues. We studied how pathologists' performance varied when aided by AI. An AI algorithm was trained using more than 32 000 breast sentinel lymph node whole slide images (WSIs) matched with their corresponding pathology reports from more than 8000 patients. The algorithm highlighted areas suspicious of harboring metastasis. Three pathologists were asked to review a dataset comprising 167 breast sentinel lymph node WSIs, of which 69 harbored cancer metastases of different sizes, enriched for challenging cases. Ninety-eight slides were benign. The pathologists read the dataset twice, both digitally, with and without AI assistance, randomized for slide and reading orders to reduce bias, separated by a 3-week washout period. Their slide-level diagnosis was recorded, and they were timed during their reads. The average reading time per slide was 129 seconds during the unassisted phase versus 58 seconds during the AI-assisted phase, resulting in an overall efficiency gain of 55% ( P <0.001). These efficiency gains are applied to both benign and malignant WSIs. Two of the 3 reading pathologists experienced significant sensitivity improvements, from 74.5% to 93.5% ( P ≤0.006). This study highlights that AI can help pathologists shorten their reading times by more than half and also improve their metastasis detection rate.

Clinical Validation of Artificial Intelligence-Augmented Pathology Diagnosis Demonstrates Significant Gains in Diagnostic Accuracy in Prostate Cancer Detection.

CONTEXT.­: Prostate cancer diagnosis rests on accurate assessment of tissue by a pathologist. The application of artificial intelligence (AI) to digitized whole slide images (WSIs) can aid pathologists in cancer diagnosis, but robust, diverse evidence in a simulated clinical setting is lacking. OBJECTIVE.­: To compare the diagnostic accuracy of pathologists reading WSIs of prostatic biopsy specimens with and without AI assistance. DESIGN.­: Eighteen pathologists, 2 of whom were genitourinary subspecialists, evaluated 610 prostate needle core biopsy WSIs prepared at 218 institutions, with the option for deferral. Two evaluations were performed sequentially for each WSI: initially without assistance, and immediately thereafter aided by Paige Prostate (PaPr), a deep learning-based system that provides a WSI-level binary classification of suspicious for cancer or benign and pinpoints the location that has the greatest probability of harboring cancer on suspicious WSIs. Pathologists' changes in sensitivity and specificity between the assisted and unassisted modalities were assessed, together with the impact of PaPr output on the assisted reads. RESULTS.­: Using PaPr, pathologists improved their sensitivity and specificity across all histologic grades and tumor sizes. Accuracy gains on both benign and cancerous WSIs could be attributed to PaPr, which correctly classified 100% of the WSIs showing corrected diagnoses in the PaPr-assisted phase. CONCLUSIONS.­: This study demonstrates the effectiveness and safety of an AI tool for pathologists in simulated diagnostic practice, bridging the gap between computational pathology research and its clinical application, and resulted in the first US Food and Drug Administration authorization of an AI system in pathology.

Independent real-world application of a clinical-grade automated prostate cancer detection system.

Artificial intelligence (AI)-based systems applied to histopathology whole-slide images have the potential to improve patient care through mitigation of challenges posed by diagnostic variability, histopathology caseload, and shortage of pathologists. We sought to define the performance of an AI-based automated prostate cancer detection system, Paige Prostate, when applied to independent real-world data. The algorithm was employed to classify slides into two categories: benign (no further review needed) or suspicious (additional histologic and/or immunohistochemical analysis required). We assessed the sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs) of a local pathologist, two central pathologists, and Paige Prostate in the diagnosis of 600 transrectal ultrasound-guided prostate needle core biopsy regions ('part-specimens') from 100 consecutive patients, and to ascertain the impact of Paige Prostate on diagnostic accuracy and efficiency. Paige Prostate displayed high sensitivity (0.99; CI 0.96-1.0), NPV (1.0; CI 0.98-1.0), and specificity (0.93; CI 0.90-0.96) at the part-specimen level. At the patient level, Paige Prostate displayed optimal sensitivity (1.0; CI 0.93-1.0) and NPV (1.0; CI 0.91-1.0) at a specificity of 0.78 (CI 0.64-0.89). The 27 part-specimens considered by Paige Prostate as suspicious, whose final diagnosis was benign, were found to comprise atrophy (n = 14), atrophy and apical prostate tissue (n = 1), apical/benign prostate tissue (n = 9), adenosis (n = 2), and post-atrophic hyperplasia (n = 1). Paige Prostate resulted in the identification of four additional patients whose diagnoses were upgraded from benign/suspicious to malignant. Additionally, this AI-based test provided an estimated 65.5% reduction of the diagnostic time for the material analyzed. Given its optimal sensitivity and NPV, Paige Prostate has the potential to be employed for the automated identification of patients whose histologic slides could forgo full histopathologic review. In addition to providing incremental improvements in diagnostic accuracy and efficiency, this AI-based system identified patients whose prostate cancers were not initially diagnosed by three experienced histopathologists. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Validation of cell-cycle arrest biomarkers for acute kidney injury using clinical adjudication.

