1
|
Gu X, He X, Wang H, Li J, Chen R, Liu H. Dynamic Susceptibility Contrast-Enhanced Perfusion-Weighted Imaging in Differentiation Between Recurrence and Pseudoprogression in High-Grade Glioma: A Meta-analysis. J Comput Assist Tomogr 2024; 48:303-310. [PMID: 37654056 DOI: 10.1097/rct.0000000000001543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
INTRODUCTION In glioma patients that have undergone surgical tumor resection, the ability to reliably distinguish between pseudoprogression (PsP) and a recurrent tumor (RT) is of key clinical importance. Accordingly, this meta-analysis evaluated the utility of dynamic susceptibility contrast-enhanced perfusion-weighted imaging as a means of distinguishing between PsP and RT when analyzing patients with high-grade glioma. MATERIALS AND METHODS The PubMed, Web of Science, and Wanfang databases were searched for relevant studies. Pooled analyses of sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) values were conducted, after which the area under the curve (AUC) for summary receiver operating characteristic curves was computed. RESULTS This meta-analysis ultimately included 21 studies enrolling 879 patients with 888 lesions. Cerebral blood volume-associated diagnostic results were reported in 20 of the analyzed studies, and the respective pooled sensitivity, specificity, PLR, and NLR values were 86% (95% confidence interval [CI], 0.81-0.89), 83% (95% CI, 0.77-0.87), 4.94 (95% CI, 3.61-6.75), and 0.18 (95% CI, 0.13-0.23) for these 20 studies. The corresponding AUC value was 0.91 (95% CI, 0.88-0.93), and the publication bias risk was low ( P = 0.976). Cerebral blood flow-related diagnostic results were additionally reported in 6 of the analyzed studies, with respective pooled sensitivity, specificity, PLR, and NLR values of 85% (95% CI, 0.78-0.90), 85% (95% CI, 0.76-0.91), 5.54 (95% CI, 3.40-9.01), and 0.18 (95% CI, 0.12-0.26). The corresponding AUC value was 0.92 (95% CI, 0.89-0.94), and the publication bias risk was low ( P = 0.373). CONCLUSIONS The present meta-analysis results suggest that dynamic susceptibility contrast-enhanced perfusion-weighted imaging represents an effective diagnostic approach to distinguishing between PsP and RT in high-grade glioma patients.
Collapse
Affiliation(s)
| | - Xining He
- From the Departments of Neurosurgery
| | - Hualong Wang
- Radiology, Binzhou People's Hospital, Binzhou, China
| | | | | | | |
Collapse
|
2
|
Artificial Intelligence in Cancer Research: Trends, Challenges and Future Directions. LIFE (BASEL, SWITZERLAND) 2022; 12:life12121991. [PMID: 36556356 PMCID: PMC9786074 DOI: 10.3390/life12121991] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/18/2022] [Accepted: 11/25/2022] [Indexed: 11/30/2022]
Abstract
The World Health Organization (WHO), in their 2022 report, identified cancer as one of the leading causes of death, accounting for about 16% of deaths worldwide. The Cancer-Moonshot community aims to reduce the cancer death rate by half in the next 25 years and wants to improve the lives of cancer-affected people. Cancer mortality can be reduced if detected early and treated appropriately. Cancers like breast cancer and cervical cancer have high cure probabilities when treated early in accordance with best practices. Integration of artificial intelligence (AI) into cancer research is currently addressing many of the challenges where medical experts fail to bring cancer to control and cure, and the outcomes are quite encouraging. AI offers many tools and platforms to facilitate more understanding and tackling of this life-threatening disease. AI-based systems can help pathologists in diagnosing cancer more accurately and consistently, reducing the case error rates. Predictive-AI models can estimate the likelihood for a person to get cancer by identifying the risk factors. Big data, together with AI, can enable medical experts to develop customized treatments for cancer patients. The side effects from this kind of customized therapy will be less severe in comparison with the generalized therapies. However, many of these AI tools will remain ineffective in fighting against cancer and saving the lives of millions of patients unless they are accessible and understandable to biologists, oncologists, and other medical cancer researchers. This paper presents the trends, challenges, and future directions of AI in cancer research. We hope that this paper will be of help to both medical experts and technical experts in getting a better understanding of the challenges and research opportunities in cancer diagnosis and treatment.
