BJR open最新文献

{"title":"Radiomorphological signs and clinical severity of SARS-CoV-2 lineage B.1.1.7.","authors":"Judit Simon,&nbsp;Kajetan Grodecki,&nbsp;Sebastian Cadet,&nbsp;Aditya Killekar,&nbsp;Piotr Slomka,&nbsp;Samuel James Zara,&nbsp;Emese Zsarnóczay,&nbsp;Chiara Nardocci,&nbsp;Norbert Nagy,&nbsp;Katalin Kristóf,&nbsp;Barna Vásárhelyi,&nbsp;Veronika Müller,&nbsp;Béla Merkely,&nbsp;Damini Dey,&nbsp;Pál Maurovich-Horvat","doi":"10.1259/bjro.20220016","DOIUrl":"https://doi.org/10.1259/bjro.20220016","url":null,"abstract":"<p><strong>Objective: </strong>We aimed to assess the differences in the severity and chest-CT radiomorphological signs of SARS-CoV-2 B.1.1.7 and non-B.1.1.7 variants.</p><p><strong>Methods: </strong>We collected clinical data of consecutive patients with laboratory-confirmed COVID-19 and chest-CT imaging who were admitted to the Emergency Department between September 1- November 13, 2020 (non-B.1.1.7 cases) and March 1-March 18, 2021 (B.1.1.7 cases). We also examined the differences in the severity and radiomorphological features associated with COVID-19 pneumonia. Total pneumonia burden (%), mean attenuation of ground-glass opacities and consolidation were quantified using deep-learning research software.</p><p><strong>Results: </strong>The final population comprised 500 B.1.1.7 and 500 non-B.1.1.7 cases. Patients with B.1.1.7 infection were younger (58.5 ± 15.6 vs 64.8 ± 17.3; <i>p</i> < .001) and had less comorbidities. Total pneumonia burden was higher in the B.1.1.7 patient group (16.1% [interquartile range (IQR):6.0-34.2%] <i>vs</i> 6.6% [IQR:1.2-18.3%]; <i>p</i> < .001). In the age-specific analysis, in patients <60 years B.1.1.7 pneumonia had increased consolidation burden (0.1% [IQR:0.0-0.7%] <i>vs</i> 0.1% [IQR:0.0-0.2%]; <i>p</i> < .001), and severe COVID-19 was more prevalent (11.5% vs  4.9%; <i>p</i> = .032). Mortality rate was similar in all age groups.</p><p><strong>Conclusion: </strong>Despite B.1.1.7 patients were younger and had fewer comorbidities, they experienced more severe disease than non-B.1.1.7 patients, however, the risk of death was the same between the two groups.</p><p><strong>Advances in knowledge: </strong>Our study provides data on deep-learning based quantitative lung lesion burden and clinical outcomes of patients infected by B.1.1.7 VOC. Our findings might serve as a model for later investigations, as new variants are emerging across the globe.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9667478/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10835772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prospective multicentre analysis of the therapeutic approach and prognostic factors determining overall survival in elderly patients with non-small-cell lung carcinoma treated with curative intent.","authors":"Jon Cacicedo,&nbsp;Francisco Casquero,&nbsp;Arturo Navarro,&nbsp;Lorea Martinez-Indart,&nbsp;Olga Del Hoyo,&nbsp;Andere Frías,&nbsp;Roberto Ortiz de Zarate,&nbsp;David Büchser,&nbsp;Alfonso Gómez-Iturriaga,&nbsp;Iñigo San Miguel,&nbsp;Fernan Suarez,&nbsp;Adrian Barcena,&nbsp;Jose Luis López-Guerra","doi":"10.1259/bjro.20210058","DOIUrl":"https://doi.org/10.1259/bjro.20210058","url":null,"abstract":"<p><strong>Objective: </strong>To analyse patterns of treatment with curative intent commonly used in elderly patients with locally advanced non-small-cell lung carcinoma (NSCLC) and predictive factors of overall survival in routine clinical practice.</p><p><strong>Methods: </strong>This multicentre prospective study included consecutive patients aged ≥65 years old diagnosed with NSCLC between February 2014 and January 2018. Inclusion criteria: age ≥65 years, stage IIIA/IIIB NSCLC. Treatment decisions were taken by a multidisciplinary committee. Kaplan-Meier curves and log-rank test were used to identify which clinical/treatment-associated variables, or pre-treatment quality of life (QOL) considering EORTC QLQ-C30 (and LC13 module) were predictive of overall survival.</p><p><strong>Results: </strong>A total of 139 patients were recruited. Median follow-up was 9.9 months (1.18-57.36 months) with a median survival of 14 months (range 11-17 months). In the group>75-year-old patients, the committee recommended chemotherapy and sequential radiotherapy (55.6%) or radiotherapy alone (22.2%), rather than surgery (3.7%) or concomitant radiochemotherapy (16.5%). However, in 65- to 75-year-old patients, surgery and concomitant radiochemotherapy were recommended in half of cases (p=0.003). Regarding multivariate analysis, the risk of death was higher in patients with pre-existing heart disease (p=0.002), low score for physical functioning (p=0.0001), symptoms of dysphagia (p=0,01), chest pain (p=0.001), and those not undergoing surgical treatment (p=0.024).