European Journal of Radiology Open最新文献

{"title":"Peroneus brevis split tear – A challenging diagnosis: A pictorial review of magnetic resonance and ultrasound imaging. Part 1. Anatomical basis and clinical insights","authors":"Katarzyna Bokwa-Dąbrowska ,&nbsp;Rafał Zych ,&nbsp;Dan Mocanu ,&nbsp;Michael Huuskonen ,&nbsp;Dawid Dziedzic ,&nbsp;Pawel Szaro","doi":"10.1016/j.ejro.2024.100633","DOIUrl":"10.1016/j.ejro.2024.100633","url":null,"abstract":"<div><div>Diagnosing peroneus brevis split tears is a significant challenge, as many cases are missed both clinically and on imaging. Anatomical variations within the superior peroneal tunnel can contribute to peroneus brevis split tears or instability of the peroneal tendons. However, determining which anatomical variations predispose patients to these injuries remains challenging due to conflicting data in the literature. In this review, we present the current understanding of the role of anatomical variants in the development of peroneus brevis split tears. Many studies emphasize the significance of the retromalleolar groove and retromalleolar tubercle, the impact of a low-lying muscle belly, and the presence of accessory muscles within the superior peroneal tunnel as contributors to peroneal pathology. Hypertrophy of the peroneal tubercle or post-traumatic irregularities in the surface of the retromalleolar groove can accelerate degenerative changes in the peroneal tendons, potentially leading to peroneus brevis split tears. The topographic anatomy of the superior peroneal tunnel is essential for systematically performing ultrasound and interpreting magnetic resonance imaging of the ankle. The first part of this review focuses on the anatomical foundations of imaging diagnostics for peroneus brevis pathology. In the second part, we will examine the radiological spectrum of peroneal tendon injuries, offering a framework to enhance diagnostic confidence in this frequently underdiagnosed pathology.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100633"},"PeriodicalIF":1.8,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11764704/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048214","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":"CT-based intratumoral and peritumoral radiomics nomogram to predict spread through air spaces in lung adenocarcinoma with diameter ≤ 3 cm: A multicenter study","authors":"Yangfan Su ,&nbsp;Junli Tao ,&nbsp;Xiaosong Lan ,&nbsp;Changyu Liang ,&nbsp;Xuemei Huang ,&nbsp;Jiuquan Zhang ,&nbsp;Kai Li ,&nbsp;Lihua Chen","doi":"10.1016/j.ejro.2024.100630","DOIUrl":"10.1016/j.ejro.2024.100630","url":null,"abstract":"<div><h3>Purpose</h3><div>The aim of this study was to explore and develop a preoperative and noninvasive model for predicting spread through air spaces (STAS) status in lung adenocarcinoma (LUAD) with diameter ≤ 3 cm.</div></div><div><h3>Methods</h3><div>This multicenter retrospective study included 640 LUAD patients. Center I included 525 patients (368 in the training cohort and 157 in the validation cohort); center II included 115 patients (the test cohort). We extracted radiomics features from the intratumor, extended tumor and peritumor regions. Multivariate logistic regression and boruta algorithm were used to select clinical independent risk factors and radiomics features, respectively. We developed a clinical model and four radiomics models (the intratumor model, extended tumor model, peritumor model and fusion model). A nomogram based on prediction probability value of the optimal radiomics model and clinical independent risk factors was developed to predict STAS status.</div></div><div><h3>Results</h3><div>Maximum diameter and nodule type were clinical independent risk factors. The extended tumor model achieved satisfactory STAS status discrimination performance with the AUC of 0.74, 0.71 and 0.80 in the three cohorts, respectively, performed better than other radiomics models. The integrated discrimination improvement value revealed that the nomogram outperformed compared to the clinical model with the value of 12 %. Patients with high nomogram score (≥ 77.31) will be identified as STAS-positive.</div></div><div><h3>Conclusions</h3><div>Peritumoral information is significant to predict STAS status. The nomogram based on the extended tumor model and clinical independent risk factors provided good preoperative prediction of STAS status in LUAD with diameter ≤ 3 cm, aiding surgical decision-making.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100630"},"PeriodicalIF":1.8,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11754163/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143029858","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":"Potential of spectral imaging generated by contrast-enhanced dual-energy CT for lung cancer histopathological classification – A preliminary study","authors":"Tomoaki Sasaki ,&nbsp;Shioto Oda ,&nbsp;Hirofumi Kuno ,&nbsp;Takashi Hiyama ,&nbsp;Tetsuro Taki ,&nbsp;Shugo Takahashi ,&nbsp;Genichiro Ishii ,&nbsp;Masahiro Tsuboi ,&nbsp;Tatsushi Kobayashi","doi":"10.1016/j.ejro.2024.100628","DOIUrl":"10.1016/j.ejro.2024.100628","url":null,"abstract":"<div><h3>Purpose</h3><div>The potential of spectral images, particularly electron density and effective Z-images, generated by dual-energy computed tomography (DECT), for the histopathologic classification of lung cancer remains unclear. This study aimed to explore which imaging factors could better reflect the histopathological status of lung cancer.</div></div><div><h3>Method</h3><div>The data of 31 patients who underwent rapid kV-switching DECT and subsequently underwent surgery for lung cancer were analyzed. Virtual monochromatic images (VMIs) of 35 keV and 70 keV, virtual non-contrast images (VNC), iodine content images, electron density images, and effective Z-images were reconstructed for the following analyses: 1) correlation with the ratio of the lepidic growth pattern in the whole tumor and 2) comparisons with the four histological groups: well-differentiated adenocarcinoma (WDA), moderately differentiated adenocarcinoma (MDA), and poorly differentiated adenocarcinoma (PDA) and squamous cell carcinoma (SCC).</div></div><div><h3>Results</h3><div>There were significant correlations between the ratio of lepidic growth pattern and 70 keV, 35 keV, VNC, and electron density images (r = -0.861, P &lt; 0.001; r = -0.791, P &lt; 0.001; r = -0.869, P &lt; 0.001; r = -0.871, P &lt; 0.001, respectively). There were significant differences in the 70 keV, 35 keV, VNC, and electron density images in the Kruskal-Wallis test (P = 0.001, P = 0.006, P &lt; 0.001, P &lt; 0.001, respectively). However, there were no significant differences in iodine content or effective Z-images.</div></div><div><h3>Conclusions</h3><div>Electron density images generated by spectral imaging may be better indicators of the histopathological classification of lung cancer.</div></div><div><h3>Clinical relevance</h3><div>Electron density images may have an added value in predicting the histopathological classification of lung cancer.</div></div><div><h3>Key points</h3><div><ul><li><span>•</span><span><div>The role of electron density and effective Z-images obtained using dual-energy CT in lung cancer classification remains unclear.</div></span></li></ul><ul><li><span>•</span><span><div>Electron density and virtual non-contrast images correlated better with the ratio of lepidic growth patterns in lung cancer.</div></span></li></ul></div><div><ul><li><span>•</span><span><div>Electron density imaging is a better indicator of the histopathological classification of lung cancer than effective Z-imaging.</div></span></li></ul></div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100628"},"PeriodicalIF":1.8,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11732575/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985023","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":"Enhancing detection of previously missed non-palpable breast carcinomas through artificial intelligence","authors":"Sahar Mansour ,&nbsp;Rasha Kamal ,&nbsp;Samar Ahmed Hussein ,&nbsp;Mostafa Emara ,&nbsp;Yomna Kassab ,&nbsp;Sherif Nasser Taha ,&nbsp;Mohammed Mohammed Mohammed Gomaa","doi":"10.1016/j.ejro.2024.100629","DOIUrl":"10.1016/j.ejro.2024.100629","url":null,"abstract":"<div><h3>Purpose</h3><div>To investigate the impact of artificial intelligence (AI) reading digital mammograms in increasing the chance of detecting missed breast cancer, by studying the AI- flagged early morphology indictors, overlooked by the radiologist, and correlating them with the missed cancer pathology types.</div></div><div><h3>Methods and materials</h3><div>Mammograms done in 2020–2023, presenting breast carcinomas (n = 1998), were analyzed in concordance with the prior one year’s result (2019–2022) assumed negative or benign. Present mammograms reviewed for the descriptors: asymmetry, distortion, mass, and microcalcifications. The AI presented abnormalities by overlaying color hue and scoring percentage for the degree of suspicion of malignancy.</div></div><div><h3>Results</h3><div>Prior mammogram with AI marking compromised 54 % (n = 555), and in the present mammograms, AI targeted 904 (88 %) carcinomas. The descriptor proportion of “asymmetry” was the common presentation of missed breast carcinoma (64.1 %) in the prior mammograms and the highest detection rate for AI was presented by “distortion” (100 %) followed by “grouped microcalcifications” (80 %). AI performance to predict malignancy in previously assigned negative or benign mammograms showed sensitivity of 73.4 %, specificity of 89 %, and accuracy of 78.4 %.