Fracture healing is a complex cascade of cellular and molecular processes. These processes require the appropriate cellular and molecular environment to ensure the restoration of skeletal stability and resolution of inflammation. In order for fracture healing to occur, the necessary building blocks for bone metabolism and synthesis must be supplied through proper nutrition. Pharmacologic therapies aimed at modulating the inflammatory response to fractures have the potential to interfere with the synthesis of molecules needed for the production of bone. Infection can interfere with, and even prevent normal fracture healing from occurring. Cellular and genetic treatment strategies are actively being developed to target deficiencies, and bridge gaps that can influence how fractures heal. Evolving technologies, including nutritional supplementation, pharmacotherapies, antibiotics, surgical techniques, as well as genetic and cellular therapies, have the potential to enhance, optimize, and even revolutionize the process of fracture healing.

Introduction

Upper tract urothelial carcinoma (UTUC) accounts for <5% of all urothelial cancers. We aimed to ascertain the clinical differences between UTUC tumours presenting de novo (DnUTUC) and those presenting secondary (SUTUC) following a bladder cancer diagnosis.

Methods

Our institutional database was queried for all UTUC patients who were surgically treated with radical nephroureterectomy or ureterectomy between 2003 and 2017. Bladder recurrence and cancer-specific mortality were compared. To reduce the possible bias due to confounding variables obtained from a simple comparison of outcomes, DnUTUC patients were matched (for age, gender, tumour location, type of surgery, grade, TNM staging, presence of carcinoma in situ, and lymphovascular invasion) with propensity score to SUTUC patients. Bladder recurrence and cancer-specific mortality were assessed with Cox proportional hazards model.

Results

A total of 117 UTUC patients were identified: 80 with DnUTUC (68.4%) and 37 with SUTUC (31.6%). A greater proportion of males with SUTUC was demonstrated (89.2% vs. 68.8; p=0.02). In both groups, 67.5% of patients had high-grade disease, but SUTUC demonstrated a higher carcinoma in situ rate (43.2% vs. 25%; p=0.047). Univariate analysis demonstrated that the five-year bladder recurrence rate was trending to be higher in SUTUC (65.3% vs. 20.5%; p=0.099). In the Cox model, however, it was associated with increased bladder recurrence (hazard ratio [HR] 3.69; 95% confidence interval [CI] 1.68-8.09; p=0.001). Although univariate analysis demonstrated that SUTUC patients were more likely to die of their disease (30.6% vs. 9%; p=0.009), the multivariable Cox model did not demonstrate this association. The limitations of this study include its retrospective, single-centre design and relatively small cohort of patients.

Conclusions

In this hypothesis-generating study, some evidence suggests that further research is needed to delineate differences between SUTUC and DnUTUC.

Five years after the last prostatic carcinoma grading consensus conference of the International Society of Urological Pathology (ISUP), accrual of new data and modification of clinical practice require an update of current pathologic grading guidelines. This manuscript summarizes the proceedings of the ISUP consensus meeting for grading of prostatic carcinoma held in September 2019, in Nice, France. Topics brought to consensus included the following: (1) approaches to reporting of Gleason patterns 4 and 5 quantities, and minor/tertiary patterns, (2) an agreement to report the presence of invasive cribriform carcinoma, (3) an agreement to incorporate intraductal carcinoma into grading, and (4) individual versus aggregate grading of systematic and multiparametric magnetic resonance imaging-targeted biopsies. Finally, developments in the field of artificial intelligence in the grading of prostatic carcinoma and future research perspectives were discussed.

The International Society of Urological Pathology (ISUP) hosts a reference image database supervised by experts with the purpose of establishing an international standard in prostate cancer grading. Here, we aimed to identify areas of grading difficulties and compare the results with those obtained from an artificial intelligence system trained in grading. In a series of 87 needle biopsies of cancers selected to include problematic cases, experts failed to reach a 2/3 consensus in 41.4% (36/87). Among consensus and non-consensus cases, the weighted kappa was 0.77 (range 0.68-0.84) and 0.50 (range 0.40-0.57), respectively. Among the non-consensus cases, four main causes of disagreement were identified: the distinction between Gleason score 3 + 3 with tangential cutting artifacts vs. Gleason score 3 + 4 with poorly formed or fused glands (13 cases), Gleason score 3 + 4 vs. 4 + 3 (7 cases), Gleason score 4 + 3 vs. 4 + 4 (8 cases) and the identification of a small component of Gleason pattern 5 (6 cases). The AI system obtained a weighted kappa value of 0.53 among the non-consensus cases, placing it as the observer with the sixth best reproducibility out of a total of 24. AI may serve as a decision support and decrease inter-observer variability by its ability to make consistent decisions. The grading of these cancer patterns that best predicts outcome and guides treatment warrants further clinical and genetic studies. Results of such investigations should be used to improve calibration of AI systems.

AIMS: Despite efforts to standardise grading of prostate cancer, even among experts there is still a considerable variation in grading practices. In this study we describe the use of Pathology Imagebase, a novel reference image library, for setting an international standard in prostate cancer grading. METHODS AND RESULTS: The International Society of Urological Pathology (ISUP) recently launched a reference image database supervised by experts. A panel of 24 international experts in prostate pathology reviewed independently microphotographs of 90 cases of prostate needle biopsies with cancer. A linear weighted kappa of 0.67 (95% confidence interval = 0.62-0.72) and consensus was reached in 50 cases. The interobserver weighted kappa varied from 0.48 to 0.89. The highest level of agreement was seen for Gleason score (GS) 3 + 3 = 6 (ISUP grade 1), while higher grades and particularly GS 4 + 3 = 7 (ISUP grade 3) showed considerable disagreement. Once a two-thirds majority was reached, images were moved automatically into a public database available for all ISUP members at www.isupweb.org. Non-members are able to access a limited number of cases. CONCLUSIONS: It is anticipated that the database will assist pathologists to calibrate their grading and, hence, decrease interobserver variability. It will also help to identify instances where definitions of grades need to be clarified.

Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge-the largest histopathology competition to date, joined by 1,290 developers-to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted κ, 95% confidence interval (CI), 0.840-0.884) and 0.868 (95% CI, 0.835-0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials.