Journal of Medical Imaging and Radiation Sciences最新文献

{"title":"The connectomics revolution: Utilizing Resting State fMRI and DTI to personalize the treatment of neurological and psychocognitive disorders","authors":"Dr Jacky Yeung","doi":"10.1016/j.jmir.2024.101467","DOIUrl":"10.1016/j.jmir.2024.101467","url":null,"abstract":"<div><h3>Background</h3><div>Connectomics has been instrumental in advancing our understanding of the intricate neural networks that underpin neurological and psychiatric conditions. Resting state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) are critical imaging modalities that provide insights into the brain's functional and structural connectivity without the need for active patient participation.</div></div><div><h3>Purpose</h3><div>This presentation is designed to showcase the pivotal role of rs-fMRI and DTI in the burgeoning field of personalized functional imaging. It will highlight how these techniques can redefine the practice of radiological technologists by integrating connectomic insights into personalized patient care.</div></div><div><h3>Methods</h3><div>The presentation will discuss the protocols for employing rs-fMRI and DTI in a clinical environment, including data collection, analysis, and interpretation strategies. It will illustrate the connectome's relevance in various neurological conditions and the ways in which these imaging techniques can contribute to the development of individualized treatment plans.</div></div><div><h3>Results</h3><div>Employing rs-fMRI and DTI for connectomic analysis has yielded promising results in pinpointing neurological disease biomarkers, deciphering psychiatric disorder pathways, and crafting tailored therapeutic interventions. These imaging modalities offer a refined perspective on brain disorders, shifting the diagnostic paradigm from a general to a more patient-centered approach.</div></div><div><h3>Conclusion</h3><div>The integration of connectomics with personalized functional imaging marks a significant advancement in the field of medical imaging. Rs-fMRI and DTI not only enhance our visualization of brain networks but also support a transition toward treatments aimed at the underlying mechanisms of disease. Embracing these techniques is crucial for the progression of personalized medicine and the enhancement of patient care outcomes.</div></div><div><h3>Implications</h3><div>The keynote will delve into the implications of these technologies for radiological technologists, stressing the necessity for a transition to a connectome-based imaging model. Additionally, it will touch upon the educational and skill development needed for practitioners to adeptly adopt and apply these sophisticated imaging techniques in a personalized healthcare setting.</div></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":"55 3","pages":"Article 101467"},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Case Study: Maxillofacial MRI of a Fetal with a Complaint of Narrowing of the Upper Alveolar Process","authors":"Dejuan Shan,&nbsp;Dr. Lianxiang Xiao","doi":"10.1016/j.jmir.2024.101505","DOIUrl":"10.1016/j.jmir.2024.101505","url":null,"abstract":"<div><h3>Purpose</h3><div>Tooth buds anomalies coincide with genetic disorders, and prenatal identification may contribute to a more accurate diagnosis. And fetal cleft lip and palate (CLP) is a common congenital facial malformation, which not only affects the appearance of children but also causes malnutrition in children with the difficulty of sucking milk. The purpose of the presentation was to improve the feasibility of fetal magnetic resonance imaging in visualizing intrauterine tooth buds alignment, CLP conditions and image quality.</div></div><div><h3>Method</h3><div>A 29-year-old pregnant woman was referred to our institution with an ultrasound report of a narrow upper alveolar process. We used 3.0T MRI Steady-State-Free-Precession (bSSFP) and Single-Shot Fast Spin-Echo (SS-FSE) sequences to examine this fetus for tooth bud abnormalities and CLP. Sagittal scanning is performed with the fetus swallowing amniotic fluid so that the tongue and palate are separated to better show the continuity of the hard and soft palate. The oblique axial position is scanned along the sagittal superior alveolar process to show the development of the tooth buds.</div></div><div><h3>Result</h3><div>Compared to the SS-FSE sequence, bSSFP sequence improves the SNR and contrast, and better shows the alignment of the tooth buds as well as the palate. It was finally confirmed that the fetus had a narrow upper alveolar eminence for abnormal tooth buds alignment (deciduous central incisor teeth, lateral incisor teeth and cusp incisors)and that there was no CLP.