</div></div><div><h3>Conclusions</h3><div>Reading mammograms with AI significantly enhances the detection of early cancerous changes, particularly in dense breast tissues. The AI's detection rate does not correlate with specific pathological types of breast cancer, highlighting its broad utility. Subtle mammographic changes in postmenopausal women, not corroborated by ultrasound but marked by AI, warrant further evaluation by advanced applications of digital mammograms and close interval AI-reading mammogram follow up to minimize the potential for missed breast carcinoma.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100629"},"PeriodicalIF":1.8,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11732583/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985004","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":"Peroneus brevis split tear – A challenging diagnosis: A pictorial review of magnetic resonance and ultrasound imaging – Part 2: Imaging with magnetic resonance and ultrasound","authors":"Katarzyna Bokwa-Dąbrowska ,&nbsp;Dan Mocanu ,&nbsp;Isaac Romanus ,&nbsp;Rafał Zych ,&nbsp;Michael Huuskonen ,&nbsp;Pawel Szaro","doi":"10.1016/j.ejro.2024.100627","DOIUrl":"10.1016/j.ejro.2024.100627","url":null,"abstract":"<div><div>Peroneal tendon pathology is common among physically active individuals, with tenosynovitis, tendon subluxation, split tears and rupture. However, diagnosing these conditions, particularly peroneus brevis split tears, is clinically and radiologically challenging. Magnetic resonance imaging (MRI) and ultrasound (US) can sometimes miss split tears. A significant portion of peroneus split tears develops on a background of tendinopathy. The presence of tenosynovitis, changes in tendon shape, and multiple subtendons can indicate a complete multifragmenting split tear. A defect on the surface of the tendon may indicate a partial-thickness split tear, commonly referred to as the \"cleft sign.\" Peroneus subluxation is particularly likely when the superior peroneal retinaculum is torn. Given the subtlety of clinical symptoms, radiological evaluation is essential. Dynamic US assessment is especially valuable for detecting instability and split tears. This pictorial review presents the imaging spectrum of the most common pathologies of the peroneus brevis tendon on US and MRI.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100627"},"PeriodicalIF":1.8,"publicationDate":"2024-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11733185/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143013312","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":"Rare pancreatic cystic neoplasms: A pictorial review","authors":"Francescamaria Donati,&nbsp;Rosa Cervelli,&nbsp;Piero Boraschi","doi":"10.1016/j.ejro.2024.100620","DOIUrl":"10.1016/j.ejro.2024.100620","url":null,"abstract":"<div><div>Since rare pancreatic cystic tumors may differ from common pancreatic cystic neoplasms in terms of treatment plan and prognosis, the differential diagnosis of these diseases is clinically relevant. Various imaging tests play an important role in the differential diagnosis of rare cystic pancreatic tumors, but accurately distinguishing these diseases solely on the basis of imaging findings is challenging. The purpose of this pictorial review is to present CT and in particular MR imaging features of rare pancreatic cystic tumors and discuss potential elements for differential diagnosis.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100620"},"PeriodicalIF":1.8,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11730956/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985029","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":"Differences in myocardial involvement between new onset and longstanding systemic lupus erythematosus patients assessed by cardiovascular magnetic resonance","authors":"Zhen Wang ,&nbsp;Xing Tang ,&nbsp;Chaohui Hang ,&nbsp;Hui Gao ,&nbsp;Jinxiu Yang ,&nbsp;Yuchi Han ,&nbsp;Yongqiang Yu ,&nbsp;Zongwen Shuai ,&nbsp;Ren Zhao ,&nbsp;Xiaohu Li","doi":"10.1016/j.ejro.2024.100623","DOIUrl":"10.1016/j.ejro.2024.100623","url":null,"abstract":"<div><h3>Objectives</h3><div>Subclinical myocardial involvement is common in systemic lupus erythematosus (SLE), but differences between new onset and longstanding SLE are not fully elucidated. This study compared myocardial involvement in new onset versus longstanding SLE using cardiovascular magnetic resonance (CMR).</div></div><div><h3>Materials and methods</h3><div>We prospectively enrolled 24 drug-naïve new onset SLE patients, 27 longstanding SLE patients, and 20 healthy controls. All participants underwent clinical evaluation and CMR examination. We analyzed left ventricular (LV) morphological, functional parameters, and tissue characterization parameters: native T1, T2, extracellular volume fraction (ECV), and late gadolinium enhancement (LGE).