Cancer Imaging最新文献

{"title":"CT-based conventional radiomics and quantification of intratumoral heterogeneity for predicting benign and malignant renal lesions.","authors":"Shuanbao Yu, Yang Yang, Zeyuan Wang, Haoke Zheng, Jinshan Cui, Yonghao Zhan, Junxiao Liu, Peng Li, Yafeng Fan, Wendong Jia, Meng Wang, Bo Chen, Jin Tao, Yuhong Li, Xuepei Zhang","doi":"10.1186/s40644-024-00775-8","DOIUrl":"10.1186/s40644-024-00775-8","url":null,"abstract":"<p><strong>Background: </strong>With the increasing incidence of renal lesions, pretreatment differentiation between benign and malignant lesions is crucial for optimized management. This study aimed to develop a machine learning model utilizing radiomic features extracted from various regions of interest (ROIs), intratumoral ecological diversity features, and clinical factors to classify renal lesions.</p><p><strong>Methods: </strong>CT images (arterial phase) of 1,795 renal lesions with confirmed pathology from three hospital sites were split into development (1184, 66%) and test (611, 34%) cohorts by surgery date. Conventional radiomic features were extracted from eight ROIs of arterial phase images. Intratumoral ecological diversity features were derived from intratumoral subregions. The combined model incorporating these features with clinical factors was developed, and its performance was compared with radiologists' interpretation.</p><p><strong>Results: </strong>Combining intratumoral and peritumoral radiomic features, along with ecological diversity features yielded the highest AUC of 0.929 among all combinations of features extracted from CT scans. After incorporating clinical factors into the features extracted from CT images, our combined model outperformed the interpretation of radiologists in the whole (AUC = 0.946 vs 0.823, P < 0.001) and small renal lesion (AUC = 0.935 vs 0.745, P < 0.001) test cohorts. Furthermore, the combined model exhibited favorable concordance and provided the highest net benefit across threshold probabilities exceeding 60%. In the whole and small renal lesion test cohorts, the AUCs for subgroups with predicted risk below or above 95% sensitivity and specificity cutoffs were 0.974 and 0.978, respectively.</p><p><strong>Conclusions: </strong>The combined model, incorporating intratumoral and peritumoral radiomic features, ecological diversity features, and clinical factors showed good performance for distinguishing benign from malignant renal lesions, surpassing radiologists' diagnoses in both whole and small renal lesions. It has the potential to save patients from unnecessary invasive biopsies/surgeries and to enhance clinical decision-making.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"130"},"PeriodicalIF":3.5,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11446113/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142364517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalized prediction of immunotherapy response in lung cancer patients using advanced radiomics and deep learning.","authors":"Chien-Yi Liao, Yuh-Min Chen, Yu-Te Wu, Heng-Sheng Chao, Hwa-Yen Chiu, Ting-Wei Wang, Jyun-Ru Chen, Tsu-Hui Shiao, Chia-Feng Lu","doi":"10.1186/s40644-024-00779-4","DOIUrl":"10.1186/s40644-024-00779-4","url":null,"abstract":"<p><strong>Background: </strong>Lung cancer (LC) is a leading cause of cancer-related mortality, and immunotherapy (IO) has shown promise in treating advanced-stage LC. However, identifying patients likely to benefit from IO and monitoring treatment response remains challenging. This study aims to develop a predictive model for progression-free survival (PFS) in LC patients with IO based on clinical features and advanced imaging biomarkers.</p><p><strong>Materials and methods: </strong>A retrospective analysis was conducted on a cohort of 206 LC patients receiving IO treatment. Pre-treatment computed tomography images were used to extract advanced imaging biomarkers, including intratumoral and peritumoral-vasculature radiomics. Clinical features, including age, gene status, hematology, and staging, were also collected. Key radiomic and clinical features for predicting IO outcomes were identified using a two-step feature selection process, including univariate Cox regression and chi-squared test, followed by sequential forward selection. The DeepSurv model was constructed to predict PFS based on clinical and radiomic features. Model performance was evaluated using the area under the time-dependent receiver operating characteristic curve (AUC) and concordance index (C-index).</p><p><strong>Results: </strong>Combining radiomics of intratumoral heterogeneity and peritumoral-vasculature with clinical features demonstrated a significant enhancement (p < 0.001) in predicting IO response. The proposed DeepSurv model exhibited a prediction performance with AUCs ranging from 0.76 to 0.80 and a C-index of 0.83. Furthermore, the predicted personalized PFS curves revealed a significant difference (p < 0.05) between patients with favorable and unfavorable prognoses.</p><p><strong>Conclusions: </strong>Integrating intratumoral and peritumoral-vasculature radiomics with clinical features enabled the development of a predictive model for PFS in LC patients with IO. The proposed model's capability to estimate individualized PFS probability and differentiate the prognosis status held promise to facilitate personalized medicine and improve patient outcomes in LC.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"129"},"PeriodicalIF":3.5,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11440728/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142342218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: The utility of ¹⁸F-FDG PET/CT for predicting the pathological response and prognosis to neoadjuvant immunochemotherapy in resectable non-small-cell lung cancer.","authors":"Rui Guo, Wanpu Yan, Fei Wang, Hua Su, Xiangxi Meng, Qing Xie, Wei Zhao, Zhi Yang, Nan Li","doi":"10.1186/s40644-024-00777-6","DOIUrl":"https://doi.org/10.1186/s40644-024-00777-6","url":null,"abstract":"","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"128"},"PeriodicalIF":3.5,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11425872/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142342217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring innovative strides in radiolabeled nanoparticle progress for multimodality cancer imaging and theranostic applications.","authors":"Atena Najdian, Davood Beiki, Milad Abbasi, Ali Gholamrezanezhad, Hojjat Ahmadzadehfar, Ali Mohammad Amani, Mehdi Shafiee Ardestani, Majid Assadi","doi":"10.1186/s40644-024-00762-z","DOIUrl":"https://doi.org/10.1186/s40644-024-00762-z","url":null,"abstract":"<p><p>Multimodal imaging unfolds as an innovative approach that synergistically employs a spectrum of imaging techniques either simultaneously or sequentially. The integration of computed tomography (CT), magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), positron emission tomography (PET), and optical imaging (OI) results in a comprehensive and complementary understanding of complex biological processes. This innovative approach combines the strengths of each method and overcoming their individual limitations. By harmoniously blending data from these modalities, it significantly improves the accuracy of cancer diagnosis and aids in treatment decision-making processes. Nanoparticles possess a high potential for facile functionalization with radioactive isotopes and a wide array of contrast agents. This strategic modification serves to augment signal amplification, significantly enhance image sensitivity, and elevate contrast indices. Such tailored nanoparticles constructs exhibit a promising avenue for advancing imaging modalities in both preclinical and clinical setting. Furthermore, nanoparticles function as a unified nanoplatform for the co-localization of imaging agents and therapeutic payloads, thereby optimizing the efficiency of cancer management strategies. Consequently, radiolabeled nanoparticles exhibit substantial potential in driving forward the realms of multimodal imaging and theranostic applications. This review discusses the potential applications of molecular imaging in cancer diagnosis, the utilization of nanotechnology-based radiolabeled materials in multimodal imaging and theranostic applications, as well as recent advancements in this field. It also highlights challenges including cytotoxicity and regulatory compliance, essential considerations for effective clinical translation of nanoradiopharmaceuticals in multimodal imaging and theranostic applications.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"127"},"PeriodicalIF":3.5,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11416024/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142280554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early treatment response assessment with [177Lu]PSMA whole-body-scintigraphy compared to interim PSMA-PET","authors":"David Ventura, Philipp Rassek, Philipp Schindler, Burak Han Akkurt, Linus Bredensteiner, Martin Bögemann, Katrin Schlack, Robert Seifert, Michael Schäfers, Wolfgang Roll, Kambiz Rahbar","doi":"10.1186/s40644-024-00773-w","DOIUrl":"https://doi.org/10.1186/s40644-024-00773-w","url":null,"abstract":"Prostate-specific membrane antigen positron emission tomography (PSMA-PET) is an essential tool for patient selection before radioligand therapy (RLT). Interim-staging with PSMA-PET during RLT allows for therapy monitoring. However, its added value over post-treatment imaging is poorly elucidated. The aim of this study was to compare early treatment response assessed by post-therapeutic whole-body scans (WBS) with interim-staging by PSMA-PET after 2 cycles in order to prognosticate OS. Men with metastasized