Immediate lymph node extraction improves retrieval rate following axillary lymph node dissection: an effective approach to improving guideline-concordant breast cancer care in Nigeria

Background

Axillary lymph node staging is essential for making therapeutic decisions and for prognostication. A minimum of ten lymph nodes is recommended for accurate staging. This study describes the process and outcomes of an audit cycle that resulted in a novel intervention instituted to improve concordance with guidelines.

Methods

The study began with a retrospective audit of lymph node retrieval following axillary lymph node dissection (ALND). Subsequent phases evaluated the efficacy of immediate lymph node extraction before fixation by comparing the mean number of lymph nodes and the proportion of guideline-concordant cases to retrospective data and concurrent cases without the intervention.

Results

The mean number of lymph nodes retrieved in the retrospective phase was 5.2, which is less than the recommended threshold. The intervention resulted in a significant increase in lymph node retrieval over the baseline rate (13.7 versus 5.2, p = 0.026). There was also a significantly higher number of lymph nodes following the intervention compared to concurrent cases managed during the same period without the intervention (13.7 versus 7.9, p = 0.004). The concordance rate was 89% in the intervention group compared to 47% in the non-intervention group (p = 0.019). There was no significant difference when the intervention was administered by either surgeons or pathologists (13.5 versus 12, p = 0.25).

Conclusion

Immediate extraction of lymph nodes resulted in significant improvement of concordant lymph node retrieval in all phases of the study. We recommend that this practice be validated in larger cohorts for possible recommendation as an effective way of improving lymph node retrieval following ALND.

Convolutional Neural Networks for the Evaluation of Chronic and Inflammatory Lesions in Kidney Transplant Biopsies

In kidney transplant biopsies, both inflammation and chronic changes are important features that predict long-term graft survival. Quantitative scoring of these features is important for transplant diagnostics and kidney research. However, visual scoring is poorly reproducible and labor intensive. The goal of this study was to investigate the potential of convolutional neural networks (CNNs) to quantify inflammation and chronic features in kidney transplant biopsies. A structure segmentation CNN and a lymphocyte detection CNN were applied on 125 whole-slide image pairs of periodic acid-Schiff- and CD3-stained slides. The CNN results were used to quantify healthy and sclerotic glomeruli, interstitial fibrosis, tubular atrophy, and inflammation within both nonatrophic and atrophic tubuli, and in areas of interstitial fibrosis. The computed tissue features showed high correlation with Banff lesion scores of five pathologists (A.A., A.Dend., J.H.B., J.K., and T.N.). Analyses on a small subset showed a moderate correlation toward higher CD3⁺ cell density within scarred regions and higher CD3⁺ cell count inside atrophic tubuli correlated with long-term change of estimated glomerular filtration rate. The presented CNNs are valid tools to yield objective quantitative information on glomeruli number, fibrotic tissue, and inflammation within scarred and non-scarred kidney parenchyma in a reproducible manner. CNNs have the potential to improve kidney transplant diagnostics and will benefit the community as a novel method to generate surrogate end points for large-scale clinical studies.