Critical Reviews in Oncogenesis最新文献_第4页

Integrating Cutting-Edge Methods to Oral Cancer Screening, Analysis, and Prognosis. 整合切割边缘方法对口腔癌症的筛查、分析和预后。

Critical Reviews in Oncogenesis Pub Date : 2023-01-01 DOI: 10.1615/CritRevOncog.2023047772

Sagar Dholariya, Ragini D Singh, Amit Sonagra, Dharamveer Yadav, Bhairavi N Vajaria, Deepak Parchwani

{"title":"Integrating Cutting-Edge Methods to Oral Cancer Screening, Analysis, and Prognosis.","authors":"Sagar Dholariya, Ragini D Singh, Amit Sonagra, Dharamveer Yadav, Bhairavi N Vajaria, Deepak Parchwani","doi":"10.1615/CritRevOncog.2023047772","DOIUrl":"10.1615/CritRevOncog.2023047772","url":null,"abstract":"<p><p>Oral cancer (OC) has become a significant barrier to health worldwide due to its high morbidity and mortality rates. OC is among the most prevalent types of cancer that affect the head and neck region, and the overall survival rate at 5 years is still around 50%. Moreover, it is a multifactorial malignancy instigated by genetic and epigenetic variabilities, and molecular heterogeneity makes it a complex malignancy. Oral potentially malignant disorders (OPMDs) are often the first warning signs of OC, although it is challenging to predict which cases will develop into malignancies. Visual oral examination and histological examination are still the standard initial steps in diagnosing oral lesions; however, these approaches have limitations that might lead to late diagnosis of OC or missed diagnosis of OPMDs in high-risk individuals. The objective of this review is to present a comprehensive overview of the currently used novel techniques viz., liquid biopsy, next-generation sequencing (NGS), microarray, nanotechnology, lab-on-a-chip (LOC) or microfluidics, and artificial intelligence (AI) for the clinical diagnostics and management of this malignancy. The potential of these novel techniques in expanding OC diagnostics and clinical management is also reviewed.</p>","PeriodicalId":35617,"journal":{"name":"Critical Reviews in Oncogenesis","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41214815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Preface: Oral Cancer: New Insights in Diagnosis, Prognosis, and Therapeutics to Management and Reconstruction. 前言：口腔癌症：诊断、预后和治疗管理和重建的新见解。

Critical Reviews in Oncogenesis Pub Date : 2023-01-01 DOI: 10.1615/CritRevOncog.v28.i2.40

Ragini D Singh

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The role of artificial intelligence and texture analysis in interventional radiological treatments of liver masses: a narrative review 人工智能和肌理分析在肝肿块介入放射治疗中的作用:综述

Critical Reviews in Oncogenesis Pub Date : 2023-01-01 DOI: 10.1615/critrevoncog.2023049855

Sonia Triggiani, Maria Teresa Contaldo, Giulia Mastellone, Maurizio Cè, Anna Maria Ierardi, Gianpaolo Carrafiello, Michaela Cellina

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Dedication to Dr. Larry K. Keefer at the Sixth International Workshop on Nitric Oxide in Cancer and Beyond. 在第六届癌症及以后的一氧化氮国际研讨会上向LarryK.Keefer博士致敬。

Critical Reviews in Oncogenesis Pub Date : 2023-01-01 DOI: 10.1615/CritRevOncog.2023048490

Khosrow Kashfi

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Development of JS-K, a First-in-Class Arylated Diazeniumdiolate, for the Treatment of Cancer. JS-K，一种用于治疗癌症的第一类芳基化二氮鎓二醇盐的开发。

Critical Reviews in Oncogenesis Pub Date : 2023-01-01 DOI: 10.1615/CritRevOncog.2023048725

Paul J Shami

引用次数: 0

Adoption of AI in oncological imaging: ethical, regulatory, and medical-legal thorough 人工智能在肿瘤成像中的应用:伦理、监管和医学法律彻底

Critical Reviews in Oncogenesis Pub Date : 2023-01-01 DOI: 10.1615/critrevoncog.2023050584

Marco Alì, Arianna Fantesini, Marco Tullio Morcella, Simona Ibba, Gennaro D'Anna, Deborah Fazzini, Sergio Papa

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In vivo endomicroscopy enables machine learning-based prediction of responsiveness to neoadjuvant chemoradiotherapy by advanced rectal cancer patients 体内内窥镜使基于机器学习的预测对晚期直肠癌患者新辅助放化疗的反应成为可能

Critical Reviews in Oncogenesis Pub Date : 2023-01-01 DOI: 10.1615/critrevoncog.2023050075

Alan Sabino, Adriana Safatle-Ribeiro, Suzylaine Lima, Carlos Marques, Fauze Maluf-Filho, Alexandre Ramos