</div></div><div><h3>Conclusion</h3><div>The use of the bSSFP sequence to better shows fetal maxillofacial structures in the presence of amniotic fluid swallowing, also improves diagnostic accuracy and the diagnosis of associated syndromes.</div></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":"55 3","pages":"Article 101505"},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pancreatic Cancer Research from the Perspective of the RTT","authors":"Dr Aileen Duffton","doi":"10.1016/j.jmir.2024.101455","DOIUrl":"10.1016/j.jmir.2024.101455","url":null,"abstract":"<div><div>Pancreatic cancer has the lowest one-year survival of any cancer in the UK. This illustrates the poor outlook suffered by patients and recognises it as a cancer of unmet need that requires further investigation. Pancreatic cancer is a global problem and according to worldwide cancer statistics, it is the 7^th top cause of cancer death. This presentation will cover a general background of ongoing pancreatic research to first highlight the bigger picture, before focussing on the current landscape of radiotherapy (RT) research for pancreatic cancer. This will be from a RTT perspective and will include a detailed case study of building a research career that investigates the many challenges of treating these patients.</div><div>RT challenges include abdominal motion that causes many uncertainties throughout the full RT pathway, and impacts image quality at each stage. Topics to be discussed will be accuracy and precision of treatment planning and delivery, acquisition of high-quality images with image guided-RT (IGRT), dealing with dose-limiting organs at risk, and determining response using advanced functional imaging protocols. The discussed issues require multi-disciplinary strategies to overcome them.</div></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":"55 3","pages":"Article 101455"},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of Artificial Intelligence in Neck Ultrasound in the Era of Precision Medicine","authors":"Prof Michael Tin Cheung Ying","doi":"10.1016/j.jmir.2024.101458","DOIUrl":"10.1016/j.jmir.2024.101458","url":null,"abstract":"<div><div>The importance of artificial intelligence (AI) in medical healthcare is increasingly becoming apparent. There is a rapid growth of scientific research in medical AI in the past years, from 1,623 studies in 2012 to 29,947 studies in 2021, and many of these studies are related to radiology. By 2023, the FDA has approved 700 AI healthcare algorithms and 527 (75.3%) are in radiology. In AI-empowered radiology, the application of AI in ultrasound imaging is emerging which includes ultrasound of liver, beast, thyroid gland, lymph node, etc. Ultrasound is commonly used for the evaluation of head and neck masses. In patients with thyroid nodules, ultrasound is used for the differentiation of benign and malignant nodules, and guiding fine-needle aspiration. Ultrasound is also a common imaging modality to assess neck lymph nodes in head and neck cancer patients. Various AI-empowered and computer-assisted diagnostic tools for ultrasound examination of thyroid nodules are available. AI-based algorithms for lymph node segmentation and classification in ultrasound images are emerging. They help clinicians improve diagnostic accuracy and guide patient management. In this talk, different AI-empowered diagnostic tools for thyroid and lymph node ultrasound imaging will be introduced and discussed.</div></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":"55 3","pages":"Article 101458"},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved patient outcomes and risk mitigation in Emergency Departments using a hybrid Radiographer Comment model","authors":"Ms Ingrid Klobasa ,&nbsp;Mr Gary Denham ,&nbsp;Emeritus Prof Marilyn Baird ,&nbsp;Prof. Jenny Hiow Hui Sim ,&nbsp;Prof Dennis Petrie ,&nbsp;Prof Derek Roebuck ,&nbsp;Mr James Abood ,&nbsp;Mr Joshua Best ,&nbsp;Ms Allie Tonks ,&nbsp;Mrs Caitlin Tu ,&nbsp;Mr Christopher Jones","doi":"10.1016/j.jmir.2024.101538","DOIUrl":"10.1016/j.jmir.2024.101538","url":null,"abstract":"<div><h3>Background/Purpose</h3><div>Verbal communication of medical imaging findings can be misinterpreted and lacks transparency. A hybrid model using radiographer comments and verbal notification to Emergency Departments (ED) was piloted across five hospitals for the more timely and safe communication of abnormal general x-ray appearances at point of care. Pilot data was evaluated to identify patient benefits and risks.