Araştırma Çıktıları

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    Predicting intra-operative and postoperative consequential events using machine-learning techniques in patients undergoing robot-assisted partial nephrectomy: a Vattikuti Collective Quality Initiative database study
    (WILEY, 2020-01-01) Bhandari, Mahendra; Nallabasannagari, Anubhav Reddy; Reddiboina, Madhu; Porter, James R.; Jeong, Wooju; Mottrie, Alexandre; Dasgupta, Prokar; Challacombe, Ben; Abaza, Ronney; Rha, Koon Ho; Parekh, Dipen J.; Ahlawat, Rajesh; Capitanio, Umberto; Yuvaraja, Thyavihally B.; Rawal, Sudhir; Moon, Daniel A.; Buffi, Nicolo M.; Sivaraman, Ananthakrishnan; Maes, Kris K.; Porpiglia, Francesco; Gautam, Gagan; Turkeri, Levent; Meyyazhgan, Kohul Raj; Patil, Preethi; Menon, Mani; Rogers, Craig
    Objective To predict intra-operative (IOEs) and postoperative events (POEs) consequential to the derailment of the ideal clinical course of patient recovery. Materials and Methods The Vattikuti Collective Quality Initiative is a multi-institutional dataset of patients who underwent robot-assisted partial nephectomy for kidney tumours. Machine-learning (ML) models were constructed to predict IOEs and POEs using logistic regression, random forest and neural networks. The models to predict IOEs used patient demographics and preoperative data. In addition to these, intra-operative data were used to predict POEs. Performance on the test dataset was assessed using area under the receiver-operating characteristic curve (AUC-ROC) and area under the precision-recall curve (PR-AUC). Results The rates of IOEs and POEs were 5.62\% and 20.98\%, respectively. Models for predicting IOEs were constructed using data from 1690 patients and 38 variables
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    Information theory approaches to improve glioma diagnostic workflows in surgical neuropathology
    (WILEY, 2022-01-01) Cevik, Lokman; Landrove, Marilyn Vazquez; Aslan, Mehmet Tahir; Khammad, Vasilii; Garagorry Guerra, Francisco Jose; Cabello-Izquierdo, Yolanda; Wang, Wesley; Zhao, Jing; Becker, Aline Paixao; Czeisler, Catherine; Rendeiro, Anne Costa; Sousa Veras, Lucas Luis; Zanon, Maicon Fernando; Reis, Rui Manuel; Matsushita, Marcus de Medeiros; Ozduman, Koray; Pamir, M. Necmettin; Danyeli, Ayca Ersen; Pearce, Thomas; Felicella, Michelle; Eschbacher, Jennifer; Arakaki, Naomi; Martinetto, Horacio; Parwani, Anil; Thomas, Diana L.; Otero, Jose Javier
    Aims Resource-strained healthcare ecosystems often struggle with the adoption of the World Health Organization (WHO) recommendations for the classification of central nervous system (CNS) tumors. The generation of robust clinical diagnostic aids and the advancement of simple solutions to inform investment strategies in surgical neuropathology would improve patient care in these settings. Methods We used simple information theory calculations on a brain cancer simulation model and real-world data sets to compare contributions of clinical, histologic, immunohistochemical, and molecular information. An image noise assay was generated to compare the efficiencies of different image segmentation methods in H\&E and Olig2 stained images obtained from digital slides. An auto-adjustable image analysis workflow was generated and compared with neuropathologists for p53 positivity quantification. Finally, the density of extracted features of the nuclei, p53 positivity quantification, and combined ATRX/age feature was used to generate a predictive model for 1p/19q codeletion in IDH-mutant tumors. Results Information theory calculations can be performed on open access platforms and provide significant insight into linear and nonlinear associations between diagnostic biomarkers. Age, p53, and ATRX status have significant information for the diagnosis of IDH-mutant tumors. The predictive models may facilitate the reduction of false-positive 1p/19q codeletion by fluorescence in situ hybridization (FISH) testing. Conclusions We posit that this approach provides an improvement on the cIMPACT-NOW workflow recommendations for IDH-mutant tumors and a framework for future resource and testing allocation.
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    Understanding the Role of the Microbiome in Cancer Diagnostics and Therapeutics by Creating and Utilizing ML Models
    (MDPI, 2022-01-01) Cekikj, Miodrag; Jakimovska Ozdemir, Milena; Kalajdzhiski, Slobodan; Ozcan, Orhan; Sezerman, Osman Ugur
    Simple Summary Cancer is one of the leading causes of death worldwide. Colorectal cancer belongs to the group of the most malignant tumors for which their burden can be only reduced through early detection and appropriate treatment. Increasing evidence indicates that the intestine microbiota is related and can impact colorectal carcinogenesis. This study proposes a multidisciplinary approach of two-phase methodology for modeling and interpreting the key biomarkers that can play a significant role in understanding the drug-resistant mechanism for patients diagnosed with colorectal cancer. The proposed methodology was evaluated using a publicly accessible dataset, which may serve clinicians as a complementary analysis tool in colorectal cancer diagnostics and therapeutics. This study contributes to the field of predictive modeling in healthcare. Recent studies have highlighted that gut microbiota can alter colorectal cancer susceptibility and progression due to its impact on colorectal carcinogenesis. This work represents a comprehensive technical approach in modeling and interpreting the drug-resistance mechanisms from clinical data for patients diagnosed with colorectal cancer. To accomplish our aim, we developed a methodology based on evaluating high-performance machine learning models where a Python-based random forest classifier provides the best performance metrics, with an overall accuracy of 91.7\%. Our approach identified and interpreted the most significant genera in the cases of resistant groups. Thus far, many studies point out the importance of present genera in the microbiome and intend to treat it separately. The symbiotic bacterial analysis generated different sets of joint feature combinations, providing a combined overview of the model's predictiveness and uncovering additional data correlations where different genera joint impacts support the therapy-resistant effect. This study points out the different perspectives of treatment since our aggregate analysis gives precise results for the genera that are often found together in a resistant group of patients, meaning that resistance is not due to the presence of one pathogenic genus in the patient microbiome, but rather several bacterial genera that live in symbiosis.
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    Hey Siri! Perform a type 3 hysterectomy. Please watch out for the ureter!'' What is autonomous surgery and what are the latest developments?
    (GALENOS YAYINCILIK, 2021-01-01) Gultekin, Ismail Burak; Karabuk, Emine; Kose, Mehmet Faruk
    As a result of major advances in deep learning algorithms and computer processing power, there have been important developments in the fields of medicine and robotics. Although fully autonomous surgery systems where human impact will be minimized are still a long way off, systems with partial autonomy have gradually entered clinical use. In this review, articles on autonomous surgery classified and summarized, with the aim of informing the reader about questions such as `` What is autonomic surgery?{''} and in which areas studies are progressing.