Collapse
|
3
|
Qin J, Yu Z, Yao Y, Liang Y, Tang Y, Wang B. Susceptibility-weighted imaging cannot distinguish radionecrosis from recurrence in brain metastases after radiotherapy: a comparison with high-grade gliomas. Clin Radiol 2022; 77:e585-e591. [PMID: 35676103 DOI: 10.1016/j.crad.2022.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 05/06/2022] [Indexed: 11/16/2022]
Abstract
AIM To explore the efficiency of susceptibility-weighted imaging (SWI) in the differential diagnosis of recurrence from radionecrosis in brain metastases (BM) and in high-grade gliomas (HGG). MATERIALS AND METHODS From September 2016 to November 2018, 56 patients with BM and 42 patients with HGG were included in this retrospective study. BM and HGG were assigned to the recurrence and radionecrosis groups according to their histopathology or follow-up results. The proportion of dark signal intensity (proDSI), which was defined as the area of dark signal on SWI or the enhancing area on contrast-enhanced T1-weighted imaging (T1WI), was calculated for each patient. Analysis of variance (ANOVA) with Tukey's honestly significant difference test was used for the repeat multiple comparisons. Receiver operating characteristic curve analysis was performed to validate the diagnostic performance. RESULTS For HGG, the proDSI in the recurrence group was significantly lower than that in the radionecrosis group (0.13 ± 0.05 versus 0.43 ± 0.11, p<0.001); however, for BM, no statistical difference was found between groups (0.49 ± 0.09 versus 0.46 ± 0.08, p=0.26). proDSI had the best diagnostic performance (AUC = 0.87, 95% CI: 0.76-0.98; sensitivity = 0.87; specificity = 0.88) for HGG, when a cut-off value of 0.21 was selected. CONCLUSIONS Semi-quantitative analysis using SWI is feasible for the differential diagnosis between recurrence and radionecrosis in HGG, but is not feasible in BM. Semi-quantitative assessment based on SWI should interpreted with caution in BM after radiotherapy in clinical practice.
Collapse
Affiliation(s)
- J Qin
- School of Medicine, Qingdao University, Qingdao, 266021, PR China; Department of Radiology, Rizhao Central Hospital, Rizhao, 276800, PR China
| | - Z Yu
- Department of Health Management Center, Qilu Hospital of Shandong University, Jinan, 250012, PR China; Nursing Theory & Practice Innovation Research Center of Shandong University, Jinan, 250012, PR China
| | - Y Yao
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, PR China
| | - Y Liang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, PR China
| | - Y Tang
- Department of Radiology, Rizhao Central Hospital, Rizhao, 276800, PR China
| | - B Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, PR China.
| |
Collapse
|
4
|
Fu R, Szidonya L, Barajas RF, Ambady P, Varallyay C, Neuwelt EA. Diagnostic performance of DSC perfusion MRI to distinguish tumor progression and treatment-related changes: a systematic review and meta-analysis. Neurooncol Adv 2022; 4:vdac027. [PMID: 35386567 PMCID: PMC8982196 DOI: 10.1093/noajnl/vdac027] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background In patients with high-grade glioma (HGG), true disease progression and treatment-related changes often appear similar on magnetic resonance imaging (MRI), making it challenging to evaluate therapeutic response. Dynamic susceptibility contrast (DSC) MRI has been extensively studied to differentiate between disease progression and treatment-related changes. This systematic review evaluated and synthesized the evidence for using DSC MRI to distinguish true progression from treatment-related changes. Methods We searched Ovid MEDLINE and the Ovid MEDLINE in-process file (January 2005-October 2019) and the reference lists. Studies on test performance of DSC MRI using relative cerebral blood volume in HGG patients were included. One investigator abstracted data, and a second investigator confirmed them; two investigators independently assessed study quality. Meta-analyses were conducted to quantitatively synthesize area under the receiver operating curve (AUROC), sensitivity, and specificity. Results We screened 1177 citations and included 28 studies with 638 patients with true tumor progression, and 430 patients with treatment-related changes. Nineteen studies reported AUROC and the combined AUROC is 0.85 (95% CI, 0.81-0.90). All studies contributed data for sensitivity and specificity, and the pooled sensitivity and specificity are 0.84 (95% CI, 0.80-0.88), and 0.78 (95% CI, 0.72-0.83). Extensive subgroup analyses based on study, treatment, and imaging characteristics generally showed similar results. Conclusions There is moderate strength of evidence that relative cerebral blood volume obtained from DSC imaging demonstrated "excellent" ability to discriminate true tumor progression from treatment-related changes, with robust sensitivity and specificity.