</p><p><strong>Conclusions: </strong>Patients >75 years received more conservative treatments. Surgery improved survival and should be carefully considered, regardless of patient age. Comorbidities and poor baseline QOL are predictive of shorter survival.</p><p><strong>Advances in knowledge: </strong>Measuring these parameters before treatment may help us to define a population of frail patients with a poorer prognosis to facilitate decision making in clinical practice.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9459856/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10829284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Treatment outcomes of stereotactic ablative body radiotherapy on oligometastases from colorectal cancer: early results of a single institution service evaluation.","authors":"Julie Duong,&nbsp;Adele Stewart-Lord,&nbsp;Prasana Nariyangadu,&nbsp;Mark Harrison,&nbsp;Yat Man Tsang","doi":"10.1259/bjro.20210071","DOIUrl":"https://doi.org/10.1259/bjro.20210071","url":null,"abstract":"<p><strong>Objective: </strong>Stereotactic ablative radiotherapy (SABR) has been suggested to be an effective non-invasive ablative therapy for oligometastases originated from colorectal cancer (CRC). This study aimed to report CRC oligometastases SABR treatment outcomes in terms of overall survival (OS), progression-free survival (PFS) and post-treatment toxicities.</p><p><strong>Methods: </strong>Treatment records of patients with CRC metachronous oligometastases who underwent SABR at a single institution between February 2015 and December 2018 were retrospectively reviewed. OS and PFS were calculated using Kaplan-Meier statistics and post-RT toxicity data was scored following CTCAE v. 4.0. Analysis of prognostic factors on OS and PFS was performed based on site of primary cancer, types of treatment to primary cancer, number of oligometastases, SABR treatment sites, intervals between treatment to primary cancer and SABR to oligometastases, biological equivalent dose, cumulative gross tumour volume and planning target volume.</p><p><strong>Results: </strong>75 patients with 86 CRC metachronous oligometastases (including liver, lung, lymph nodes and bone) were included. The median age was 65.5 years (range 42.5-87.2) with a median follow-up of 23.8 months (range 3.1-46.5). The estimated median PFS was 14.6 months (95% CI 9.6-19.6). and estimated median OS was 33.3 months (95% CI 22.9-43.7). Majority of patients tolerated SABR well with the most common acute side-effects of Grade 1 fatigue. No Grade 3 or higher toxicities were reported at any time points.Only SABR treatment sites (<i>p</i> = 0.03) and cumulative volumes of planning target volume (<i>p</i> = 0.02) were found to be statistically significant independent predictors of PFS and OS respectively.</p><p><strong>Conclusion: </strong>This study showed modest PFS, OS, and post-treatment toxicity outcomes on SABR to metachronous oligometastases from CRC. It has highlighted that cumulative tumour volume may be a stronger prognostic factor of OS comparing to the number of metastases.</p><p><strong>Advances in knowledge: </strong>There are limited data published on the efficacy and post-treatment toxicity of CRC oligometastases SABR with adequate length of follow-up. Our retrospective study suggests that cumulative tumour volume may be a stronger prognostic factor of OS comparing to the number of oligometastases.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9459869/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9080662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of bowel dilation on small bowel motility measurements with cine-MRI: assessment of two quantification techniques.","authors":"Kyra L van Rijn,&nbsp;Jaap Stoker,&nbsp;Alex Menys,&nbsp;Catharina S de Jonge","doi":"10.1259/bjro.20210049","DOIUrl":"https://doi.org/10.1259/bjro.20210049","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the effect of bowel dilation on cine-MRI small bowel motility measurements, by comparing a conventional motility score (including bowel wall and lumen) with a bowel wall-specific motility score in healthy and diseased populations.