Detecting B-cell lymphoma-6 overexpression status in primary central nervous system lymphoma using multiparametric MRI-based machine learning DOI Creative Commons

Mingxiao Wang,

Guoli Liu, Nan Zhang

et al.

Neuroradiology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 24, 2025

In primary central nervous system lymphoma (PCNSL), B-cell lymphoma-6 (BCL-6) is an unfavorable prognostic biomarker. We aim to non-invasively detect BCL-6 overexpression in PCNSL patients using multiparametric MRI and machine learning techniques. 65 (101 lesions) with (PCNSL) diagnosed from January 2013 July 2023, all were randomly divided into a training set validation according ratio of 8 2. ADC map derived DWI (b = 0/1000 s/mm2), fast spin echo T2WI, T2FLAIR, collected at 3.0 T. A total 2234 radiomics features the tumor segmentation area extracted LASSO used select features. Logistic regression (LR), Naive bayes (NB), Support vector (SVM), K-nearest Neighbor, (KNN) Multilayer Perceptron (MLP), for learning, sensitivity, specificity, accuracy F1-score, under curve (AUC) was evaluate detection performance five classifiers, 6 groups combinations different sequences fitted 5 optimal classifier obtained. status could be identified varying degrees 30 models based on radiomics, model improved by combining classifiers. (SVM) combined three sequence group had largest AUC (0.95) satisfactory (0.87) set. Multiparametric promising detecting overexpression.

Language: Английский

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment DOI Open Access
Narendra N. Khanna, Mahesh Maindarkar, Vijay Viswanathan

et al.

Healthcare, Journal Year: 2022, Volume and Issue: 10(12), P. 2493 - 2493

Published: Dec. 9, 2022

: The price of medical treatment continues to rise due (i) an increasing population; (ii) aging human growth; (iii) disease prevalence; (iv) a in the frequency patients that utilize health care services; and (v) increase price.

Language: Английский

Citations

174
Primary brain tumours in adults DOI
Martin J. van den Bent, Marjolein Geurts, Pim J. French

et al.

The Lancet, Journal Year: 2023, Volume and Issue: 402(10412), P. 1564 - 1579

Published: Sept. 19, 2023

Language: Английский

Citations

155
Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine DOI Open Access
Sanjay Saxena, Biswajit Jena, Neha Gupta

et al.

Cancers, Journal Year: 2022, Volume and Issue: 14(12), P. 2860 - 2860

Published: June 9, 2022

Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates prediction model through various AI methods to stratify risk patients, monitor therapeutic approaches, assess clinical outcomes. shown tremendous achievements prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, progression-free for human cancer study. Although immense performance care aspects, it several challenges limitations. The proposed review provides an overview radiogenomics viewpoints on role terms its promises computational well oncological aspects offers opportunities era medicine. also presents recommendations diminish these obstacles.

Language: Английский

Citations

87
Artificial Intelligence in Brain Tumor Imaging: A Step toward Personalized Medicine DOI Creative Commons
Maurizio Cè, Giovanni Irmici,

Chiara Foschini

et al.

Current Oncology, Journal Year: 2023, Volume and Issue: 30(3), P. 2673 - 2701

Published: Feb. 22, 2023

The application of artificial intelligence (AI) is accelerating the paradigm shift towards patient-tailored brain tumor management, achieving optimal onco-functional balance for each individual. AI-based models can positively impact different stages diagnostic and therapeutic process. Although histological investigation will remain difficult to replace, in near future radiomic approach allow a complementary, repeatable non-invasive characterization lesion, assisting oncologists neurosurgeons selecting best option correct molecular target chemotherapy. AI-driven tools are already playing an important role surgical planning, delimiting extent lesion (segmentation) its relationships with structures, thus allowing precision surgery as radical reasonably acceptable preserve quality life. Finally, AI-assisted prediction complications, recurrences response, suggesting most appropriate follow-up. Looking future, AI-powered promise integrate biochemical clinical data stratify risk direct patients personalized screening protocols.

Language: Английский

Citations

52
Artificial Intelligence in CT and MR Imaging for Oncological Applications DOI Open Access
Ramesh Paudyal, Akash Shah, Oğuz Akın

et al.

Cancers, Journal Year: 2023, Volume and Issue: 15(9), P. 2573 - 2573

Published: April 30, 2023

Cancer care increasingly relies on imaging for patient management. The two most common cross-sectional modalities in oncology are computed tomography (CT) and magnetic resonance (MRI), which provide high-resolution anatomic physiological imaging. Herewith is a summary of recent applications rapidly advancing artificial intelligence (AI) CT MRI oncological that addresses the benefits challenges resultant opportunities with examples. Major remain, such as how best to integrate AI developments into clinical radiology practice, vigorous assessment quantitative MR data accuracy, reliability utility research integrity oncology. Such necessitate an evaluation robustness biomarkers be included developments, culture sharing, cooperation knowledgeable academics vendor scientists companies operating fields. Herein, we will illustrate few solutions these efforts using novel methods synthesizing different contrast modality images, auto-segmentation, image reconstruction examples from lung well abdome, pelvis, head neck MRI. community must embrace need metrics beyond lesion size measurement. extraction longitudinal tracking registered lesions understanding tumor environment invaluable interpreting disease status treatment efficacy. This exciting time work together move field forward narrow AI-specific tasks. New datasets used improve personalized management cancer patients.

