Cytoreductive nephrectomy in the current treatment algorithm

The two recent prospective randomized trials CARMENA and SURTIME have changed the therapy paradigm of metastatic renal cell carcinoma. The CARMENA trial was conducted to investigate whether cytoreductive nephrectomy (CN) is required in the targeted therapy area, whereas SURTIME studied whether deferred CN in combination with sunitinib can be used to identify patients with inherent targeted therapy resistance. In the current review, we provide a comprehensive discussion of two randomized studies and the current evidence with up-do-date algorithms for treating primary metastatic clear-cell renal cell carcinoma in the era of targeted therapy and immune-checkpoint inhibition

The adjuvant treatment of kidney cancer: a multidisciplinary outlook

Approximately 70% of cases of kidney cancer are localized or locally advanced at diagnosis. Among patients who undergo surgery for these cancers, 30–35% will eventually develop potentially fatal metachronous distant metastases. Effective adjuvant treatments are urgently needed to reduce the risk of recurrence of kidney cancer and of dying of metastatic disease. To date, almost all of the tested adjuvant agents have failed to demonstrate any benefit. Only two trials of an autologous renal tumour cell vaccine and of the vascular endothelial growth factor receptor (VEGFR) tyrosine kinase inhibitor sunitinib have shown positive results, but these have been criticized for methodological reasons and conflicting data, respectively. The results of two additional trials of targeted agents as adjuvant therapies have not yet been published. Novel immune checkpoint inhibitors are promising approaches to adjuvant therapy in kidney cancer, and a number of trials are now underway. An important component of the management of patients with kidney cancer, particularly those who undergo radical resection for localized renal cell carcinoma, is the preservation of kidney function to reduce morbidity and mortality. The optimal management of these patients therefore requires a multidisciplinary approach involving nephrologists, oncologists, urologists and pathologists

Contrast is all you need

In this study, we analyze data-scarce classification scenarios, where available labeled legal data is small and imbalanced, potentially hurting the quality of the results. We focused on two finetuning objectives; SetFit (Sentence Transformer Finetuning), a contrastive learning setup, and a vanilla finetuning setup on a legal provision classification task. Additionally, we compare the features that are extracted with LIME (Local Interpretable Model-agnostic Explanations) to see which particular features contributed to the model’s classification decisions. The results show that a contrastive setup with SetFit performed better than vanilla finetuning while using a fraction of the training samples. LIME results show that the contrastive learning approach helps boost both positive and negative features which are legally informative and contribute to the classification results. Thus a model finetuned with a contrastive objective seems to base its decisions more confidently on legally informative features