Predictive effect of the systemic inflammation response index (SIRI) on the efficacy and prognosis of neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer.

BACKGROUND: Inflammation is a part of tumours, and inflammatory cells can affect the proliferation, invasion, and development of tumour cells. An increasing number of peripheral blood inflammatory markers have been found to play very important roles in the treatment and prognosis of cancer patients. The systemic inflammatory response index (SIRI) is a newer inflammatory marker, and its role in colorectal cancer, especially in locally advanced rectal cancer, is still unclear. METHODS: From 2015 to 2020, 198 patients with locally advanced rectal cancer (LARC) who underwent surgery following neoadjuvant chemoradiotherapy (Neo-CRT) were analysed. Patients were categorized into good- and poor- response groups according to their pathological results, and clinical characteristics and baseline parameters were compared between the two groups. The optimal cutoff values for inflammatory indicators were determined using receiver operating characteristic (ROC) analysis. Univariate and multivariate analyses were performed using the Cox proportional hazard model. Survival analysis was performed via the Kaplan‒Meier method. RESULTS: After patients were grouped into good and poor response groups, indicator differences were found in CEA, neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), and SIRI. According to the ROC analysis, the NLR (P = 0.015), SII (P = 0.001), and SIRI (P = 0.029) were significant prognostic factors. After univariate and multivariate analyses of the Cox proportional hazards regression model, only the SIRI was found to be an independent prognostic factor for overall survival (OS) and disease-free survival (DFS). Finally, Kaplan‒Meier survival curves also confirmed the ability of the SIRI to predict survival. CONCLUSION: The preoperative SIRI can be used to predict the response to Neo-CRT in LARC patients and is an independent predictor of OS and DFS in postoperative patients. A high SIRI was associated with poor radiotherapy response and predicted poor OS and DFS.

Assessing anatomical variations of the inferior mesenteric artery via three-dimensional CT angiography and laparoscopic colorectal surgery: a retrospective observational study.

To assess the anatomy of the inferior mesenteric artery (IMA) and its branches by reviewing laparoscopic left-sided colorectal cancer surgery videos and comparing them with preoperative three-dimensional computed tomography (3D-CT) angiography, to verify the accuracy of 3D-CT vascular reconstruction techniques. High-definition surgical videos and preoperative imaging data of 200 patients who underwent laparoscopic left-sided colorectal cancer surgery were analysed, and the alignment of the IMA and its branches in relation to the inferior mesenteric vein (IMV) was observed and summarized. The above two methods were used to measure the length of the IMA and its branches. Of 200 patients, 47.0% had the sigmoid arteries (SAs) arise from the common trunk with the superior rectal artery (SRA), and 30.5% had the SAs arise from the common trunk with the left colic artery (LCA). In 3.5% of patients, the SAs arising from both the LCA and SRA. The LCA, SA, and SRA emanated from the same point in 13.5% of patients, and the LCA was absent in 5.5% of patients. The range of D cm (IMA length measured by intraoperative silk thread) and d cm (IMA length measured by 3D-CT vascular reconstruction) in all cases was 1.84-6.62 cm and 1.85-6.52 cm, respectively, and there was a significant difference between them. (p < 0.001). The lengths between the intersection of the LCA and IMV measured intraoperatively were 0.64-4.29 cm, 0.87-4.35 cm, 1.32-4.28 cm and 1.65-3.69 cm in types 1A, 1B, 1C, and 2, respectively, and there was no significant difference between the groups (p = 0.994). There was only a significant difference in the length of the IMA between the 3D-CT vascular reconstruction and intraoperative observation data, which can provide guidance to surgeons in preoperative preparation.

PyHFO: lightweight deep learning-powered end-to-end high-frequency oscillations analysis application.

Objective. This study aims to develop and validate an end-to-end software platform, PyHFO, that streamlines the application of deep learning (DL) methodologies in detecting neurophysiological biomarkers for epileptogenic zones from EEG recordings.Approach. We introduced PyHFO, which enables time-efficient high-frequency oscillation (HFO) detection algorithms like short-term energy and Montreal Neurological Institute and Hospital detectors. It incorporates DL models for artifact and HFO with spike classification, designed to operate efficiently on standard computer hardware.Main results. The validation of PyHFO was conducted on three separate datasets: the first comprised solely of grid/strip electrodes, the second a combination of grid/strip and depth electrodes, and the third derived from rodent studies, which sampled the neocortex and hippocampus using depth electrodes. PyHFO demonstrated an ability to handle datasets efficiently, with optimization techniques enabling it to achieve speeds up to 50 times faster than traditional HFO detection applications. Users have the flexibility to employ our pre-trained DL model or use their EEG data for custom model training.Significance. PyHFO successfully bridges the computational challenge faced in applying DL techniques to EEG data analysis in epilepsy studies, presenting a feasible solution for both clinical and research settings. By offering a user-friendly and computationally efficient platform, PyHFO paves the way for broader adoption of advanced EEG data analysis tools in clinical practice and fosters potential for large-scale research collaborations.

Discovering Neurophysiological Characteristics of Pathological High-Frequency Oscillations in Epilepsy with an Explainable Deep Generative Model.

Interictal high-frequency oscillation (HFO) is a promising biomarker of the epileptogenic zone (EZ). However, objective definitions to distinguish between pathological and physiological HFOs have remained elusive, impeding HFOs' clinical applications. We employed self-supervised deep generative variational autoencoders to learn such discriminative HFO features directly from their morphologies in a data-driven manner. We studied a large retrospective cohort of 185 patients who underwent intracranial monitoring and analyzed 686,410 candidate HFO events collected from 18,265 brain contacts across diverse brain regions. The model automatically clustered HFOs into distinct morphological groups in the latent space. One cluster consisted of putative morphologically defined pathological HFOs (mpHFOs): HFOs in that cluster were observed to be associated with spikes and exhibited high signal intensity both in the HFO band (>80 Hz) at detection and in the sub-HFO band (10-80 Hz) surrounding the detection and were primarily localized in the seizure onset zone (SOZ). Moreover, resection of brain regions based on a higher prevalence of interictal mpHFOs better predicted postoperative seizure outcomes than current clinical standards based on SOZ removal. Our self-supervised, explainable, deep generative model distills pathological HFOs and thus potentially helps delineate the EZ purely from interictal intracranial EEG data.