</div></div><div><h3>Methods</h3><div>A multidisciplinary steering group advised on design and implementation strategies. The radiographer comments were transmitted from imaging consoles to ED dashboards and verbal calls provided to the ED doctor/referring team for critical/urgent conditions.</div><div>Radiographer comments (n=1102) were sent to five Emergency Departments (ED) by 69 radiographers (24/7) for a minimum of three months. Positive Predictive Values (PPV), reporting Turn Around Times (TAT) and clinically significant cases were collected at pilot sites. Radiographer comments were compared with radiology reports and classified as True Positive (TP), False Positive (FP) or indeterminate (ID) by two independent auditors. FP and ID comments were investigated with ED referrers and/or site radiologists. Risk assessments were conducted by two independent radiologists using low, moderate, high-very high categories. Radiology report discrepancies found incidentally were confirmed with ED doctors and further imaging data. Wilson Score Intervals determined confidence levels.</div></div><div><h3>Results</h3><div>The average pooled PPV was 0.96; (0.949 - 0.972; 95% CI). Incorrect comments (42) were analysed for potential harm; (3.9%; 95% CI: 2.9 - 5.3). A risk assessment for these demonstrated 37 low, five moderate and no high-very high-risk cases. 282 patient benefits (26.4 %; 95% CI: 23.8 – 29.1%) and 42 radiology report discrepancies were identified; (3.9%; 95% CI: 2.9 - 5.3).</div></div><div><h3>Conclusions</h3><div>The model is based on patient advocacy and has the potential to save lives. A quarter of patients benefited from radiographer comments. Risk mitigation was possible in 3.9% of cases. No adverse outcomes were reported.</div></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":"55 3","pages":"Article 101538"},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A preliminary study of deep learning-based compressed sensing accelerated mDIXON for segmented coronary adipose tissue evaluation in patients with suspected coronary artery disease","authors":"Pengfei Peng,&nbsp;Dr Jiayu Sun","doi":"10.1016/j.jmir.2024.101503","DOIUrl":"10.1016/j.jmir.2024.101503","url":null,"abstract":"<div><h3>Background</h3><div>The secretion of dysfunctional PCAT is positively correlated with coronary artery stenosis, degree of calcification, and plaque progression. It is important to develop novel clinical diagnostic tools for coronary heart disease based on PCAT assessment. Homsi et al. introduced and validated coronary magnetic resonance angiography (MRA), based on the three-dimensional (3D)-modified Dixon (mDIXON) technique, for epicardial adipose tissue quantification. Therefore, the present study was to use non-contrast-enhanced compressed sensing artificial intelligence framework 3D mDIXON coronary MRA for PCAT quantification in patients with suspected CAD. It also evaluated segmented PCAT's relationship with coronary plaque characteristics and stenosis severity.</div></div><div><h3>Methods</h3><div>The study protocol was approved by the institutional ethics committee of the hospital. We included 35 symptomatic patients with CAD (111 arteries with plaque, 169 without plaque) (Figure 1). All the subjects underwent CMR on a 3T clinical MR scanner to evaluate segmented PCAT volume and fat-fraction of 8 coronary segments. We manually traced the segmented PCAT volume, and calculated the fat fraction of the segmented PCAT by formula: only fat images (F)/F + only water images (W). We compared the segmented PCAT volume and fat-fraction across 8 coronary segments with different plaque types and degrees of stenosis defined with CCTA and explored the relationship between them.</div></div><div><h3>Results</h3><div>The coronary segments with plaques had a higher segmented PCAT volume and fat-fraction than those without plaques. Meanwhile, segmented PCAT volume around mixed plaques was larger than non-calcified or calcified plaques (p = 0.014 and p &lt; 0.001) (Figure 3). There was a moderate correlation between the segmented PCAT volume and plaque type (r = 0.493, p &lt; 0.001). The fat-fraction had similar results (r = 0.480, p &lt; 0.001).</div></div><div><h3>Conclusion</h3><div>The non-contrast-enhanced, whole-heart coronary MRA framework with CSAI is able to measure segmented PCAT volume and fat-fraction. The segmented PCAT volume is more significantly associated with the coronary plaque characters than fat-fraction.