</p><p><strong>Methods: </strong>Four populations were included: 10 Crohn's patients with a stricture and prestricture dilation for segmental motility analysis, and 14 mannitol-prepared healthy subjects, 15 fasted healthy subjects and eight chronic intestinal pseudo-obstruction (CIPO) patients (characterized by dilated bowel loops) for global small bowel motility analysis. All subjects underwent a cine-MRI scan from which two motility scores were calculated: a conventional score (including bowel wall and lumen) and a bowel wall-specific score. The difference between the two scores was calculated per population and compared between groups with a one-way ANOVA and Tukey-Kramer analysis.</p><p><strong>Results: </strong>In Crohn's patients, the median (IQR) change between the conventional and wall-specific motility score was 0% (-2 to +4%) within the stricture and 0% (-1 to +7%) in the prestricture dilation. For the global small bowel, this was -1% (-5 to 0%) in mannitol-prepared healthy subjects, -2% (-6 to +2%) in fasted healthy subjects and +14% (+6 to+20%) in CIPO patients. The difference between the two motility scores in CIPO patients differed significantly from the four other groups (<i>p</i> = 0.002 to <i>p</i> < 0.001).</p><p><strong>Conclusions: </strong>The conventional small bowel motility score seems robust in Crohn's disease patients and healthy subjects. In patients with globally and grossly dilated bowel loops, a bowel-wall specific motility score may give a better representation of small bowel motility.</p><p><strong>Advances in knowledge: </strong>These findings support researchers and clinicians with making informed choices for using cine-MRI motility analysis in different populations.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9459865/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9374862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRI as a biomarker for breast cancer diagnosis and prognosis.","authors":"Francesca Galati,&nbsp;Veronica Rizzo,&nbsp;Rubina Manuela Trimboli,&nbsp;Endi Kripa,&nbsp;Roberto Maroncelli,&nbsp;Federica Pediconi","doi":"10.1259/bjro.20220002","DOIUrl":"https://doi.org/10.1259/bjro.20220002","url":null,"abstract":"<p><p>Breast cancer (BC) is the most frequently diagnosed female invasive cancer in Western countries and the leading cause of cancer-related death worldwide. Nowadays, tumor heterogeneity is a well-known characteristic of BC, since it includes several nosological entities characterized by different morphologic features, clinical course and response to treatment. Thus, with the spread of molecular biology technologies and the growing knowledge of the biological processes underlying the development of BC, the importance of imaging biomarkers as non-invasive information about tissue hallmarks has progressively grown. To date, breast magnetic resonance imaging (MRI) is considered indispensable in breast imaging practice, with widely recognized indications such as BC screening in females at increased risk, locoregional staging and neoadjuvant therapy (NAT) monitoring. Moreover, breast MRI is increasingly used to assess not only the morphologic features of the pathological process but also to characterize individual phenotypes for targeted therapies, building on developments in genomics and molecular biology features. The aim of this review is to explore the role of breast multiparametric MRI in providing imaging biomarkers, leading to an improved differentiation of benign and malignant breast lesions and to a customized management of BC patients in monitoring and predicting response to treatment. Finally, we discuss how breast MRI biomarkers offer one of the most fertile ground for artificial intelligence (AI) applications. In the era of personalized medicine, with the development of omics-technologies, machine learning and big data, the role of imaging biomarkers is embracing new opportunities for BC diagnosis and treatment.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9459861/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9080657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acknowledgement to Reviewers 2021.","authors":"","doi":"10.1259/bjro.20229001","DOIUrl":"https://doi.org/10.1259/bjro.20229001","url":null,"abstract":"","PeriodicalId":72419,"journal":{"name":"BJR open","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9989144/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9451681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correlated MR spectroscopic imaging of breast cancer to investigate metabolites and lipids: acceleration and compressed sensing reconstruction.","authors":"Ajin Joy,&nbsp;Andres Saucedo,&nbsp;Melissa Joines,&nbsp;Stephanie Lee-Felker,&nbsp;Sumit Kumar,&nbsp;Manoj K Sarma,&nbsp;James Sayre,&nbsp;Maggie DiNome,&nbsp;M Albert Thomas","doi":"10.1259/bjro.20220009","DOIUrl":"https://doi.org/10.1259/bjro.20220009","url":null,"abstract":"<p><strong>Objectives: </strong>The main objective of this work was to detect novel biomarkers in breast cancer by spreading the MR spectra over two dimensions in multiple spatial locations using an accelerated 5D EP-COSI technology.