Language: Английский

Citations

52
Advancements in Oncology with Artificial Intelligence—A Review Article DOI Open Access
Nikitha Vobugari,

Vikranth Raja,

Udhav Sethi

et al.

Cancers, Journal Year: 2022, Volume and Issue: 14(5), P. 1349 - 1349

Published: March 6, 2022

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

Language: Английский

Citations

58
Artificial Intelligence and Precision Medicine: A New Frontier for the Treatment of Brain Tumors DOI Creative Commons
Armelle Philip,

Betty Samuel,

Saurabh Bhatia

et al.

Life, Journal Year: 2022, Volume and Issue: 13(1), P. 24 - 24

Published: Dec. 22, 2022

Brain tumors are a widespread and serious neurological phenomenon that can be life- threatening. The computing field has allowed for the development of artificial intelligence (AI), which mimic neural network human brain. One use this technology been to help researchers capture hidden, high-dimensional images brain tumors. These provide new insights into nature improve treatment options. AI precision medicine (PM) converging revolutionize healthcare. potential cancer imaging interpretation in several ways, including more accurate tumor genotyping, precise delineation volume, better prediction clinical outcomes. AI-assisted surgery an effective safe option treating This review discusses various PM techniques used treatment. tumors, i.e., genomic profiling, microRNA panels, quantitative imaging, radiomics, hold great promise future. However, there challenges must overcome these technologies reach their full

Language: Английский

Citations

39
Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology DOI Creative Commons
Jun Shao, Jiechao Ma, Qin Zhang

et al.

Seminars in Cancer Biology, Journal Year: 2023, Volume and Issue: 91, P. 1 - 15

Published: Feb. 20, 2023

Personalized treatment strategies for cancer frequently rely on the detection of genetic alterations which are determined by molecular biology assays. Historically, these processes typically required single-gene sequencing, next-generation or visual inspection histopathology slides experienced pathologists in a clinical context. In past decade, advances artificial intelligence (AI) technologies have demonstrated remarkable potential assisting physicians with accurate diagnosis oncology image-recognition tasks. Meanwhile, AI techniques make it possible to integrate multimodal data such as radiology, histology, and genomics, providing critical guidance stratification patients context precision therapy. Given that mutation is unaffordable time-consuming considerable number patients, predicting gene mutations based routine radiological scans whole-slide images tissue AI-based methods has become hot issue actual practice. this review, we synthesized general framework integration (MMI) intelligent diagnostics beyond standard techniques. Then summarized emerging applications prediction mutational profiles common cancers (lung, brain, breast, other tumor types) pertaining radiology histology imaging. Furthermore, concluded there truly exist multiple challenges way its real-world application medical field, including curation, feature fusion, model interpretability, practice regulations. Despite challenges, still prospect implementation highly decision-support tool aid oncologists future management.

Language: Английский

Citations

38
Recapitulating the Key Advances in the Diagnosis and Prognosis of High-Grade Gliomas: Second Half of 2021 Update DOI Open Access
Guido Fròsina

International Journal of Molecular Sciences, Journal Year: 2023, Volume and Issue: 24(7), P. 6375 - 6375

Published: March 28, 2023

High-grade gliomas (World Health Organization grades III and IV) are the most frequent fatal brain tumors, with median overall survivals of 24–72 14–16 months, respectively. We reviewed progress in diagnosis prognosis high-grade published second half 2021. A literature search was performed PubMed using general terms “radio* gliom*” a time limit from 1 July 2021 to 31 December Important advances were provided both imaging non-imaging diagnoses these hard-to-treat cancers. Our prognostic capacity also increased during This review article demonstrates slow, but steady improvements, scientifically technically, which express an chance that patients may be correctly diagnosed without invasive procedures. The those strictly depends on final results complex diagnostic process, widely varying survival rates.

Language: Английский

Citations

24
Artificial Intelligence and Pediatrics: Synthetic Knowledge Synthesis DOI Open Access
Jernej Završnik, Peter Kokol, Bojan Žlahtič

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(3), P. 512 - 512

Published: Jan. 26, 2024

The first publication on the use of artificial intelligence (AI) in pediatrics dates back to 1984. Since then, research AI has become much more popular, and number publications largely increased. Consequently, a need for holistic landscape enabling researchers other interested parties gain insights into arisen. To fill this gap, novel methodology, synthetic knowledge synthesis (SKS), was applied. Using SKS, we identified most prolific countries, institutions, source titles, funding agencies, themes frequently used algorithms their applications pediatrics. corpus extracted from Scopus (Elsevier, Netherlands) bibliographic database analyzed using VOSViewer, version 1.6.20. Done An exponential growth literature observed last decade. United States, China, Canada were productive countries. Deep learning machine algorithm classification, natural language processing popular approach. Pneumonia, epilepsy, asthma targeted pediatric diagnoses, prediction clinical decision making frequent applications.

Language: Английский

Citations

10