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":"28 1","pages":"xi-xviii"},"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.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":"5 1","pages":"0"},"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":"136 1","pages":"0"},"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}
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":"24 1","pages":"0"},"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":"40 1","pages":"0"},"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}
Pub Date : 2023-01-01DOI: 10.1615/critrevoncog.2023049947
Rupali Das
Invariant natural killer T cells (iNKTs) are innate-type T lymphocytes that directly kill tumor cells or tumor-growth promoting immunosuppressive cells such as the tumor-associated macrophages. Additionally, iNKTs robustly transactivate the antitumor functions of T, B, natural killer, and dendritic cells as well as reinvigorate exhausted immune cells in the tumor microenvironment. As such, iNKTs make excellent candidates for inclusion in anti-cancer cellular therapies. However, to capitalize on the potential benefits of iNKT cell-based approaches, it is imperative that we develop new and clinically viable strategies to enhance their antitumor function. To that end, two novel monoclonal antibodies (mAbs) that selectively bind to the human (NKTT320) or murine (NKT14m) invariant T cell receptor have been recently developed and characterized. Studies using purified human iNKTs (in vitro) and a model of non-human primate (in vivo) reveal that NKTT320 promotes swift, vigorous and sustained iNKT cell activation that is accompanied by robust production of inflammatory mediators and bystander immune cell activation. Furthermore, NKTT320 augments expression of cytotoxic markers and human iNKT cell degranulation. Similarly, NKT14m prompts dramatic murine iNKT cell activation and functional response both in vitro and in vivo. However, antitumor efficacy of a single dose of NKT14m injection in tumor-bearing mice is limited and tumor-model dependent. In contrast, combination treatment of NKT14m with either low dose interleukin (IL)-12 or the chemotherapeutic agent, cyclophosphamide results in a superior antitumor response in vivo. This is evident by activation of both iNKTs and other immune cells, prolonged survival of the tumor-challenged mice and long-lasting immunity. Collectively, these recent studies justify further development of anti-iTCR mAbs that can be used alone or in conjunction with immunomodulatory agents to enhance iNKT cell antitumor immunity against various cancers.
{"title":"T cell receptor-engaging monoclonal antibodies mobilize the anti-tumor functions of invariant natural killer T cells","authors":"Rupali Das","doi":"10.1615/critrevoncog.2023049947","DOIUrl":"https://doi.org/10.1615/critrevoncog.2023049947","url":null,"abstract":"Invariant natural killer T cells (iNKTs) are innate-type T lymphocytes that directly kill tumor cells or tumor-growth promoting immunosuppressive cells such as the tumor-associated macrophages. Additionally, iNKTs robustly transactivate the antitumor functions of T, B, natural killer, and dendritic cells as well as reinvigorate exhausted immune cells in the tumor microenvironment. As such, iNKTs make excellent candidates for inclusion in anti-cancer cellular therapies. However, to capitalize on the potential benefits of iNKT cell-based approaches, it is imperative that we develop new and clinically viable strategies to enhance their antitumor function. To that end, two novel monoclonal antibodies (mAbs) that selectively bind to the human (NKTT320) or murine (NKT14m) invariant T cell receptor have been recently developed and characterized. Studies using purified human iNKTs (in vitro) and a model of non-human primate (in vivo) reveal that NKTT320 promotes swift, vigorous and sustained iNKT cell activation that is accompanied by robust production of inflammatory mediators and bystander immune cell activation. Furthermore, NKTT320 augments expression of cytotoxic markers and human iNKT cell degranulation. Similarly, NKT14m prompts dramatic murine iNKT cell activation and functional response both in vitro and in vivo. However, antitumor efficacy of a single dose of NKT14m injection in tumor-bearing mice is limited and tumor-model dependent. In contrast, combination treatment of NKT14m with either low dose interleukin (IL)-12 or the chemotherapeutic agent, cyclophosphamide results in a superior antitumor response in vivo. This is evident by activation of both iNKTs and other immune cells, prolonged survival of the tumor-challenged mice and long-lasting immunity. Collectively, these recent studies justify further development of anti-iTCR mAbs that can be used alone or in conjunction with immunomodulatory agents to enhance iNKT cell antitumor immunity against various cancers.","PeriodicalId":35617,"journal":{"name":"Critical Reviews in Oncogenesis","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135755304","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.2023048491
Khosrow Kashfi
The pioneering studies of Dr. Larry Keefer and colleagues with diazeniumdiolates or NONOates as a platform have unraveled the chemical biology of many nitric oxides and have led to the design of a variety of promising therapeutic agents in oncology, gastroenterology, antimicrobials, wound healing, and the like. This dedication to Dr. Larry Keefer briefly highlights some of his studies using the diazeniumdiolate platform in the cancer arena.