</div></div><div><h3>Results</h3><div>Both new onset and longstanding SLE groups showed elevated native T1, T2, and ECV values compared to the control group (all P &lt; 0.05). Additionally, the new onset SLE group exhibited higher T2 values compared to the longstanding SLE group [55.3 vs. 52.8 ms, P &lt; 0.05]. The new onset group also demonstrated higher left ventricular (LV) end-diastolic volume index (LVEDVi), LV end-systolic volume index (LVSVi), and LV mass index (LVMi) than controls (all P &lt; 0.05), with LVEDVi significantly higher than in the longstanding group (P &lt; 0.05).</div></div><div><h3>Conclusion</h3><div>CMR tissue characterization imaging can detect early myocardial involvement in patients with new onset and longstanding SLE. Patients with new onset SLE exhibit more pronounced myocardial edema than those with longstanding SLE. This suggests that SLE patients are at risk of myocardial damage at various stages of the disease, underscoring the need for early monitoring and long-term management to prevent the progression of myocardial remodeling.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100623"},"PeriodicalIF":1.8,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11731982/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985000","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":"Deep learning radiomics analysis for prediction of survival in patients with unresectable gastric cancer receiving immunotherapy","authors":"Miaomiao Gou ,&nbsp;Hongtao Zhang ,&nbsp;Niansong Qian ,&nbsp;Yong Zhang ,&nbsp;Zeyu Sun ,&nbsp;Guang Li ,&nbsp;Zhikuan Wang ,&nbsp;Guanghai Dai","doi":"10.1016/j.ejro.2024.100626","DOIUrl":"10.1016/j.ejro.2024.100626","url":null,"abstract":"<div><h3>Objective</h3><div>Immunotherapy has become an option for the first-line therapy of advanced gastric cancer (GC), with improved survival. Our study aimed to investigate unresectable GC from an imaging perspective combined with clinicopathological variables to identify patients who were most likely to benefit from immunotherapy.</div></div><div><h3>Method</h3><div>Patients with unresectable GC who were consecutively treated with immunotherapy at two different medical centers of Chinese PLA General Hospital were included and divided into the training and validation cohorts, respectively. A deep learning neural network, using a multimodal ensemble approach based on CT imaging data before immunotherapy, was trained in the training cohort to predict survival, and an internal validation cohort was constructed to select the optimal ensemble model. Data from another cohort were used for external validation. The area under the receiver operating characteristic curve was analyzed to evaluate performance in predicting survival. Detailed clinicopathological data and peripheral blood prior to immunotherapy were collected for each patient. Univariate and multivariable logistic regression analysis of imaging models and clinicopathological variables was also applied to identify the independent predictors of survival. A nomogram based on multivariable logistic regression was constructed.</div></div><div><h3>Result</h3><div>A total of 79 GC patients in the training cohort and 97 patients in the external validation cohort were enrolled in this study. A multi-model ensemble approach was applied to train a model to predict the 1-year survival of GC patients. Compared to individual models, the ensemble model showed improvement in performance metrics in both the internal and external validation cohorts. There was a significant difference in overall survival (OS) among patients with different imaging models based on the optimum cutoff score of 0.5 (HR = 0.20, 95 % CI: 0.10–0.37, <em>P</em> &lt; 0.001). Multivariate Cox regression analysis revealed that the imaging models, PD-L1 expression, and lung immune prognostic index were independent prognostic factors for OS. We combined these variables and built a nomogram. The calibration curves showed that the C-index of the nomogram was 0.85 and 0.78 in the training and validation cohorts.</div></div><div><h3>Conclusion</h3><div>The deep learning model in combination with several clinical factors showed predictive value for survival in patients with unresectable GC receiving immunotherapy.