</div></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":"55 3","pages":"Article 101503"},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FOCUS DWI and Deep Learning Reconstruction in breast MRI: A comparison with conventional DWI","authors":"Mrs. Yue Ming ,&nbsp;Prof Jiayu Sun ,&nbsp;Mrs. Fan Yang ,&nbsp;Dr Huilou Liang ,&nbsp;Mr. Bo Zhang","doi":"10.1016/j.jmir.2024.101543","DOIUrl":"10.1016/j.jmir.2024.101543","url":null,"abstract":"<div><h3>Purpose</h3><div>To employ deep-learning based reconstruction (DLR) to improve the SNR of FOCUS DWI for breast imaging in Asian patients and investigate the feasibility and performance of reduced-FOV FOCUS DWI and FOCUS DWI with deep learning-based reconstruction (DLR) for breast MRI in Asian patients with small breast volumes.</div></div><div><h3>Materials and Methods</h3><div>Forty-nine female patients suspected of having breast cancer from July 2023 to December 2023. They underwent breast MRI examinations using three sequences: Conventional DWI, Focus DWI, Focus-DLR DWI. Two radiologists independently assessed image quality using a 5-point Likert scale. They also outlined the lesions, calculating the signal-to-noise ratio (SNR) of the lesion, the Contrast-to-Noise Ratio (CNR) between the lesion and surrounding tissue, and the Apparent Diffusion Coefficient (ADC) of the lesion. Image scores, SNR, CNR and ADC were compared using the Friedman test.</div></div><div><h3>Results</h3><div>FOCUS-DLR DWI had higher scores in terms of the overall image quality, the anatomical details, lesion conspicuity, artifacts and distortion than conventional DWI (P&lt;0.001, P&lt;0.001, P&lt;0.001, P&lt;0.001, P&lt;0.001). The SNR of FOCUS-DLR DWI was higher than that of conventional DWI and FOCUS DWI (P&lt;0.001, P&lt;0.001), while there were no statistically significant differences between FOCUS-DWI and conventional DWI(P&gt;0.05). What's more, in terms of CNR values and ADC values, there were no significant difference among three sequences.</div></div><div><h3>Conclusion</h3><div>Our findings indicate that FOCUS DWI with deep learning-based reconstruction produces superior images than conventional DWI, enhancing the applicability of this technique in clinical practice. Deep learning-based reconstruction provides a new direction for optimizing DWI imaging techniques in Asian breast MRI.</div></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":"55 3","pages":"Article 101543"},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differentiation of Benign and Malignant Lymph Nodes using Ultrasound-based Radiomics and Machine Learning","authors":"Miss Xinyang Han ,&nbsp;Prof. Michael Tin-Cheung Ying ,&nbsp;Mr. Jingguo Qu ,&nbsp;Dr. Ziman Chen ,&nbsp;Mr. Simon Takadiyi Gunda ,&nbsp;Prof. Jing Cai ,&nbsp;Prof Jing Qin ,&nbsp;Prof. Winnie Chiu Wing Chu ,&nbsp;Prof. Ann Dorothy King","doi":"10.1016/j.jmir.2024.101544","DOIUrl":"10.1016/j.jmir.2024.101544","url":null,"abstract":"<div><h3>Background</h3><div>The evaluation of lymph node characteristics is crucial for tumor staging and patient prognosis assessment, but cytological and histopathological examinations of lymph nodes are invasive and costly. This study aims to develop machine learning models for differentiating benign and malignant lymph nodes based on radiomics features of grayscale ultrasound images and patients‘ clinical characteristics.</div></div><div><h3>Methods</h3><div>Between 2021 and 2023, a total of 285 ultrasound images of lymph nodes were collected from 88 patients. The diagnosis of lymph nodes was confirmed by pathological examination. The image feature reduction process was done by student's t-test, Pearson correlation analysis, and Random Forest feature importance selection. Six well-established machine learning models, including Support Vector Machines (SVM), Stochastic Gradient Descent (SGD), k-nearest Neighbors (KNN), Random Forest, XGBoost, and LightGBM, were developed using a combination of patient's clinical features and radiomics features of ultrasound images. The cases were randomly divided into training and test sets in an 8:2 ratio, and the area under the receiver operating characteristic curve (AUC) was adopted to evaluate model performance.</div></div><div><h3>Results</h3><div>There were 135 malignant and 150 benign cases in this study, including neck and axillary lymph nodes. A total of 11 radiomics features and one clinical feature were generated after the selection process, and they were used to build the final model. The AUC values of the SGD, SVM, KNN, Random Forest, XGBoost, and LightGBM in differentiating benign and malignant lymph nodes were 0.817, 0.765, 0.746, 0.816, 0.766, and 0.747, respectively.