Collapse
Affiliation(s)
- Rongwei Fu
- Oregon Health & Science University-Portland State University, School of Public Health, Portland, Oregon, USA.,Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
| | - Laszlo Szidonya
- Department of Radiology, Oregon Health & Science University, Portland, Oregon, USA.,Neuro-Oncology Program, Oregon Health & Science University, Portland, Oregon, USA.,Heart and Vascular Center, Diagnostic Radiology, Semmelweis University, Budapest, Hungary
| | - Ramon F Barajas
- Department of Radiology, Oregon Health & Science University, Portland, Oregon, USA.,Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Knight Cancer Institute Translational Oncology Program, Oregon Health & Science University, Portland, Oregon, USA
| | - Prakash Ambady
- Neuro-Oncology Program, Oregon Health & Science University, Portland, Oregon, USA
| | | | - Edward A Neuwelt
- Neuro-Oncology Program, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurosurgery, Oregon Health and Sciences University, Portland, Oregon, USA.,Office of Research and Development, Department of Veterans Affairs Medical Center, Portland, Oregon, USA
| |
Collapse
|
5
|
Abstract
PURPOSE OF REVIEW This review aims to cover current MRI techniques for assessing treatment response in brain tumors, with a focus on radio-induced lesions. RECENT FINDINGS Pseudoprogression and radionecrosis are common radiological entities after brain tumor irradiation and are difficult to distinguish from real progression, with major consequences on daily patient care. To date, shortcomings of conventional MRI have been largely recognized but morphological sequences are still used in official response assessment criteria. Several complementary advanced techniques have been proposed but none of them have been validated, hampering their clinical use. Among advanced MRI, brain perfusion measures increase diagnostic accuracy, especially when added with spectroscopy and susceptibility-weighted imaging. However, lack of reproducibility, because of several hard-to-control variables, is still a major limitation for their standardization in routine protocols. Amide Proton Transfer is an emerging molecular imaging technique that promises to offer new metrics by indirectly quantifying intracellular mobile proteins and peptide concentration. Preliminary studies suggest that this noncontrast sequence may add key biomarkers in tumor evaluation, especially in posttherapeutic settings. SUMMARY Benefits and pitfalls of conventional and advanced imaging on posttreatment assessment are discussed and the potential added value of APT in this clinicoradiological evolving scenario is introduced.