Larry Keefer博士及其同事以二醇二氮鎓或NONOates为平台进行的开创性研究揭示了许多一氧化氮的化学生物学，并在肿瘤学、胃肠病学、抗菌剂、伤口愈合等领域设计出了各种有前景的治疗剂。这份对Larry Keefer博士的奉献简要强调了他在癌症领域使用二氮烯鎓二醇酯平台的一些研究。
{"title":"Fifty Years of Diazeniumdiolate Research: A Tribute to Dr. Larry K. Keefer.","authors":"Khosrow Kashfi","doi":"10.1615/CritRevOncog.2023048491","DOIUrl":"10.1615/CritRevOncog.2023048491","url":null,"abstract":"<p><p>The pioneering studies of Dr. Larry Keefer and colleagues with diazeniumdiolates or NONOates as a platform have unraveled the chemical biology of many nitric oxides and have led to the design of a variety of promising therapeutic agents in oncology, gastroenterology, antimicrobials, wound healing, and the like. This dedication to Dr. Larry Keefer briefly highlights some of his studies using the diazeniumdiolate platform in the cancer arena.</p>","PeriodicalId":35617,"journal":{"name":"Critical Reviews in Oncogenesis","volume":"28 1","pages":"47-55"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11076142/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41214820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1615/CritRevOncog.2023047302
Katrina M Miranda, Lisa A Ridnour, Robert Y S Cheng, David A Wink, Douglas D Thomas
Nitric oxide (NO) and the enzyme that synthesizes it, nitric oxide synthase 2 (NOS2), have emerged as key players in inflammation and cancer. Expression of NOS2 in tumors has been correlated both with positive outcomes and with poor prognoses. The chemistry of NO is the major determinate to the biological outcome and the concentration of NO, which can range over five orders of magnitude, is critical in determining which pathways are activated. It is the activation of specific oncogenic and immunological mechanisms that shape the outcome. The kinetics of specific reactions determine the mechanisms of action. In this review, the relevant reactions of NO and related species are discussed with respect to these oncogenic and immunological signals.
{"title":"The Chemical Biology of NO that Regulates Oncogenic Signaling and Metabolism: NOS2 and Its Role in Inflammatory Disease.","authors":"Katrina M Miranda, Lisa A Ridnour, Robert Y S Cheng, David A Wink, Douglas D Thomas","doi":"10.1615/CritRevOncog.2023047302","DOIUrl":"10.1615/CritRevOncog.2023047302","url":null,"abstract":"<p><p>Nitric oxide (NO) and the enzyme that synthesizes it, nitric oxide synthase 2 (NOS2), have emerged as key players in inflammation and cancer. Expression of NOS2 in tumors has been correlated both with positive outcomes and with poor prognoses. The chemistry of NO is the major determinate to the biological outcome and the concentration of NO, which can range over five orders of magnitude, is critical in determining which pathways are activated. It is the activation of specific oncogenic and immunological mechanisms that shape the outcome. The kinetics of specific reactions determine the mechanisms of action. In this review, the relevant reactions of NO and related species are discussed with respect to these oncogenic and immunological signals.</p>","PeriodicalId":35617,"journal":{"name":"Critical Reviews in Oncogenesis","volume":"28 1","pages":"27-45"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11318306/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41214825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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