Artificial intelligence in cancer imaging: Clinical challenges and applications

CA A Cancer Journal for Clinicians, Journal Year: 2019, Volume and Issue: 69(2), P. 127 - 157

Published: Feb. 5, 2019

Abstract Judgement, as one of the core tenets medicine, relies upon integration multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms evolution disease but also need to take into account individual condition patients, their ability receive treatment, and responses treatment. Challenges remain in accurate detection, characterization, monitoring cancers despite improved technologies. Radiographic assessment most commonly visual evaluations, interpretations which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises make great strides qualitative interpretation cancer imaging expert clinicians, including volumetric delineation tumors over time, extrapolation tumor genotype biological course from radiographic phenotype, prediction clinical outcome, impact treatment on adjacent organs. AI automate processes initial images shift workflow management whether or administer an intervention, subsequent observation yet envisioned paradigm. Here, authors review current state applied describe advances 4 types (lung, brain, breast, prostate) illustrate how common problems are being addressed. Although studies evaluating applications oncology date have been vigorously validated reproducibility generalizability, results do highlight increasingly concerted efforts pushing technology use future directions care.

Language: Английский

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Radiomics: from qualitative to quantitative imaging

British Journal of Radiology, Journal Year: 2020, Volume and Issue: 93(1108)

Published: Feb. 26, 2020

Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and turn it into valuable predictive outcomes. As result of advances both computational hardware machine learning algorithms, computers are making great strides obtaining quantitative information from correlating with Radiomics, its two forms “handcrafted deep,” emerging field that translates images data yield biological enable radiologic phenotypic profiling for diagnosis, theragnosis, decision support, monitoring. Handcrafted radiomics multistage process which features based on shape, pixel intensities, texture extracted radiographs. Within this review, we describe the steps: starting data, how extracted, correlate clinical outcomes, resulting models used make predictions, such as survival, detection classification diagnostics. The application deep learning, second arm radiomics, place workflow discussed, along advantages disadvantages. To better illustrate technologies being used, provide real-world applications oncology, showcasing research well covering limitations future direction.

Language: Английский

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Radiogenomics: bridging imaging and genomics

Abdominal Radiology, Journal Year: 2019, Volume and Issue: 44(6), P. 1960 - 1984

Published: May 2, 2019

From diagnostics to prognosis response prediction, new applications for radiomics are rapidly being developed. One of the fastest evolving branches involves linking imaging phenotypes tumor genetic profile, a field commonly referred as "radiogenomics." In this review, general outline radiogenomic literature concerning prominent mutations across different sites will be provided. The radiogenomics originates from image processing techniques developed decades ago; however, many technical and clinical challenges still need addressed. Nevertheless, increasingly accurate robust models presented future appears bright.

Language: Английский

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Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics

European Journal of Nuclear Medicine and Molecular Imaging, Journal Year: 2019, Volume and Issue: 46(13), P. 2656 - 2672

Published: June 18, 2019

The aim of this systematic review was to analyse literature on artificial intelligence (AI) and radiomics, including all medical imaging modalities, for oncological non-oncological applications, in order assess how far the image mining research stands from routine application. To do this, we applied a trial phases classification inspired drug development process. Among articles considered inclusion PubMed were multimodality AI radiomics investigations, with validation analysis aimed at relevant clinical objectives. Quality assessment selected papers performed according QUADAS-2 criteria. We developed criteria studies. Overall 34,626 retrieved, 300 applying inclusion/exclusion criteria, 171 high-quality (QUADAS-2 ≥ 7) identified analysed. In 27/171 (16%), 141/171 (82%), 3/171 (2%) studies an AI-based algorithm, model, combined radiomics/AI approach, respectively, described. A total 26/27(96%) 1/27 (4%) classified as phase II III, respectively. Consequently, 13/141 (9%), 10/141 (7%), 111/141 (79%), 7/141 (5%) 0, I, II, All three categorised trials. results are promising but still not mature enough tools be implemented setting widely used. transfer learning well-known process, some specific adaptations discipline could represent most effective way algorithms become standard care tools.