Collapse
Affiliation(s)
- Lucia Nichelli
- Department of Neuroradiology, Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix
- Sorbonne Université, INSERM, CNRS, Assistance Publique-Hôpitaux de Paris, Institut du Cerveau et de la Moelle épinière, boulevard de l’Hôpital, Paris
| | - Stefano Casagranda
- Department of Research & Innovation, Olea Medical, avenue des Sorbiers, La Ciotat, France
| |
Collapse
|
6
|
Preisner F, Friedmann-Bette B, Wehrstein M, Vollherbst DFJ, Heiland S, Bendszus M, Hilgenfeld T. In Vivo Visualization of Tissue Damage Induced by Percutaneous Muscle Biopsy via Novel High-Resolution MR Imaging. Med Sci Sports Exerc 2021; 53:1367-1374. [PMID: 33449606 DOI: 10.1249/mss.0000000000002601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
PURPOSE Percutaneous muscle biopsy is the gold standard for tissue assessment in clinical practice and scientific studies. The aim of this study was to assess and quantify the ensuing tissue damage by in vivo magnetic resonance imaging (MRI). METHODS In this prospective study, we enrolled 22 healthy participants who underwent MRI of the thigh musculature about 1 wk after a percutaneous muscle biopsy of the vastus lateralis muscle. A total of 17 participants also volunteered for a second MR examination 2 wk after biopsy. Volumes of susceptibility-weighted imaging (SWI) lesions and muscle edema were assessed by SWI and T2-weighted MRI, respectively, after manual segmentation by two independent readers. For quantitative in vivo hematoma volume assessment, we additionally determined signal changes induced by experimental hematoma in an ex vivo model. RESULTS Mean overall volume of SWI lesions 1 wk after biopsy was 26.5 ± 21.7 μL, accompanied by a mean perifocal edema volume of 790.1 ± 591.4 μL. In participants who underwent two examinations, mean volume of SWI lesions slightly decreased from 29.8 ± 23.6 to 23.9 ± 16.8 μL within 1 wk (P = 0.13). Muscle edema volume decreased from 820.2 ± 632.4 to 359.6 ± 207.3 μL at the same time (P = 0.006). By calibration with the ex vivo findings, signal alterations on SWI corresponded to a blood volume of approximately 10-50 μL. CONCLUSIONS Intramuscular hematoma and accompanying muscle edema after percutaneous biopsy are small and decrease rapidly within the first 2 wk. These in vivo findings underline the limited invasiveness of the procedure.
Collapse
Affiliation(s)
- Fabian Preisner
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, GERMANY
| | - Birgit Friedmann-Bette
- Department of Sports Medicine (Internal Medicine VII), Medical Clinic, Heidelberg University Hospital, Heidelberg, GERMANY
| | - Michaela Wehrstein
- Department of Sports Medicine (Internal Medicine VII), Medical Clinic, Heidelberg University Hospital, Heidelberg, GERMANY
| | | | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, GERMANY
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, GERMANY
| | - Tim Hilgenfeld
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, GERMANY
| |
Collapse
|
7
|
Cluceru J, Nelson SJ, Wen Q, Phillips JJ, Shai A, Molinaro AM, Alcaide-Leon P, Olson MP, Nair D, LaFontaine M, Chunduru P, Villanueva-Meyer JE, Cha S, Chang SM, Berger MS, Lupo JM. Recurrent tumor and treatment-induced effects have different MR signatures in contrast enhancing and non-enhancing lesions of high-grade gliomas. Neuro Oncol 2021; 22:1516-1526. [PMID: 32319527 DOI: 10.1093/neuonc/noaa094] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Differentiating treatment-induced injury from recurrent high-grade glioma is an ongoing challenge in neuro-oncology, in part due to lesion heterogeneity. This study aimed to determine whether different MR features were relevant for distinguishing recurrent tumor from the effects of treatment in contrast-enhancing lesions (CEL) and non-enhancing lesions (NEL). METHODS This prospective study analyzed 291 tissue samples (222 recurrent tumor, 69 treatment-effect) with known coordinates on imaging from 139 patients who underwent preoperative 3T MRI and surgery for a suspected recurrence. 8 MR parameter values were tested from perfusion-weighted, diffusion-weighted, and MR spectroscopic imaging at each tissue sample location for association with histopathological outcome using generalized estimating equation models for CEL and NEL tissue samples. Individual cutoff values were evaluated using receiver operating characteristic curve analysis with 5-fold cross-validation. RESULTS In tissue samples obtained from CEL, elevated relative cerebral blood volume (rCBV) was associated with the presence of recurrent tumor pathology (P < 0.03), while increases in normalized choline (nCho) and choline-to-NAA index (CNI) were associated with the presence of recurrent tumor pathology in NEL tissue samples (P < 0.008). A mean CNI cutoff value of 2.7 had the highest performance, resulting in mean sensitivity and specificity of 0.61 and 0.81 for distinguishing treatment-effect from recurrent tumor within the NEL. CONCLUSION Although our results support prior work that underscores the utility of rCBV in distinguishing the effects of treatment from recurrent tumor within the contrast enhancing lesion, we found that metabolic parameters may be better at differentiating recurrent tumor from treatment-related changes in the NEL of high-grade gliomas.