RATIONALE: We recently reported two novel biomarkers for acute kidney injury (AKI), tissue inhibitor of metalloproteinases (TIMP)-2 and insulin-like growth factor binding protein 7 (IGFBP7), both related to G1 cell cycle arrest. OBJECTIVES: We now validate a clinical test for urinary [TIMP-2]·[IGFBP7] at a high-sensitivity cutoff greater than 0.3 for AKI risk stratification in a diverse population of critically ill patients. METHODS: We conducted a prospective multicenter study of 420 critically ill patients. The primary analysis was the ability of urinary [TIMP-2]·[IGFBP7] to predict moderate to severe AKI within 12 hours. AKI was adjudicated by a committee of three independent expert nephrologists who were masked to the results of the test. MEASUREMENTS AND MAIN RESULTS: Urinary TIMP-2 and IGFBP7 were measured using a clinical immunoassay platform. The primary endpoint was reached in 17% of patients. For a single urinary [TIMP-2]·[IGFBP7] test, sensitivity at the prespecified high-sensitivity cutoff of 0.3 (ng/ml)(2)/1,000 was 92% (95% confidence interval [CI], 85-98%) with a negative likelihood ratio of 0.18 (95% CI, 0.06-0.33). Critically ill patients with urinary [TIMP-2]·[IGFBP7] greater than 0.3 had seven times the risk for AKI (95% CI, 4-22) compared with critically ill patients with a test result below 0.3. In a multivariate model including clinical information, urinary [TIMP-2]·[IGFBP7] remained statistically significant and a strong predictor of AKI (area under the curve, 0.70, 95% CI, 0.63-0.76 for clinical variables alone, vs. area under the curve, 0.86, 95% CI, 0.80-0.90 for clinical variables plus [TIMP-2]·[IGFBP7]). CONCLUSIONS: Urinary [TIMP-2]·[IGFBP7] greater than 0.3 (ng/ml)(2)/1,000 identifies patients at risk for imminent AKI. Clinical trial registered with www.clinicaltrials.gov (NCT 01573962).

Clinical outcomes with selectively constrained SECURE-C cervical disc arthroplasty: two-year results from a prospective, randomized, controlled, multicenter investigational device exemption study.

STUDY DESIGN: Prospective, multicenter, randomized, and controlled Investigational Device Exemption clinical trial. OBJECTIVE: To compare the clinical safety and effectiveness of the selectively constrained SECURE-C (Globus Medical, Audubon, PA) Cervical Artificial Disc to anterior cervical discectomy and fusion (ACDF). SUMMARY OF BACKGROUND DATA: Cervical total disc replacement has been developed as an alternative to ACDF by allowing segmental motion. Current cervical total disc replacement designs incorporate constrained and unconstrained metal-on-metal or metal-on-polymer articulating designs with various means of fixation. METHODS: A total of 380 patients from 18 investigational sites were prospectively enrolled in the study. Patients were randomized, treated surgically, and evaluated postoperatively at 6 weeks, 3 months, 6 months, 12 months, and 24 months. Clinical outcomes include overall success, visual analogue scale pain (right arm, left arm, and neck), neck disability index, neurological status, Short Form 36 (SF-36) Health Status Survey questionnaires, range of motion, and adverse events. Bayesian statistical methods were used to analyze the outcomes. RESULTS: Overall success results demonstrated statistical superiority of the randomized SECURE-C group compared with the randomized ACDF group at 24 months, with a posterior probability of 100% using the protocol-specified criteria and 98.1% using Food and Drug Administration-defined criteria. At 24 months postoperatively, SECURE-C demonstrated clinically significant improvement in pain and function in terms of neck disability index, visual analogue scale, and 36-Item Short Form Health Survey. At 24 months, the percentage of patients experiencing secondary surgical interventions at the index level was statistically lower for the SECURE-C group (2.5%) than the ACDF group (9.7%). At 24 months, 84.6% of as-treated SECURE-C patients were range-of-motion successes. Satisfaction was high among SECURE-C patients. CONCLUSION: The selectively constrained SECURE-C Cervical Artificial Disc is as safe and effective as the standard of care, an anterior cervical discectomy and fusion. SECURE-C is statistically superior in terms of overall success, index-level subsequent surgical procedures, and patient satisfaction, making it an attractive surgical option for patients with symptomatic cervical disc disease. LEVEL OF EVIDENCE: 1.

Effects of the combination of metyrapone and oxazepam on cocaine craving and cocaine taking: a double-blind, randomized, placebo-controlled pilot study.

Although cocaine dependence affects an estimated 1.6 million people in the USA, there are currently no medications approved for the treatment of this disorder. Experiments performed in animal models have demonstrated that inhibitors of the stress response effectively reduce intravenous cocaine self-administration. This exploratory, double-blind, placebo-controlled study was designed to assess the safety and efficacy of combinations of the cortisol synthesis inhibitor metyrapone, and the benzodiazepine oxazepam, in 45 cocaine-dependent individuals. The subjects were randomized to a total daily dose of 500 mg metyrapone/20 mg oxazepam (low dose), a total daily dose of 1500 mg metyrapone/20 mg oxazepam (high dose), or placebo for 6 weeks of treatment. The outcome measures were a reduction in cocaine craving and associated cocaine use as determined by quantitative measurements of the cocaine metabolite benzoylecgonine (BE) in urine at all visits. Of the randomized subjects, 49% completed the study. The combination of metyrapone and oxazepam was well tolerated and tended to reduce cocaine craving and cocaine use, with significant reductions at several time points when controlling for baseline scores. These data suggest that further assessments of the ability of the metyrapone and oxazepam combination to support cocaine abstinence in cocaine-dependent subjects are warranted.