{"title":"In vivo endomicroscopy enables machine learning-based prediction of responsiveness to neoadjuvant chemoradiotherapy by advanced rectal cancer patients","authors":"Alan Sabino, Adriana Safatle-Ribeiro, Suzylaine Lima, Carlos Marques, Fauze Maluf-Filho, Alexandre Ramos","doi":"10.1615/critrevoncog.2023050075","DOIUrl":"https://doi.org/10.1615/critrevoncog.2023050075","url":null,"abstract":"Probe-based confocal laser endomicroscopy (pCLE) enables in vivo cell-level observation in the colorectal mucosa (CM) during colonoscopy. Assessment of pCLE images is limited by endoscopists’ availability, training, and prevalence of qualitative criteria. Artificial intelligence tools may improve the accuracy of analysis of pCLE movies of the CM contributing for enhanced prognostics. Motiro is an automated unified framework for statistics-based digital pathology of pCLE movies of the CM. Motiro performs a batch mode analysis of pCLE movies for automatic characterization of a tumoral region and its surroundings which enables classifying a patient as responsive to neoadjuvant chemoradiotherapy (neoCRT) or not based on pre-neoCRT pCLE movies. The processing flow is as follows: Motiro builds histograms of fluorescence of all frames; computes the fractal dimension of the contours appearing in frames of videos reporting the tumoral region and its surrounding mucosa; the generated features are feed in Machine Learning (ML) algorithms which aim to predict response to neoCRT. We analyze movies of 47 patients having locally advanced rectal cancer. Accuracy on classification of patients responding or not to neoCRT, based on analysis of images of the tumoral regions or their surrounding areas respectively reach ~0.62 or ~ 0.70. Feature analysis shows that the main contributors for the classification are the fluorescence intensities. We employed a ML framework for predicting whether an advanced rectal cancer patient will respond or not to neoCRT. We demonstrate that the analysis of the mucosa surrounding the tumor region enables better predictive power.","PeriodicalId":35617,"journal":{"name":"Critical Reviews in Oncogenesis","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135611720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

My Friend Larry Keefer. 我的朋友拉里·基弗。

Critical Reviews in Oncogenesis Pub Date : 2023-01-01 DOI: 10.1615/CritRevOncog.2023048727

Paul J Shami

引用次数: 0

Artificial intelligence in bone metastasis imaging: recent progresses from diagnosis to treatment - a narrative review 人工智能在骨转移成像中的应用:从诊断到治疗的最新进展

Critical Reviews in Oncogenesis Pub Date : 2023-01-01 DOI: 10.1615/critrevoncog.2023050470

Elena Caloro, Giulia Gnocchi, Cettina Quarrella, Maurizio Ce, Gianpaolo Carrafiello, Michaela Cellina

{"title":"Artificial intelligence in bone metastasis imaging: recent progresses from diagnosis to treatment - a narrative review","authors":"Elena Caloro, Giulia Gnocchi, Cettina Quarrella, Maurizio Ce, Gianpaolo Carrafiello, Michaela Cellina","doi":"10.1615/critrevoncog.2023050470","DOIUrl":"https://doi.org/10.1615/critrevoncog.2023050470","url":null,"abstract":"The introduction of artificial intelligence (AI) represents an actual revolution in the radiological field, including bone lesion imaging. Bone lesions are often detected both in healthy and oncological patients and the differential diagnosis can be challenging but decisive, because it affects the diagnostic and therapeutic process, especially in case of metastases. Several studies have already demonstrated how the integration of AI-based tools in the current clinical workflow could bring benefits to patients and to healthcare workers. AI technologies could help radiologists in early bone metastases detection, increasing the diagnostic accuracy and reducing the overdiagnosis and the number of unnecessary deeper investigations. In addition, radiomics and radiogenomics approaches could go beyond the qualitative features, visible to the human eyes, extrapolating cancer genomic and behaviour information from imaging, in order to plan a targeted and personalized treatment. In this article, we want to provide a comprehensive summary of the most promising AI applications in bone metastasis imaging and their role from diagnosis to treatment and prognosis, including the analysis of future challenges and new perspectives.","PeriodicalId":35617,"journal":{"name":"Critical Reviews in Oncogenesis","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135057961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring the Potential of Artificial Intelligence in Breast Ultrasound 探讨人工智能在乳腺超声中的应用潜力

Critical Reviews in Oncogenesis Pub Date : 2023-01-01 DOI: 10.1615/critrevoncog.2023048873

Giovanni Irmici, Maurizio Ce', Gianmarco Della Pepa, Elisa D'Ascoli, Claudia De Berardinis, Emilia Giambersio, Lidia Rabiolo, Ludovica La Rocca, Serena Carriero, Catherine Depretto, Gianfranco Scaperrotta, Michaela Cellina

{"title":"Exploring the Potential of Artificial Intelligence in Breast Ultrasound","authors":"Giovanni Irmici, Maurizio Ce', Gianmarco Della Pepa, Elisa D'Ascoli, Claudia De Berardinis, Emilia Giambersio, Lidia Rabiolo, Ludovica La Rocca, Serena Carriero, Catherine Depretto, Gianfranco Scaperrotta, Michaela Cellina","doi":"10.1615/critrevoncog.2023048873","DOIUrl":"https://doi.org/10.1615/critrevoncog.2023048873","url":null,"abstract":"Breast ultrasound has emerged as a valuable imaging modality in the detection and characterization of breast lesions, particularly in women with dense breast tissue or contraindications for mammography. Within this framework, artificial intelligence (AI) has garnered significant attention for its potential to improve diagnostic accuracy in breast ultrasound and revolutionize the workflow. This review article aims to comprehensively explore the current state of research and development in harnessing AI's capabilities for breast ultrasound. We delve into various AI techniques, including machine learning, deep learning, as well as their applications in automating lesion detection, segmentation, and classification tasks. Furthermore, the review addresses the challenges and hurdles faced in implementing AI systems in breast ultrasound diagnostics, such as data privacy, interpretability, and regulatory approval. Ethical considerations pertaining to the integration of AI into clinical practice are also discussed, emphasizing the importance of maintaining a patient-centered approach. The integration of AI into breast ultrasound holds great promise for improving diagnostic accuracy, enhancing efficiency, and ultimately advancing patient’s care. By examining the current state of research and identifying future opportunities, this review aims to contribute to the understanding and utilization of AI in breast ultrasound and encourage further interdisciplinary collaboration to maximize its potential in clinical practice.","PeriodicalId":35617,"journal":{"name":"Critical Reviews in Oncogenesis","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135501064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0