castration-resistant PC (mCRPC) who had received at least two cycles of RLT, and interim PSMA-PET were evaluated retrospectively. PROMISE V2 framework was used to categorize PSMA expression and assess response to treatment. Response was defined as either disease control rate (DCR) for responders or progression for non-responders. A total of 188 men with mCRPC who underwent RLT between February 2015 and December 2021 were included. The comparison of different imaging modalities revealed a strong and significant correlation with Cramer V test: e.g. response on WBS during second cycle compared to interim PET after two cycles of RLT (cφ = 0.888, P < 0.001, n = 188). The median follow-up time was 14.7 months (range: 3–63 months; 125 deaths occurred). Median overall survival (OS) time was 14.5 months (95% CI: 11.9–15.9). In terms of OS analysis, early progression during therapy revealed a significantly higher likelihood of death: e.g. second cycle WBS (15 vs. 25 months, P < 0.001) with a HR of 2.81 (P < 0.001) or at PET timepoint after 2 cycles of RLT (11 vs. 24 months, P < 0.001) with a HR of 3.5 (P < 0.001). For early biochemical response, a PSA decline of at least 50% after two cycles of RLT indicates a significantly lower likelihood of death (26 vs. 17 months, P < 0.001) with a HR of 0.5 (P < 0.001). Response assessment of RLT by WBS and interim PET after two cycles of RLT have high congruence and can identify patients at risk of poor outcome. This indicates that interim PET might be omitted for response assessment, but future trials corroborating these findings are warranted.","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"2 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Immune-related [18F]FDG PET findings in patients undergoing checkpoint inhibitors treatment: correlation with clinical adverse events and prognostic implications","authors":"Giulia Santo, Maria Cucè, Antonino Restuccia, Teresa Del Giudice, Pierfrancesco Tassone, Francesco Cicone, Pierosandro Tagliaferri, Giuseppe Lucio Cascini","doi":"10.1186/s40644-024-00774-9","DOIUrl":"https://doi.org/10.1186/s40644-024-00774-9","url":null,"abstract":"Direct comparisons between [18F]FDG PET/CT findings and clinical occurrence of immune-related adverse events (irAEs) based on independent assessments of clinical and imaging features in patients receiving immune checkpoint inhibitors (ICIs) are missing. Our aim was to estimate sites, frequency, and timing of immune-related PET findings during ICIs treatment in patients with melanoma and NSCLC, and to assess their correlation with clinical irAEs. Prognostic implications of immune-related events were also investigated. Fifty-one patients with melanoma (47%) or NSCLC (53%) undergoing multiple PET examinations during anti-PD1/PDL1 treatment were retrospectively included. Clinical irAEs were graded according to CTCAE v.5.0. Abnormal PET findings suggestive of immune activation were described by two readers blinded to the clinical data. Progression-free survival (PFS) and overall survival (OS) were analyzed with the Kaplan-Meier method in patients stratified according to the presence of irAEs, immune-related PET findings or both. Twenty-one patients showed clinical irAEs only (n = 6), immune-related PET findings only (n = 6), or both (n = 9). In patients whose imaging findings corresponded to clinical irAEs (n = 7), a positive correlation between SUVmax and the severity of the clinical event was observed (rs=0.763, p = 0.046). Clinical irAEs occurred more frequently in patients without macroscopic disease than in metastatic patients (55% vs. 23%, p = 0.039). Patients who developed clinical irAEs had a significantly longer PFS than patients who remained clinically asymptomatic, both in the overall cohort (p = 0.011) and in the subgroup of (n = 35) patients with metastatic disease (p = 0.019). The occurrence of immune-related PET findings significantly stratified PFS in the overall cohort (p = 0.040), and slightly missed statistical significance in patients with metastatic disease (p = 0.08). The best stratification of PFS was achieved when all patients who developed immune-related events, either clinically relevant or detected by PET only, were grouped together both in the overall cohort (p = 0.002) and in patients with metastatic disease (p = 0.004). In the whole sample, OS was longer in patients who developed any immune-related events (p = 0.032). Patients with melanoma or NSCLC under ICI treatment can develop clinical irAEs, immune-related PET findings, or both. The occurrence of immune-related events has a prognostic impact. Combining clinical information with PET assessment improved outcome stratification.","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"208 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiomics predicts the prognosis of patients with clear cell renal cell carcinoma by reflecting the