Collapse
Affiliation(s)
- Julia Cluceru
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California
| | - Sarah J Nelson
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California
| | - Qiuting Wen
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California
| | - Joanna J Phillips
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California.,Department of Neurological Surgery, University of California San Francisco, San Francisco, California.,Department of Pathology, University of California San Francisco, San Francisco, California
| | - Anny Shai
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California
| | - Annette M Molinaro
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California
| | - Paula Alcaide-Leon
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California
| | - Marram P Olson
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California
| | - Devika Nair
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California
| | - Marisa LaFontaine
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California
| | - Pranathi Chunduru
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California
| | - Javier E Villanueva-Meyer
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California
| | - Soonmee Cha
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California
| | - Susan M Chang
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California
| | - Mitchel S Berger
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California
| | - Janine M Lupo
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California
| |
Collapse
|
8
|
Wang L, Wei L, Wang J, Li N, Gao Y, Ma H, Qu X, Zhang M. Evaluation of perfusion MRI value for tumor progression assessment after glioma radiotherapy: A systematic review and meta-analysis. Medicine (Baltimore) 2020; 99:e23766. [PMID: 33350761 PMCID: PMC7769293 DOI: 10.1097/md.0000000000023766] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 11/15/2020] [Indexed: 01/25/2023] Open
Abstract
OBJECTIVES This study aimed to evaluate the diagnostic performance of magnetic resonance perfusion-weighted imaging (PWI) as a noninvasive method to assess post-treatment radiation effect and tumor progression in patients with glioma. METHODS A systematic literature search was performed in the PubMed, Cochrane Library, and Embase databases up to March 2020. The quality of the included studies was assessed by the quality assessment of diagnostic accuracy studies 2. Data were extracted to calculate sensitivity, specificity, and diagnostic odds ratio (DOR), 95% Confidence interval (CI) and analyze the heterogeneity of the studies (Spearman correlation coefficient, I2 test). We performed meta-regression and subgroup analyses to identify the impact of study heterogeneity. RESULTS Twenty studies were included, with available data for analysis on 939 patients and 968 lesions. All included studies used dynamic susceptibility contrast (DSC) PWI, four also used dynamic contrast-enhanced PWI, and three also used arterial spin marker imaging PWI. When DSC was considered, the pooled sensitivity and specificity were 0.83 (95% CI, 0.79 to 0.86) and 0.83 (95% CI, 0.78 to 0.87), respectively; pooled DOR, 21.31 (95% CI, 13.07 to 34.73); area under the curve (AUC), 0.887; Q∗, 0.8176. In studies using dynamic contrast-enhanced, the pooled sensitivity and specificity were 0.73 (95% CI, 0.66 to 0.80) and 0.80 (95% CI, 0.69 to 0.88), respectively; pooled DOR, 10.83 (95% CI, 2.01 to 58.43); AUC, 0.9416; Q∗, 0.8795. In studies using arterial spin labeling, the pooled sensitivity and specificity were 0.79 (95% CI, 0.69 to 0.87) and 0.78 (95% CI, 0.67 to 0.87), respectively; pooled DOR, 15.63 (95% CI, 4.61 to 53.02); AUC, 0.8786; Q∗, 0.809. CONCLUSIONS Perfusion magnetic resonance imaging displays moderate overall accuracy in identifying post-treatment radiation effect and tumor progression in patients with glioma. Based on the current evidence, DSC-PWI is a relatively reliable option for assessing tumor progression after glioma radiotherapy.