</div></div><div><h3>Conclusion</h3><div>By utilizing machine learning models, particularly the SGD and Random Forest, it is possible for radiomics features from ultrasound images to effectively classify benign and malignant lymph nodes, thereby improving diagnostic efficiency.</div></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":"55 3","pages":"Article 101544"},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coronary CT Angiography Applications with photon-counting CT","authors":"Ms Yee Kwan Elaine Chung","doi":"10.1016/j.jmir.2024.101483","DOIUrl":"10.1016/j.jmir.2024.101483","url":null,"abstract":"<div><div>The first photon-counting computed tomography (PCCT) was installed in Hong Kong in December 2023. I would like to discuss the background and clinical uses of quantum technology in PCCT for coronary angiography. The photon-counting detector is a new, advanced technology that uses detectors that discriminate the energy of individual photons in the x-ray beam and convert the detected individual photons into electric signals. By comparison with traditional CT (energy-integrating detector CT, EID-CT), it offers multiple advantages over standard energy-integrating detectors, including uniform photon weighting across multiple x-ray energies. 10 clinical cases of the patients were compared using PCCT and EID-CT, regarding the image quality of proximal, middle, and distal vessels, calcified plaque, stents, non-calcified plaque, and artefacts of pericardial calcification. The coronary stents and calcified plaque can be assessed by the special features of true-lumen that allow for calcium removal based on material decomposition. The ultra-high-resolution scanning protocol has advantages in demonstrating the in-stent lumen of coronary arteries. Mono-energetic images improve the diagnostic value of cardiac CT angiography. This new technology promises to overcome the blooming artefacts of heavy calcified coronary plaques or beam-hardening artefacts in patients with coronary stents. PCCT enables improved image quality and diagnostic confidence for coronary CT angiography examinations in comparison to EID-CT.</div></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":"55 3","pages":"Article 101483"},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The value of T1ρ mapping in preoperatively predicting the status of ER, PR, HER-2 and Ki-67 in breast cancer","authors":"Ms. Lanqing Yang,&nbsp;Ms. Sixian Hu,&nbsp;Ms. Yi Zeng,&nbsp;Prof. Chunchao Xia","doi":"10.1016/j.jmir.2024.101491","DOIUrl":"10.1016/j.jmir.2024.101491","url":null,"abstract":"<div><h3>Purpose</h3><div>to explore the value of T1ρ mapping in preoperatively predicting the status of ER, PR, HER-2 and Ki-67 in patients with breast cancer</div></div><div><h3>Method</h3><div>26 biopsy proved breast cancer patients were prospectively enrolled in this study. They all received preoperative clinical routine breast MRI and T1 ρ mapping sequence. Patients were grouped into ER, PR, Her-2 and Ki-67 negative (n=5, 5, 20, 13, respectively) and positive groups (n=21, 21, 6, 13, respectively) with reference to pathological results. ROIs were drawn by two radiologists along the edge of the tumor at three largest slices on T1ρ mapping images, and avoiding artifacts, blood vessels, necrosis, etc. Calculate the average value of two measurements as a final absolute T1ρ value of the lesion. Independent t test, ROC curves, analysis were used for statistical analyses.</div></div><div><h3>Results</h3><div>Patients with ER positive status had significantly lower T1ρ value than negative group (P&lt;0.01). ROC curve showed that T1p presented AUCs of 0.867 in predicting ER status. Patients with PR positive status also had significantly lower T1ρ value than negative group (P=0.01). ROC curve showed that T1p presented AUCs of 0.79 in predicting PR status. Patients with Her-2 positive status had significantly higher T1ρ value than negative group (P=0.04). ROC curve showed that T1p presented AUCs of 0.77 in predicting Her-2 status. Patients with Ki-67 positive status showed significantly higher T1ρ value than negative group (P=0.02). ROC curve showed that T1p presented AUCs of 0.75 in predicting Ki-67 status.</div></div><div><h3>Conclusion</h3><div>T1ρ Mapping has the potential for preoperative evaluation of ER, PR, HER-2, and Ki-67 status, which may give additional information to guide individualized therapeutic strategy in breast cancer.</div></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":"55 3","pages":"Article 101491"},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}