Functional magnetic resonance imaging in oncology: state of the art

Guimarães, Marcos Duarte; Schuch, Alice; Hochhegger, Bruno; Gross, Jefferson Luiz; Chojniak, Rubens; Marchiori, Edson

doi:10.1590/s0100-39842014000200013

Cited by 24 publications

(12 citation statements)

References 47 publications

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“…While radiography, ultrasonography, and CT are the main imaging techniques employed in cancer diagnosis, MRI is an emerging imaging diagnosis method that, while well established in the clinical practice, is in continuous development. Specifically, MRI can produce three-dimensional, multimodal images in a noninvasive way, without the use of ionizing radiation and with exceptional spatial and contrast resolution, allowing unprecedented accuracy in tumour investigation [6]. The novelty of MRI analysis is focused mainly on improving the anatomical resolution and on the advent of functional as well as molecular approaches [7].…”

Section: Diagnostic Imaging and Anatomic Pathology: An Alliance Fomentioning

confidence: 99%

Combining Diagnostic Imaging and Pathology for Improving Diagnosis and Prognosis of Cancer

Schillaci

Scimeca

Toschi

et al. 2019

Contrast Media & Molecular Imaging

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In the era of personalized medicine, the management of oncological patients requires a translational and multidisciplinary approach. During early phases of cancer development, biochemical alterations of cell metabolism occur much before the formation of detectable tumour masses. Current molecular imaging techniques, targeted to the study of molecular kinetics, employ molecular tracers capable of detecting cancer lesions with both high sensitivity and specificity while also providing essential information for both prognosis and therapy. On the contrary, complementary and crucial information is provided by histopathological examination and ancillary techniques such as immunohistochemistry. Thus, the successful collaboration between diagnostic imaging and anatomic pathology can represent a fundamental step in the “tortuous” but decisive path towards personalized medicine.

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Section: Diagnostic Imaging and Anatomic Pathology: An Alliance Fomentioning

confidence: 99%

Combining Diagnostic Imaging and Pathology for Improving Diagnosis and Prognosis of Cancer

Schillaci

Scimeca

Toschi

et al. 2019

Contrast Media & Molecular Imaging

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“…The ability to directly visualize biological response to radiotherapy during a treatment course would allow the opportunity to tailor dose delivery. Online daily ART to target areas of persistent areas of restricted diffusion, for example, could possibly improve outcomes although implementation of functional imaging on MR-linac poses a number of challenges ( 72 ). For instance, there is a decrease in signal intensity of healthy prostate tissue on T2-weighted imaging during the course of treatment, which reduces visibility of the dominant intraprostatic lesion ( 73 ).…”

Section: Opportunities For Future Development Of Mrgrt In Prostate Camentioning

confidence: 99%

MR-Guided Radiotherapy for Prostate Cancer

et al. 2020

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External beam radiotherapy remains the primary treatment modality for localized prostate cancer. The radiobiology of prostate carcinoma lends itself to hypofractionation, with recent studies showing good outcomes with shorter treatment schedules. However, the ability to accurately deliver hypofractionated treatment is limited by current image-guided techniques. Magnetic resonance imaging is the main diagnostic tool for localized prostate cancer and its use in the therapeutic setting offers anatomical information to improve organ delineation. MR-guided radiotherapy, with daily re-planning, has shown early promise in the accurate delivery of radiotherapy. In this article, we discuss the shortcomings of current image-guidance strategies and the potential benefits and limitations of MR-guided treatment for prostate cancer. We also recount present experiences of MR-linac workflow and the opportunities afforded by this technology.

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“…Rooted in radiology, radiomics focuses on extracting quantitative features by deep mining of clinical images [87,88,89]. Compared with pathology, cancer imaging in radiology is much less invasive and often captures organ- or system-level physiological and pathological features (e.g., hemodynamics based on functional MRI [90] and glucose uptake based on PET [91]). Therefore, pathological and radiological images provide complementary information for the characterization of a tumor.…”

Section: Future Directionsmentioning

confidence: 99%

Artificial Intelligence in Lung Cancer Pathology Image Analysis

Wang

Yang

Rong

et al. 2019

Cancers

170

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Objective: Accurate diagnosis and prognosis are essential in lung cancer treatment selection and planning. With the rapid advance of medical imaging technology, whole slide imaging (WSI) in pathology is becoming a routine clinical procedure. An interplay of needs and challenges exists for computer-aided diagnosis based on accurate and efficient analysis of pathology images. Recently, artificial intelligence, especially deep learning, has shown great potential in pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection. Materials and Methods: In this review, we aim to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer. Results: We outlined the current challenges and opportunities in lung cancer pathology image analysis, discussed the recent deep learning developments that could potentially impact digital pathology in lung cancer, and summarized the existing applications of deep learning algorithms in lung cancer diagnosis and prognosis. Discussion and Conclusion: With the advance of technology, digital pathology could have great potential impacts in lung cancer patient care. We point out some promising future directions for lung cancer pathology image analysis, including multi-task learning, transfer learning, and model interpretation.

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Functional magnetic resonance imaging in oncology: state of the art

Cited by 24 publications

References 47 publications

Combining Diagnostic Imaging and Pathology for Improving Diagnosis and Prognosis of Cancer

Combining Diagnostic Imaging and Pathology for Improving Diagnosis and Prognosis of Cancer

MR-Guided Radiotherapy for Prostate Cancer

Artificial Intelligence in Lung Cancer Pathology Image Analysis

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