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100626"},"PeriodicalIF":1.8,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11728962/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142979626","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":"The predictive power of baseline metabolic and volumetric [18F]FDG PET parameters with different thresholds for early therapy failure and mortality risk in DLBCL patients undergoing CAR-T-cell therapy","authors":"Emil Novruzov ,&nbsp;Helena A. Peters ,&nbsp;Kai Jannusch ,&nbsp;Guido Kobbe ,&nbsp;Sascha Dietrich ,&nbsp;Johannes C. Fischer ,&nbsp;Jutta Rox ,&nbsp;Gerald Antoch ,&nbsp;Frederik L. Giesel ,&nbsp;Christina Antke ,&nbsp;Ben-Niklas Baermann ,&nbsp;Eduards Mamlins","doi":"10.1016/j.ejro.2024.100619","DOIUrl":"10.1016/j.ejro.2024.100619","url":null,"abstract":"<div><h3>Objective</h3><div>[¹⁸F]FDG imaging is an integral part of patient management in CAR-T-cell therapy for recurrent or therapy-refractory DLBCL. The calculation methods of predictive power of specific imaging parameters still remains elusive. With this retrospective study, we sought to evaluate the predictive power of the baseline metabolic parameters and tumor burden calculated with automated segmentation via different thresholding methods for early therapy failure and mortality risk in DLBCL patients.</div></div><div><h3>Materials and methods</h3><div>Eighteen adult patients were enrolled, who underwent CAR-T-cell therapy accompanied by at least one pretherapeutic and two posttherapeutic [¹⁸F]FDG PET scans within 30 and 90 days between December 2018 and October 2023. We performed single-click automatic segmentation within VOIs in addition to extracting the SUV parameters to calculate the MTVs and TLGs by applying thresholds based on the concepts of a fixed absolute threshold with an SUV_max &gt; 4.0, a relative absolute threshold with an isocontour of &gt; 40 % of the SUV_max, a background threshold involving the addition of the liver SUV value and its 2 SD values, and only the liver SUV value.</div></div><div><h3>Results</h3><div>For early therapy failure, baseline metabolic parameters such as the SUV_max, SUV_peak and SUV_mean tended to have greater predictive power than did the baseline metabolic burden. However, the baseline metabolic burden was superior in the prediction of mortality risk regardless of the thresholding method used.</div></div><div><h3>Conclusion</h3><div>This study revealed that automated delineation methods of metabolic tumor burden using different thresholds do not differ in outcome substantially. Therefore, the current clinical standard with a fixed absolute threshold value of SUV &gt; 4.0 seems to be a feasible option.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100619"},"PeriodicalIF":1.8,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11719856/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142972521","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":"Preliminary study on detection and diagnosis of focal liver lesions based on a deep learning model using multimodal PET/CT images","authors":"Yingqi Luo ,&nbsp;Qingqi Yang ,&nbsp;Jinglang Hu ,&nbsp;Xiaowen Qin ,&nbsp;Shengnan Jiang ,&nbsp;Ying Liu","doi":"10.1016/j.ejro.2024.100624","DOIUrl":"10.1016/j.ejro.2024.100624","url":null,"abstract":"<div><h3>Objectives</h3><div>To develop and validate a deep learning model using multimodal PET/CT imaging for detecting and classifying focal liver lesions (FLL).</div></div><div><h3>Methods</h3><div>This study included 185 patients who underwent ¹⁸F-FDG PET/CT imaging at our institution from March 2022 to February 2023. We analyzed serological data and imaging. Liver lesions were segmented on PET and CT, serving as the \"reference standard\". Deep learning models were trained using PET and CT images to generate predicted segmentations and classify lesion nature. Model performance was evaluated by comparing the predicted segmentations with the reference segmentations, using metrics such as Dice, Precision, Recall, F1-score, ROC, and AUC, and compared it with physician diagnoses.</div></div><div><h3>Results</h3><div>This study finally included 150 patients, comprising 46 patients with benign liver nodules, 51 patients with malignant liver nodules, and 53 patients with no FLLs. Significant differences were observed among groups for age, AST, ALP, GGT, AFP, CA19–9and CEA. On the validation set, the Dice coefficient of the model was 0.740. For the normal group, the recall was 0.918, precision was 0.904, F1-score was 0.909, and AUC was 0.976. For the benign group, the recall was 0.869, precision was 0.862, F1-score was 0.863, and AUC was 0.928. For the malignant group, the recall was 0.858, precision was 0.914, F1-score was 0.883, and AUC was 0.979. The model's overall diagnostic performance was between that of junior and senior physician.</div></div><div><h3>Conclusion</h3><div>This deep learning model demonstrated high sensitivity in detecting FLLs and effectively differentiated between benign and malignant lesions.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100624"},"PeriodicalIF":1.8,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11720101/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142972520","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}