Collapse
Affiliation(s)
| | - Lizhou Wei
- Department of neurosurgery, Xijing hospital, Fourth military medical university
| | | | - Na Li
- Department of radiology, Ninth Hospital of Xi’an
| | - Yanzhong Gao
- Department of radiology, Ninth Hospital of Xi’an
| | - Hongge Ma
- Department of radiology, Ninth Hospital of Xi’an
| | - Xinran Qu
- Department of radiology, Ninth Hospital of Xi’an
| | - Ming Zhang
- Department of Radiology, the First Affiliated Hospital of Xi ’an Jiao tong University, Shaanxi Province, China
| |
Collapse
|
9
|
Magnetic resonance imaging evaluation of brain glioma before postoperative radiotherapy. Clin Transl Oncol 2020; 23:820-826. [PMID: 32857338 DOI: 10.1007/s12094-020-02474-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 08/03/2020] [Indexed: 02/07/2023]
Abstract
PURPOSE To investigate the magnetic resonance imaging (MRI) images of brain glioma before postoperative radiotherapy, and to provide reference for the delineation of postoperative radiotherapy target area. METHODS Retrospective analysis was performed on 106 cases of brain glioma confirmed by surgery and pathology in our hospital, including 70 cases of high-grade glioma (HGG) and 36 cases of low-grade glioma (LGG). The MRI images of the lesions within 1 month before and after surgery were analyzed, the apparent diffusion coefficient (ADC) values in the near and far tumor areas were measured, respectively, and the corresponding rADC values were calculated. RESULTS The incidence of residual tumors of postoperative HGG and LGG was 0, 15.7% (0/36, 11/70), respectively. The incidence of postoperative reactive enhancement was 11.0% and 52.9% (4/36 and 37/70), respectively. About 30.6% and 81.4% (11/36 and 57/70) of patients with adjacent meningeal enhancement were found in the operative area. CONCLUSIONS The MRI images of HGG and LGG before postoperative radiotherapy had certain characteristics, providing a favorable guidance for the delineation of the target area of radiotherapy and the formulation of treatment plan.
Collapse
|
10
|
Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, Allison T, Arnaout O, Abbosh C, Dunn IF, Mak RH, Tamimi RM, Tempany CM, Swanton C, Hoffmann U, Schwartz LH, Gillies RJ, Huang RY, Aerts HJWL. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin 2019; 69:127-157. [PMID: 30720861 PMCID: PMC6403009 DOI: 10.3322/caac.21552] [Citation(s) in RCA: 589] [Impact Index Per Article: 117.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.
Collapse
Affiliation(s)
- Wenya Linda Bi
- Assistant Professor of Neurosurgery, Department of Neurosurgery, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Ahmed Hosny
- Research Scientist, Department of Radiation Oncology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Matthew B. Schabath
- Associate Member, Department of Cancer EpidemiologyH. Lee Moffitt Cancer Center and Research InstituteTampaFL
| | - Maryellen L. Giger
- Professor of Radiology, Department of RadiologyUniversity of ChicagoChicagoIL
| | - Nicolai J. Birkbak
- Research Associate, The Francis Crick InstituteLondonUnited Kingdom
- Research Associate, University College London Cancer InstituteLondonUnited Kingdom
| | - Alireza Mehrtash
- Research Assistant, Department of Radiology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
- Research Assistant, Department of Electrical and Computer EngineeringUniversity of British ColumbiaVancouverBCCanada
| | - Tavis Allison
- Research Assistant, Department of RadiologyColumbia University College of Physicians and SurgeonsNew YorkNY
- Research Assistant, Department of RadiologyNew York Presbyterian HospitalNew YorkNY
| | - Omar Arnaout
- Assistant Professor of Neurosurgery, Department of Neurosurgery, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Christopher Abbosh
- Research Fellow, The Francis Crick InstituteLondonUnited Kingdom
- Research Fellow, University College London Cancer InstituteLondonUnited Kingdom
| | - Ian F. Dunn