tumor heterogeneity and microenvironment","authors":"Ji Wu, Jian Li, Bo Huang, Sunbin Dong, Luyang Wu, Xiping Shen, Zhigang Zheng","doi":"10.1186/s40644-024-00768-7","DOIUrl":"https://doi.org/10.1186/s40644-024-00768-7","url":null,"abstract":"We aimed to develop and externally validate a CT-based deep learning radiomics model for predicting overall survival (OS) in clear cell renal cell carcinoma (ccRCC) patients, and investigate the association of radiomics with tumor heterogeneity and microenvironment. The clinicopathological data and contrast-enhanced CT images of 512 ccRCC patients from three institutions were collected. A total of 3566 deep learning radiomics features were extracted from 3D regions of interest. We generated the deep learning radiomics score (DLRS), and validated this score using an external cohort from TCIA. Patients were divided into high and low-score groups by the DLRS. Sequencing data from the corresponding TCGA cohort were used to reveal the differences of tumor heterogeneity and microenvironment between different radiomics score groups. What’s more, univariate and multivariate Cox regression were used to identify independent risk factors of poor OS after operation. A combined model was developed by incorporating the DLRS and clinicopathological features. The SHapley Additive exPlanation method was used for interpretation of predictive results. At multivariate Cox regression analysis, the DLRS was identified as an independent risk factor of poor OS. The genomic landscape of different radiomics score groups was investigated. The heterogeneity of tumor cell and tumor microenvironment significantly varied between both groups. In the test cohort, the combined model had a great predictive performance, with AUCs (95%CI) for 1, 3 and 5-year OS of 0.879(0.868–0.931), 0.854(0.819–0.899) and 0.831(0.813–0.868), respectively. There was a significant difference in survival time between different groups stratified by the combined model. This model showed great discrimination and calibration, outperforming the existing prognostic models (all p values < 0.05). The combined model allowed for the prognostic prediction of ccRCC patients by incorporating the DLRS and significant clinicopathologic features. The radiomics features could reflect the tumor heterogeneity and microenvironment.","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"30 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of tube voltage, radiation dose and adaptive statistical iterative reconstruction strength level on the detection and characterization of pulmonary nodules in ultra-low-dose chest CT","authors":"Yue Yao, Xuan Su, Lei Deng, JingBin Zhang, Zengmiao Xu, Jianying Li, Xiaohui Li","doi":"10.1186/s40644-024-00770-z","DOIUrl":"https://doi.org/10.1186/s40644-024-00770-z","url":null,"abstract":"To explore the effects of tube voltage, radiation dose and adaptive statistical iterative reconstruction (ASiR-V) strength level on the detection and characterization of pulmonary nodules by an artificial intelligence (AI) software in ultra-low-dose chest CT (ULDCT). An anthropomorphic thorax phantom containing 12 spherical simulated nodules (Diameter: 12 mm, 10 mm, 8 mm, 5 mm; CT value: -800HU, -630HU, 100HU) was scanned with three ULDCT protocols: Dose-1 (70kVp:0.11mSv, 100kVp:0.10mSv), Dose-2 (70kVp:0.34mSv, 100kVp:0.32mSv), Dose-3 (70kVp:0.53mSv, 100kVp:0.51mSv). All scanning protocols were repeated five times. CT images were reconstructed using four different strength levels of ASiR-V (0%=FBP, 30%, 50%, 70%ASiR-V) with a slice thickness of 1.25 mm. The characteristics of the physical nodules were used as reference standards. All images were analyzed using a commercially available AI software to identify nodules for calculating nodule detection rate (DR) and to obtain their long diameter and short diameter, which were used to calculate the deformation coefficient (DC) and size measurement deviation percentage (SP) of nodules. DR, DC and SP of different imaging groups were statistically compared. Image noise decreased with the increase of ASiR-V strength level, and the 70 kV images had lower noise under the same strength level (mean-value 70 kV: 40.14 ± 7.05 (dose 1), 27.55 ± 7.38 (dose 2), 23.88 ± 6.98 (dose 3); 100 kV: 42.36 ± 7.62 (dose 1); 30.78 ± 6.87 (dose 2); 26.49 ± 6.61 (dose 3)). Under the same dose level, there were no differences in DR between 70 kV and 100 kV (dose 1: 58.76% vs. 58.33%; dose 2: 73.33% vs. 70.83%; dose 3: 75.42% vs. 75.42%, all p > 0.05). The DR of GGNs increased significantly at dose 2 and higher (70 kV: 38.12% (dose 1), 60.63% (dose 2), 64.38% (dose 3); 100 kV: 37.50% (dose 1), 59.38% (dose 2), 66.25% (dose 3)). In general, the use of ASiR-V at higher strength levels (> 50%) and 100 kV provided better (lower) DC and SP. Detection rates are similar between 70 kV and 100 kV scans. The 70 kV images have better noise