Advancements in Oncology with Artificial Intelligence—A Review Article

Abstract

Well-trained machine learning (ML) and artificial intelligence (AI) systems can provide clinicians with therapeutic assistance, potentially increasing efficiency and improving efficacy. ML has demonstrated high accuracy in oncology-related diagnostic imaging, including screening mammography interpretation, colon polyp detection, glioma classification, and grading. By utilizing ML techniques, the manual steps of detecting and segmenting lesions are greatly reduced. ML-based tumor imaging analysis is independent of the experience level of evaluating physicians, and the results are expected to be more standardized and accurate. One of the biggest challenges is its generalizability worldwide. The current detection and screening methods for colon polyps and breast cancer have a vast amount of data, so they are ideal areas for studying the global standardization of artificial intelligence. Central nervous system cancers are rare and have poor prognoses based on current management standards. ML offers the prospect of unraveling undiscovered features from routinely acquired neuroimaging for improving treatment planning, prognostication, monitoring, and response assessment of CNS tumors such as gliomas. By studying AI in such rare cancer types, standard management methods may be improved by augmenting personalized/precision medicine. This review aims to provide clinicians and medical researchers with a basic understanding of how ML works and its role in oncology, especially in breast cancer, colorectal cancer, and primary and metastatic brain cancer. Understanding AI basics, current achievements, and future challenges are crucial in advancing the use of AI in oncology.

Connected-SegNets: A Deep Learning Model for Breast Tumor Segmentation from X-ray Images

“…Automated techniques are being developed to analyze and diagnose breast mammograms with the goal of counteracting this variability and standardizing diagnostic procedures [ 14 , 15 ]. The rapid emergence of artificial intelligence (AI) and deep learning (DL) has significant implications for breast cancer diagnosis [ 16 , 17 , 18 ]. The advancements in image segmentation using convolutional neural networks (CNNs) have been applied to segment breast cancer from X-ray images [ 19 , 20 , 21 , 22 , 23 ].…”

Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

“…From this figure, it can be seen that the first step uses the different technologies available to acquire the internal tissue dynamics of the breast, so they can be expressed in an image; the second step is used to execute algorithms that perform basic tasks on the images (for instance, correcting the color scale), so the segmentation, which is the detection of Region-of-interest (ROI), can be done; then, the third step quantifies the differences between images that have abnormalities from the ones that do not have; finally, once the differences are quantified, it is necessary to classify them to provide a diagnosis. With the rapid development of novel technologies that can capture more accurately the dynamics of the breast tissues, numerous advances have been done in all the aforementioned fields; in this sense, the goal of detecting all the abnormalities without generating false alarms is still a highly desirable feature for all the proposals [ 5 , 6 ]. Recently, some articles have reviewed some proposals regarding the feature classification and its interpretation [ 6 , 7 , 8 , 9 ]; yet, an article that presents the main technologies used to form the breast image as well as the processing stages required to provide a diagnosis is still missing.…”

“…With the rapid development of novel technologies that can capture more accurately the dynamics of the breast tissues, numerous advances have been done in all the aforementioned fields; in this sense, the goal of detecting all the abnormalities without generating false alarms is still a highly desirable feature for all the proposals [ 5 , 6 ]. Recently, some articles have reviewed some proposals regarding the feature classification and its interpretation [ 6 , 7 , 8 , 9 ]; yet, an article that presents the main technologies used to form the breast image as well as the processing stages required to provide a diagnosis is still missing. This article presents a state-of-the-art review of both the technologies used to create the breast image as well as the strategies employed to perform the image processing and classification.…”

Integration of artificial intelligence and precision oncology in Latin America

“…AI algorithms were created to be used primarily with a particular system; if the algorithm is used with other systems it would probably show low performance. This represents a striking barrier because these data storage systems are not generally available in all healthcare facilities ( 52 ).…”

“…For instance, in AI-mediated segmentation of a specific structure in a whole slide image (WSI), the completion of this task highly depends on the veridical reference annotations (data) by expert pathologists, therefore the need for well-structured and homogeneous organized data. Nevertheless, it is known that the current absence of this standardization concerning staining reagents, protocols, and section thicknesses (of radiologic images) is reported ( 52 – 30 ). To address these issues there have been some initiatives such as the minimum Common Oncology Data Elements (mCODE), which tries to identify and standardize essential cancer data in electronic health records (EHR).…”