- Associate Professor of Neurosurgery, Department of Neurosurgery, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Raymond H. Mak
- Associate Professor, Department of Radiation Oncology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Rulla M. Tamimi
- Associate Professor, Department of MedicineBrigham and Women’s Hospital, Dana‐Farber Cancer Institute, Harvard Medical SchoolBostonMA
| | - Clare M. Tempany
- Professor of Radiology, Department of Radiology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Charles Swanton
- Professor, The Francis Crick InstituteLondonUnited Kingdom
- Professor, University College London Cancer InstituteLondonUnited Kingdom
| | - Udo Hoffmann
- Professor of Radiology, Department of RadiologyMassachusetts General Hospital and Harvard Medical SchoolBostonMA
| | - Lawrence H. Schwartz
- Professor of Radiology, Department of RadiologyColumbia University College of Physicians and SurgeonsNew YorkNY
- Chair, Department of RadiologyNew York Presbyterian HospitalNew YorkNY
| | - Robert J. Gillies
- Professor of Radiology, Department of Cancer PhysiologyH. Lee Moffitt Cancer Center and Research InstituteTampaFL
| | - Raymond Y. Huang
- Assistant Professor, Department of Radiology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Hugo J. W. L. Aerts
- Associate Professor, Departments of Radiation Oncology and Radiology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
- Professor in AI in Medicine, Radiology and Nuclear Medicine, GROWMaastricht University Medical Centre (MUMC+)MaastrichtThe Netherlands
| |
Collapse
|
11
|
Hansen MR, Pan E, Wilson A, McCreary M, Wang Y, Stanley T, Pinho MC, Guo X, Okuda DT. Post-gadolinium 3-dimensional spatial, surface, and structural characteristics of glioblastomas differentiate pseudoprogression from true tumor progression. J Neurooncol 2018; 139:731-738. [DOI: 10.1007/s11060-018-2920-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2018] [Accepted: 05/31/2018] [Indexed: 02/07/2023]
|
12
|
Leakage correction improves prognosis prediction of dynamic susceptibility contrast perfusion MRI in primary central nervous system lymphoma. Sci Rep 2018; 8:456. [PMID: 29323247 PMCID: PMC5765049 DOI: 10.1038/s41598-017-18901-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2017] [Accepted: 12/19/2017] [Indexed: 12/20/2022] Open
Abstract
To evaluate whether the cerebral blood volume (CBV) measurement with leakage correction from dynamic susceptibility contrast perfusion weighted imaging can be useful in predicting prognosis for primary central nervous system lymphoma (PCNSL). 46 PCNSL patients were included and classified by radiation therapy (RT) stratification into RT (n = 30) and non-RT (n = 16) groups. The corresponding histogram parameters of normalized CBV (nCBV) maps with or without leakage correction were calculated on contrast-enhanced T1 weighted image (CE T1WI) or on fluid attenuated inversion recovery image. The 75th percentile nCBV with leakage correction based on CE T1WI (T1 nCBVL75%) had a significant difference between the short and long progression free survival (PFS) subgroups of the RT group and the non-RT group, respectively. Based on the survival analysis, patients in the RT group with high T1 nCBVL75% had earlier progression than the others with a low T1 nCBVL75%. However, patients in the non-RT group with a high T1 nCBVL75% had slower progression than the others with a low T1 nCBVL75%. Based on RT stratification, the CBV with leakage correction has potential as a noninvasive biomarker for the prognosis prediction of PCNSL to identify high risk patients and it has a different correlation with the PFS based on the presence of combined RT.
Collapse
|
13
|
Dongas J, Asahina AT, Bacchi S, Patel S. Magnetic Resonance Perfusion Imaging in the Diagnosis of High-Grade Glioma Progression and Treatment-Related Changes: A Systematic Review. ACTA ACUST UNITED AC 2018. [DOI: 10.4236/ojmn.2018.83024] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|