</p><p><strong>Methods: </strong>The 5D EP-COSI data were non-uniformly undersampled with an acceleration factor of 8 and reconstructed using group sparsity-based compressed sensing reconstruction. Different metabolite and lipid ratios were then quantified and statistically analyzed for significance. Linear discriminant models based on the quantified metabolite and lipid ratios were generated. Spectroscopic images of the quantified metabolite and lipid ratios were also reconstructed.</p><p><strong>Results: </strong>The 2D COSY spectra generated using the 5D EP-COSI technique showed differences among healthy, benign, and malignant tissues in terms of their mean values of metabolite and lipid ratios, especially the ratios of potential novel biomarkers based on unsaturated fatty acids, myo-inositol, and glycine. It is further shown the potential of choline and unsaturated lipid ratio maps, generated from the quantified COSY signals across multiple locations in the breast, to serve as complementary markers of malignancy that can be added to the multiparametric MR protocol. Discriminant models using metabolite and lipid ratios were found to be statistically significant for classifying benign and malignant tumor from healthy tissues.</p><p><strong>Conclusions: </strong>Accelerated 5D EP-COSI technique demonstrates the potential to detect novel biomarkers such as glycine, myo-inositol, and unsaturated fatty acids in addition to commonly reported choline in breast cancer, and facilitates metabolite and lipid ratio maps which have the potential to play a significant role in breast cancer detection.</p><p><strong>Advances in knowledge: </strong>This study presents the first evaluation of a multidimensional MR spectroscopic imaging technique for the detection of potentially novel biomarkers based on glycine, myo-inositol, and unsaturated fatty acids, in addition to commonly reported choline. Spatial mapping of choline and unsaturated fatty acid ratios with respect to water in malignant and benign breast masses are also shown. These metabolic characteristics may serve as additional biomarkers for improving the diagnostic and therapeutic evaluation of breast cancer.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9969076/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10820715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intubation and mortality prediction in hospitalized COVID-19 patients using a combination of convolutional neural network-based scoring of chest radiographs and clinical data.","authors":"Aileen O'Shea,&nbsp;Matthew D Li,&nbsp;Nathaniel D Mercaldo,&nbsp;Patricia Balthazar,&nbsp;Avik Som,&nbsp;Tristan Yeung,&nbsp;Marc D Succi,&nbsp;Brent P Little,&nbsp;Jayashree Kalpathy-Cramer,&nbsp;Susanna I Lee","doi":"10.1259/bjro.20210062","DOIUrl":"https://doi.org/10.1259/bjro.20210062","url":null,"abstract":"<p><strong>Objective: </strong>To predict short-term outcomes in hospitalized COVID-19 patients using a model incorporating clinical variables with automated convolutional neural network (CNN) chest radiograph analysis.</p><p><strong>Methods: </strong>A retrospective single center study was performed on patients consecutively admitted with COVID-19 between March 14 and April 21 2020. Demographic, clinical and laboratory data were collected, and automated CNN scoring of the admission chest radiograph was performed. The two outcomes of disease progression were intubation or death within 7 days and death within 14 days following admission. Multiple imputation was performed for missing predictor variables and, for each imputed data set, a penalized logistic regression model was constructed to identify predictors and their functional relationship to each outcome. Cross-validated area under the characteristic (AUC) curves were estimated to quantify the discriminative ability of each model.</p><p><strong>Results: </strong>801 patients (median age 59; interquartile range 46-73 years, 469 men) were evaluated. 