Transfer learning may explain pigeons' ability to detect cancer in histopathology.

Pigeons' unexpected competence in learning to categorize unseen histopathological images has remained an unexplained discovery for almost a decade (Levensonet al2015PLoS One10e0141357). Could it be that knowledge transferred from their bird's-eye views of the earth's surface gleaned during flight contributes to this ability? Employing a simulation-based verification strategy, we recapitulate this biological phenomenon with a machine-learning analog. We model pigeons' visual experience during flight with the self-supervised pre-training of a deep neural network on BirdsEyeViewNet; our large-scale aerial imagery dataset. As an analog of the differential food reinforcement performed in Levensonet al's study 2015PLoS One10e0141357), we apply transfer learning from this pre-trained model to the same Hematoxylin and Eosin (H&E) histopathology and radiology images and tasks that the pigeons were trained and tested on. The study demonstrates that pre-training neural networks with bird's-eye view data results in close agreement with pigeons' performance. These results support transfer learning as a reasonable computational model of pigeon representation learning. This is further validated with six large-scale downstream classification tasks using H&E stained whole slide image datasets representing diverse cancer types.

[Neurosurgical treatment of tumors of the pineal region - literature review and overview of cases at OMIII]. / A pineális régió tumorainak idegsebészeti ellátása ­ irodalmi áttekintés és az OMIII beteganyagának vizsgálata.

Pineal region tumors account for less than 1% of adult supratentorial tumors. Their treatment requires a multimodality approach. Previously, the treatment of choice was direct surgery, which is associated with high surgical risk. Advances in minimally invasive techniques and onco-radiotherapy offer a safe and multimodal personalized therapy. The aim of our study was to describe the practice of our Institute based on combined endoscopic and radiotherapy techniques. We performed a retrospective clinical study. We processed data from 23 adult patients who underwent endoscopic third ventricle fenestration and pineal tumor biopsy between 2014 and 2023. Descriptive statistics, t-test, Fisher's exact test and Kaplan-Meier analysis were performed. Clinical improvement with endoscopic intervention was achieved in 78.3% of cases. Significant increase in preoperative performance status was observed in the postoperative period (p=2.755e-5), and radiotherapy resulted in regression or stable disease. Our results suggest a safe treatment with good clinical outcome and an excellent alternative to direct surgery.

Are Artificial Intelligence-Assisted Three-Dimensional Histological Reconstructions Reliable for the Assessment of Trabecular Microarchitecture?

Objectives: This study aimed to create a three-dimensional histological reconstruction through the AI-assisted classification of tissues and the alignment of serial sections. The secondary aim was to evaluate if the novel technique for histological reconstruction accurately replicated the trabecular microarchitecture of bone. This was performed by conducting micromorphometric measurements on the reconstruction and comparing the results obtained with those of microCT reconstructions. Methods: A bone biopsy sample was harvested upon re-entry following sinus floor augmentation. Following microCT scanning and histological processing, a modified version of the U-Net architecture was trained to categorize tissues on the sections. Detector-free local feature matching with transformers was used to create the histological reconstruction. The micromorphometric parameters were calculated using Bruker's CTAn software (version 1.18.8.0, Bruker, Kontich, Belgium) for both histological and microCT datasets. Results: Correlation coefficients calculated between the micromorphometric parameters measured on the microCT and histological reconstruction suggest a strong linear relationship between the two with p-values of 0.777, 0.717, 0.705, 0.666, and 0.687 for BV/TV, BS/TV, Tb.Pf Tb.Th, and Tb.Sp, respectively. Bland-Altman and mountain plots suggest good agreement between BV/TV measurements on the two reconstruction methods. Conclusions: This novel method for three-dimensional histological reconstruction provides researchers with a tool that enables the assessment of accurate trabecular microarchitecture and histological information simultaneously.

[New, innovative prognosis calculator for patients with metastatic spinal tumors]. / Új, innovatív prognóziskalkulátor áttétes gerinctumoros betegek számára.

Background and purpose: The aim of our research was to create a scoring system that predicts prognosis and recommends therapeutic options for patients with metastatic spine tumor. Increasing oncological treatment opportunities and prolonged survival have led to a growing need to address clinical symptoms caused by meta-stases of the primary tumor. Spinal metastases can cause a significant reduction in quality of life due to the caused neurological deficits. A scoring system that predicts prognosis with sufficient accuracy could help us to achieve personalised treatment options. Methods: Methods - We performed a retrospective clinical research of data from patients over 18 years of age who underwent surgery due to symptomatic spinal metastasis at the National Institute of Mental Disorders, Neurology and Neurosurgery between 2008 and 2018. Data from 454 patients were analysed. Survival analysis (Kaplan-Meier, log-rank, Cox model) was performed, network science-based correlation analysis was used to select the proper prognostic factors of our scoring system, such that its C value (predictive ability index) was maximized. Results: Multivariate Cox analysis resulted in the identification of 5 independent prognostic factors (primary tumour type, age, ambulatory status, internal organ metastases, serum protein level). Our system predicted with an average accuracy of 70.6% over the 10-year study period. Conclusion: Our large case series of surgical dataset of patients with symptomatic spinal metastasis was used to create a risk calculator system that can help in the choice of therapy. Our risk calculator is also available online at https://emk.semmelweis.hu/gerincmet.