Pub Date : 2023-01-01DOI: 10.1615/CritRevOncog.2023047772
Sagar Dholariya, Ragini D Singh, Amit Sonagra, Dharamveer Yadav, Bhairavi N Vajaria, Deepak Parchwani
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.
{"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}
Pub Date : 2023-01-01DOI: 10.1615/CritRevOncog.v28.i2.40
Ragini D Singh
{"title":"Preface: Oral Cancer: New Insights in Diagnosis, Prognosis, and Therapeutics to Management and Reconstruction.","authors":"Ragini D Singh","doi":"10.1615/CritRevOncog.v28.i2.40","DOIUrl":"10.1615/CritRevOncog.v28.i2.40","url":null,"abstract":"","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":"41214817","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}
Pub Date : 2023-01-01DOI: 10.1615/critrevoncog.2023049855
Sonia Triggiani, Maria Teresa Contaldo, Giulia Mastellone, Maurizio Cè, Anna Maria Ierardi, Gianpaolo Carrafiello, Michaela Cellina
{"title":"The role of artificial intelligence and texture analysis in interventional radiological treatments of liver masses: a narrative review","authors":"Sonia Triggiani, Maria Teresa Contaldo, Giulia Mastellone, Maurizio Cè, Anna Maria Ierardi, Gianpaolo Carrafiello, Michaela Cellina","doi":"10.1615/critrevoncog.2023049855","DOIUrl":"https://doi.org/10.1615/critrevoncog.2023049855","url":null,"abstract":"","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":"134980626","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}
Pub Date : 2023-01-01DOI: 10.1615/CritRevOncog.2023048490
Khosrow Kashfi
{"title":"Dedication to Dr. Larry K. Keefer at the Sixth International Workshop on Nitric Oxide in Cancer and Beyond.","authors":"Khosrow Kashfi","doi":"10.1615/CritRevOncog.2023048490","DOIUrl":"10.1615/CritRevOncog.2023048490","url":null,"abstract":"","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":"41214818","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}
Pub Date : 2023-01-01DOI: 10.1615/CritRevOncog.2023048725
Paul J Shami
{"title":"Development of JS-K, a First-in-Class Arylated Diazeniumdiolate, for the Treatment of Cancer.","authors":"Paul J Shami","doi":"10.1615/CritRevOncog.2023048725","DOIUrl":"10.1615/CritRevOncog.2023048725","url":null,"abstract":"","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":"41214819","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}
Pub Date : 2023-01-01DOI: 10.1615/critrevoncog.2023050584
Marco Alì, Arianna Fantesini, Marco Tullio Morcella, Simona Ibba, Gennaro D'Anna, Deborah Fazzini, Sergio Papa
Artificial Intelligence (AI) algorithms have shown great promise in oncological imaging, outperforming or matching radiologists in retrospective studies, signifying their potential for advanced screening capabilities. These AI tools offer valuable support to radiologists, assisting them in critical tasks such as prioritizing reporting, early cancer detection, and precise measurements, thereby bolstering clinical decision-making. With the healthcare landscape witnessing a surge in imaging requests and a decline in available radiologists, the integration of AI has become increasingly appealing. By streamlining workflow efficiency and enhancing patient care, AI presents a transformative solution to the challenges faced by oncological imaging practices. Nevertheless, successful AI integration necessitates navigating various ethical, regulatory, and medical-legal challenges. This review endeavors to provide a comprehensive overview of these obstacles, aiming to foster a responsible and effective implementation of AI in oncological imaging.
{"title":"Adoption of AI in oncological imaging: ethical, regulatory, and medical-legal thorough","authors":"Marco Alì, Arianna Fantesini, Marco Tullio Morcella, Simona Ibba, Gennaro D'Anna, Deborah Fazzini, Sergio Papa","doi":"10.1615/critrevoncog.2023050584","DOIUrl":"https://doi.org/10.1615/critrevoncog.2023050584","url":null,"abstract":"Artificial Intelligence (AI) algorithms have shown great promise in oncological imaging, outperforming or matching radiologists in retrospective studies, signifying their potential for advanced screening capabilities. These AI tools offer valuable support to radiologists, assisting them in critical tasks such as prioritizing reporting, early cancer detection, and precise measurements, thereby bolstering clinical decision-making. With the healthcare landscape witnessing a surge in imaging requests and a decline in available radiologists, the integration of AI has become increasingly appealing. By streamlining workflow efficiency and enhancing patient care, AI presents a transformative solution to the challenges faced by oncological imaging practices. Nevertheless, successful AI integration necessitates navigating various ethical, regulatory, and medical-legal challenges. This review endeavors to provide a comprehensive overview of these obstacles, aiming to foster a responsible and effective implementation of AI in oncological imaging.","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":"135910823","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}
Pub Date : 2023-01-01DOI: 10.1615/critrevoncog.2023050075
Alan Sabino, Adriana Safatle-Ribeiro, Suzylaine Lima, Carlos Marques, Fauze Maluf-Filho, Alexandre Ramos
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.
{"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}
Pub Date : 2023-01-01DOI: 10.1615/CritRevOncog.2023048727
Paul J Shami
{"title":"My Friend Larry Keefer.","authors":"Paul J Shami","doi":"10.1615/CritRevOncog.2023048727","DOIUrl":"10.1615/CritRevOncog.2023048727","url":null,"abstract":"","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":"41214823","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}
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.
{"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}
Pub Date : 2023-01-01DOI: 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
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.
{"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}
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