36 patients were deceased and 207 were intubated at 7 days and 65 were deceased at 14 days. Cross-validated AUC values for predictive models were 0.82 (95% CI, 0.79-0.86) for death or intubation within 7 days and 0.82 (0.78-0.87) for death within 14 days. Automated CNN chest radiograph score was an important variable in predicting both outcomes.</p><p><strong>Conclusion: </strong>Automated CNN chest radiograph analysis, in combination with clinical variables, predicts short-term intubation and death in patients hospitalized for COVID-19 infection. Chest radiograph scoring of more severe disease was associated with a greater probability of adverse short-term outcome.</p><p><strong>Advances in knowledge: </strong>Model-based predictions of intubation and death in COVID-19 can be performed with high discriminative performance using admission clinical data and convolutional neural network-based scoring of chest radiograph severity.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9459864/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9080658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diffusion-weighted MR imaging of musculoskeletal tissues: incremental role over conventional MR imaging in bone, soft tissue, and nerve lesions.","authors":"Mina Guirguis,&nbsp;Gaurav Sharan,&nbsp;Jerry Wang,&nbsp;Avneesh Chhabra","doi":"10.1259/bjro.20210077","DOIUrl":"https://doi.org/10.1259/bjro.20210077","url":null,"abstract":"<p><p>Diffusion-weighted imaging is increasingly becoming popular in musculoskeletal radiology for its incremental role over conventional MR imaging in the diagnostic strategy and assessment of therapeutic response of bone and soft tissue lesions. This article discusses the technical considerations of diffusion-weighted imaging, how to optimize its performance, and outlines the role of this novel imaging in the identification and characterization of musculoskeletal lesions, such as bone and soft tissue tumors, musculoskeletal infections, arthritis, myopathy, and peripheral neuropathy. The readers can use the newly learned concepts from the presented material containing illustrated case examples to enhance their conventional musculoskeletal imaging and interventional practices and optimize patient management, their prognosis, and outcomes.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9667480/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10826528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of cancer outcome assessment using MRI: A review of deep-learning methods.","authors":"Yousef Mazaheri,&nbsp;Sunitha B Thakur,&nbsp;Almir Gv Bitencourt,&nbsp;Roberto Lo Gullo,&nbsp;Andreas M Hötker,&nbsp;David D B Bates,&nbsp;Oguz Akin","doi":"10.1259/bjro.20210072","DOIUrl":"https://doi.org/10.1259/bjro.20210072","url":null,"abstract":"<p><p>Accurate evaluation of tumor response to treatment is critical to allow personalized treatment regimens according to the predicted response and to support clinical trials investigating new therapeutic agents by providing them with an accurate response indicator. Recent advances in medical imaging, computer hardware, and machine-learning algorithms have resulted in the increased use of these tools in the field of medicine as a whole and specifically in cancer imaging for detection and characterization of malignant lesions, prognosis, and assessment of treatment response. Among the currently available imaging techniques, magnetic resonance imaging (MRI) plays an important role in the evaluation of treatment assessment of many cancers, given its superior soft-tissue contrast and its ability to allow multiplanar imaging and functional evaluation. In recent years, deep learning (DL) has become an active area of research, paving the way for computer-assisted clinical and radiological decision support. DL can uncover associations between imaging features that cannot be visually identified by the naked eye and pertinent clinical outcomes. The aim of this review is to highlight the use of DL in the evaluation of tumor response assessed on MRI. In this review, we will first provide an overview of common DL architectures used in medical imaging research in general. Then, we will review the studies to date that have applied DL to magnetic resonance imaging for the task of treatment response assessment. Finally, we will discuss the challenges and opportunities of using DL within the clinical workflow.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9459949/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9080656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}