"The biomarker field is blessed with a plethora of imaging and molecular-based data, and at the same time, plagued with so much data that no one individual can comprehend it all," explained Guest Editor Karin Rodland, PhD, Pacific Northwest National Laboratory, Richland; and Oregon Health and Science University, Portland, OR, USA. "AI offers a solution to that problem, and it has the potential to uncover novel interactions that more accurately reflect the biology of cancer and other diseases." Promising applications of AI, DL, and ML presented in this issue include identifying early-stage cancers, inferring the site of the specific cancer, aiding in the assignment of appropriate therapeutic options for each patient, characterizing the tumor microenvironment, and predicting the response to immunotherapy. A comprehensive overview of the literature regarding the use of AI approaches to identify biomarkers for ovarian and pancreatic cancer illustrates underlying principles and looks at the gaps and challenges that face the field as a whole. Ovarian and pancreatic cancers are rare, but lethal because they lack early symptoms and detection.

Amsterdam, March 1, 2022 – Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have transformed many industries and areas of science. Now, these tools are being applied to address the challenges of cancer biomarker discovery, where the analysis of vast amounts of imaging and molecular data is beyond the ability of traditional statistical analyses and tools. In a special issue of Cancer Biomarkers, researchers propose various approaches and explore some of the unique challenges of using AI, DL, and ML to improve the accuracy and predictive power of biomarkers for cancer and other diseases. "The biomarker field is blessed with a plethora of imaging and molecular-based data, and at the same time, plagued with so much data that no one individual can comprehend it all," explained Guest Editor Karin Rodland, PhD, Pacific Northwest National Laboratory, Richland; and Oregon Health and Science University, Portland, OR, USA. "AI offers a solution to that problem, and it has the potential to uncover novel interactions that more accurately reflect the biology of cancer and other diseases."

Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have transformed many industries and areas of science. Now, these tools are being applied to address the challenges of cancer biomarker discovery, where the analysis of vast amounts of imaging and molecular data is beyond the ability of traditional statistical analyses and tools. In a special issue of Cancer Biomarkers, researchers propose various approaches and explore some of the unique challenges of using AI, DL, and ML to improve the accuracy and predictive power of biomarkers for cancer and other diseases. "The biomarker field is blessed with a plethora of imaging and molecular-based data, and at the same time, plagued with so much data that no one individual can comprehend it all," explained Guest Editor Karin Rodland, PhD, Pacific Northwest National Laboratory, Richland; and Oregon Health and Science University, Portland, OR, USA. "AI offers a solution to that problem, and it has the potential to uncover novel interactions that more accurately reflect the biology of cancer and other diseases."

Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have transformed many industries and areas of science. Now, these tools are being applied to address the challenges of cancer biomarker discovery, where the analysis of vast amounts of imaging and molecular data is beyond the ability of traditional statistical analyses and tools. In a special issue of Cancer Biomarkers, researchers propose various approaches and explore some of the unique challenges of using AI, DL, and ML to improve the accuracy and predictive power of biomarkers for cancer and other diseases. "The biomarker field is blessed with a plethora of imaging and molecular-based data, and at the same time, plagued with so much data that no one individual can comprehend it all," explained Guest Editor Karin Rodland, Ph.D., Pacific Northwest National Laboratory, Richland; and Oregon Health and Science University, Portland, OR, U.S.. "AI offers a solution to that problem, and it has the potential to uncover novel interactions that more accurately reflect the biology of cancer and other diseases." Promising applications of AI, DL, and ML presented in this issue include identifying early-stage cancers, inferring the site of the specific cancer, aiding in the assignment of appropriate therapeutic options for each patient, characterizing the tumor microenvironment, and predicting the response to immunotherapy.

Despite major advances in treatment and diagnosis over the past decades, cancer still ranks as a leading cause of mortality and a major impediment to extending life expectancy worldwide. Artificial intelligence's future involvement in healthcare, particularly in the detection and treatment of cancer, is anticipated to take a variety of forms, ranging from identifying a specific form of cancer to evaluating which therapy method may best treat that particular instance. AI promises to increase customization of cancer care and help individuals live with the illness with a greater quality of life and fewer side effects. In order to discover cancer in its most treatable stage, screenings are meant to monitor patients who do not show any symptoms proactively. U.S. Preventative Services Task Force recommends screening for breast, cervical, colorectal, and lung cancer.

The combination of expert knowledge and multidisciplinary approaches highlighted in the book make it a valuable source of information for physicians and clinical researchers active in the field of cancer diagnosis and treatment (oncologists, oncologic surgeons, radiation oncologists, nuclear medicine physicians, and radiologists) and computer science scholars seeking to understand medical applications of artificial intelligence. Each chapter presents information on related sub-topics in a reader-friendly format. The combination of expert knowledge and multidisciplinary approaches highlighted in the book make it a valuable source of information for physicians and clinical researchers active in the field of cancer diagnosis and treatment (oncologists, oncologic surgeons, radiation oncologists, nuclear medicine physicians, and radiologists) and computer science scholars seeking to understand medical applications of artificial intelligence. Shigao Huang obtained his PhD from the University of Macau and focused on precision medicine, such as immunotherapy for cancer, nano-medicine, and radiotherapy for cancer. He has published over 30 SCI papers and has worked as a peer reviewer in many reputable journals.

The "Innovations in Cancer Diagnosis and Treatment, Micro-LEDs, Renewable Energy Generation and Storage, and Fault Detection" report has been added to ResearchAndMarkets.com's offering. The edition also provides insights on the role of macropinocytosis in pancreatic cancer. The TOE covers use of ceramic electrodes for doubling energy density and a biosensor for earlier diagnosis of tumors.