Causality-enriched epigenetic age uncouples damage and adaptation.

Machine learning models based on DNA methylation data can predict biological age but often lack causal insights. By harnessing large-scale genetic data through epigenome-wide Mendelian randomization, we identified CpG sites potentially causal for aging-related traits. Neither the existing epigenetic clocks nor age-related differential DNA methylation are enriched in these sites. These CpGs include sites that contribute to aging and protect against it, yet their combined contribution negatively affects age-related traits. We established a new framework to introduce causal information into epigenetic clocks, resulting in DamAge and AdaptAge-clocks that track detrimental and adaptive methylation changes, respectively. DamAge correlates with adverse outcomes, including mortality, while AdaptAge is associated with beneficial adaptations. These causality-enriched clocks exhibit sensitivity to short-term interventions. Our findings provide a detailed landscape of CpG sites with putative causal links to lifespan and healthspan, facilitating the development of aging biomarkers, assessing interventions, and studying reversibility of age-associated changes.

Outcomes of Artificial Intelligence Volumetric Assessment of Kidneys and Renal Tumors for Preoperative Assessment of Nephron-Sparing Interventions.

Background: Renal-cell carcinoma is the most common kidney cancer and the 13th most common cause of cancer death worldwide. Partial nephrectomy and percutaneous ablation, increasingly utilized to treat small renal masses and preserve renal parenchyma, require precise preoperative imaging interpretation. We sought to develop and evaluate a convolutional neural network (CNN), a type of deep learning (DL) artificial intelligence (AI), to act as a surgical planning aid by determining renal tumor and kidney volumes through segmentation on single-phase CT. Materials and Methods: After Institutional Review Board approval, the CT images of 319 patients were retrospectively analyzed. Two distinct CNNs were developed for (1) bounding cube localization of the right and left hemiabdomen and (2) segmentation of the renal parenchyma and tumor within each bounding cube. Training was performed on a randomly selected cohort of 269 patients. CNN performance was evaluated on a separate cohort of 50 patients using Sorensen-Dice coefficients (which measures the spatial overlap between the manually segmented and neural network-derived segmentations) and Pearson correlation coefficients. Experiments were run on a graphics processing unit-optimized workstation with a single NVIDIA GeForce GTX Titan X (12GB, Maxwell Architecture). Results: Median Dice coefficients for kidney and tumor segmentation were 0.970 and 0.816, respectively; Pearson correlation coefficients between CNN-generated and human-annotated estimates for kidney and tumor volume were 0.998 and 0.993 (p < 0.001), respectively. End-to-end trained CNNs were able to perform renal parenchyma and tumor segmentation on a new test case in an average of 5.6 seconds. Conclusions: Initial experience with automated DL AI demonstrates that it is capable of rapidly and accurately segmenting kidneys and renal tumors on single-phase contrast-enhanced CT scans and calculating tumor and renal volumes.

Multiple renal capillary hemangiomas in a patient with end-stage renal disease.

Renal capillary hemangiomas are rare and benign vascular tumors which are typically incidentally discovered on imaging. Surgical excision is often performed, as imaging appearance is similar to malignant lesions. Renal hemangiomas are typically solitary and unilateral. We present a rare case of multiple renal capillary hemangiomas in a patient with end-stage renal disease. Two hemangiomas were detected on imaging and 2 smaller hemangiomas were detected upon pathological evaluation, suggesting there may be a wider prevalence of smaller, radiographically-occult renal hemangiomas.

Significance and Prevalence of Haziness Surrounding the Hepatic Artery and Celiac Axis on Computed Tomographic Imaging After Pancreaticoduodenectomy.

PURPOSE: This study was conducted to assess the prevalence and significance of "haziness" around the hepatic artery and celiac axis in patients after pancreaticoduodenectomy. METHODS: This retrospective study was conducted on 116 patients who underwent pancreaticoduodenectomy or a similar procedure and had no clinical evidence of tumor recurrence or malignancy within 2 years from the date of surgery. RESULTS: Most images exhibited at least mild to moderate haziness around the hepatic artery and celiac axis. Patients with benign vs malignant results on formal pathology had no significant difference in severity of findings. Haziness remained in the mild to moderate range 2 years after surgery. CONCLUSIONS: Mild to moderate soft tissue stranding with increased attenuation around the hepatic artery and celiac axis is a common finding after pancreaticoduodenectomy that may persist for years after surgery. Such haziness alone has low specificity for tumor recurrence and should not be regarded as an indicator of malignancy.