Language: Английский

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Deep learning classification of lung cancer histology using CT images

Scientific Reports, Journal Year: 2021, Volume and Issue: 11(1)

Published: March 9, 2021

Tumor histology is an important predictor of therapeutic response and outcomes in lung cancer. Tissue sampling for pathologist review the most reliable method classification, however, recent advances deep learning medical image analysis allude to utility radiologic data further describing disease characteristics risk stratification. In this study, we propose a radiomics approach predicting non-small cell cancer (NSCLC) tumor from non-invasive standard-of-care computed tomography (CT) data. We trained validated convolutional neural networks (CNNs) on dataset comprising 311 early-stage NSCLC patients receiving surgical treatment at Massachusetts General Hospital (MGH), with focus two common histological types: adenocarcinoma (ADC) Squamous Cell Carcinoma (SCC). The CNNs were able predict AUC 0.71(p = 0.018). also found that using machine classifiers such as k-nearest neighbors (kNN) support vector (SVM) CNN-derived quantitative features yielded comparable discriminative performance, up 0.71 (p 0.017). Our best performing CNN functioned robust probabilistic classifier heterogeneous test sets, qualitatively interpretable visual explanations its predictions. Deep based can identify phenotypes It has potential augment existing approaches serve corrective aid diagnosticians.

Language: Английский

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Meningioma: not always a benign tumor. A review of advances in the treatment of meningiomas

CNS Oncology, Journal Year: 2021, Volume and Issue: 10(2)

Published: May 21, 2021

Meningiomas are the most common primary intracranial tumors. The majority of meningiomas benign, but they can present different grades dedifferentiation from grade I to III (anaplastic/malignant) that associated with outcomes. Radiological surveillance is a valid option for low-grade asymptomatic meningiomas. In other cases, treatment usually surgical, aimed at achieving complete resection. use adjuvant radiotherapy gold standard III, debated II and not generally indicated radically resected systemic treatments standardized. Here we report review literature on clinical, radiological molecular characteristics meningiomas, available strategies ongoing clinical trials.

Language: Английский

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Artificial intelligence in cancer diagnosis and therapy: Current status and future perspective

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 165, P. 107356 - 107356

Published: Aug. 14, 2023

Language: Английский

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Imaging and diagnostic advances for intracranial meningiomas

Neuro-Oncology, Journal Year: 2018, Volume and Issue: 21(Supplement_1), P. i44 - i61

Published: Oct. 18, 2018

The archetypal imaging characteristics of meningiomas are among the most stereotypic all central nervous system (CNS) tumors. In era plain film and ventriculography, was only performed if a mass suspected, their results were more suggestive than definitive. Following century technological development, we can now rely on to non-invasively diagnose meningioma with great confidence precisely delineate locations these tumors relative surrounding structures inform treatment planning. Asymptomatic may be identified growth monitored over time; moreover, routinely serves as an essential tool survey tumor burden at various stages during course treatment, thereby providing guidance effectiveness or need for further intervention. Modern radiological techniques expanding power from detection monitoring include extraction biologic information advanced analysis parameters. These contemporary approaches have led promising attempts predict grade and, in turn, contribute prognostic data. this supplement article, review important current future aspects diagnosis management meningioma, including conventional using CT, MRI, nuclear medicine.

Language: Английский

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A deep learning radiomics model for preoperative grading in meningioma

European Journal of Radiology, Journal Year: 2019, Volume and Issue: 116, P. 128 - 134

Published: May 1, 2019

Language: Английский

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A Deep Look Into the Future of Quantitative Imaging in Oncology: A Statement of Working Principles and Proposal for Change

International Journal of Radiation Oncology*Biology*Physics, Journal Year: 2018, Volume and Issue: 102(4), P. 1074 - 1082

Published: Aug. 28, 2018

Language: Английский

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