performance under the same ASiR-V level, while images of 100 kV and higher ASiR-V levels are better in preserving the nodule morphology (lower DC and SP); the dose levels above 0.33mSv provide high sensitivity for nodules detection, especially the simulated ground glass nodules.","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"21 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of axillary lymph node metastasis using a magnetic resonance imaging radiomics model of invasive breast cancer primary tumor","authors":"Wei Shi, Yingshi Su, Rui Zhang, Wei Xia, Zhenqiang Lian, Ning Mao, Yanyu Wang, Anqin Zhang, Xin Gao, Yan Zhang","doi":"10.1186/s40644-024-00771-y","DOIUrl":"https://doi.org/10.1186/s40644-024-00771-y","url":null,"abstract":"This study investigated the clinical value of breast magnetic resonance imaging (MRI) radiomics for predicting axillary lymph node metastasis (ALNM) and to compare the discriminative abilities of different combinations of MRI sequences. This study included 141 patients diagnosed with invasive breast cancer from two centers (center 1: n = 101, center 2: n = 40). Patients from center 1 were randomly divided into training set and test set 1. Patients from center 2 were assigned to the test set 2. All participants underwent preoperative MRI, and four distinct MRI sequences were obtained. The volume of interest (VOI) of the breast tumor was delineated on the dynamic contrast-enhanced (DCE) postcontrast phase 2 sequence, and the VOIs of other sequences were adjusted when required. Subsequently, radiomics features were extracted from the VOIs using an open-source package. Both single- and multisequence radiomics models were constructed using the logistic regression method in the training set. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and precision of the radiomics model for the test set 1 and test set 2 were calculated. Finally, the diagnostic performance of each model was compared with the diagnostic level of junior and senior radiologists. The single-sequence ALNM classifier derived from DCE postcontrast phase 1 had the best performance for both test set 1 (AUC = 0.891) and test set 2 (AUC = 0.619). The best-performing multisequence ALNM classifiers for both test set 1 (AUC = 0.910) and test set 2 (AUC = 0.717) were generated from DCE postcontrast phase 1, T2-weighted imaging, and diffusion-weighted imaging single-sequence ALNM classifiers. Both had a higher diagnostic level than the junior and senior radiologists. The combination of DCE postcontrast phase 1, T2-weighted imaging, and diffusion-weighted imaging radiomics features had the best performance in predicting ALNM from breast cancer. Our study presents a well-performing and noninvasive tool for ALNM prediction in patients with breast cancer.","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"36 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142185434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proceedings of ICIS SGCR-WIRES 2024, held jointly with the 23rd International Cancer Imaging Society Annual Conference, collaborating with the Singapore Radiological Society and College of Radiologists Singapore","authors":"","doi":"10.1186/s40644-024-00751-2","DOIUrl":"https://doi.org/10.1186/s40644-024-00751-2","url":null,"abstract":"&lt;h3&gt;O1 A randomized controlled trial of preoperative prostate artery embolization before transurethral resection of prostate glands larger than 80cc&lt;/h3&gt;&lt;h4&gt;Zong Yi Chin&lt;sup&gt;1&lt;/sup&gt;, Alvin YM Lee&lt;sup&gt;2&lt;/sup&gt;, Neo Shu Hui&lt;sup&gt;3&lt;/sup&gt;, Ng Tze Kiat&lt;sup&gt;2&lt;/sup&gt;, Edwin Jonathan Aslim&lt;sup&gt;2&lt;/sup&gt;, Allen SP Sim&lt;sup&gt;4&lt;/sup&gt;, Pradesh Kumar&lt;sup&gt;5&lt;/sup&gt;, John SP Yuen&lt;sup&gt;2&lt;/sup&gt;, Kenneth Chen&lt;sup&gt;2&lt;/sup&gt;, Sivanathan Chandramohan&lt;sup&gt;1&lt;/sup&gt;\u0000&lt;/h4&gt;&lt;h5&gt;\u0000&lt;sup&gt;1&lt;/sup&gt;Vascular and Interventional Radiology, Singapore General Hospital, Singapore; &lt;sup&gt;2&lt;/sup&gt;Urology, Singapore General Hospital, Singapore; &lt;sup&gt;3&lt;/sup&gt;Urology, Sengkang General Hospital, Singapore; &lt;sup&gt;4&lt;/sup&gt;Urology, Gleneagles Medini Johor, Malaysia; &lt;sup&gt;5&lt;/sup&gt;Radiology, Sunway Medical Centre, Malaysia&lt;/h5&gt;&lt;p&gt;\u0000&lt;i&gt;Cancer Imaging (2024)&lt;/i&gt;, &lt;b&gt;24 (1):&lt;/b&gt; O1&lt;/p&gt;&lt;br/&gt;&lt;p&gt;\u0000&lt;b&gt;Objectives/ Teaching Points:&lt;/b&gt;\u0000&lt;/p&gt;&lt;p&gt;To study the impact of preoperative prostate artery embolization (PAE) on intraoperative blood loss during transurethral resection of the prostate (TURP) for large prostates (exceeding 80 cc).&lt;/p&gt;&lt;p&gt;\u0000&lt;b&gt;Material(s) and Method(s):&lt;/b&gt;\u0000&lt;/p&gt;&lt;p&gt;A prospective, surgeon-blinded, randomized controlled trial was performed at a single tertiary centre. Patients with prostate volumes over 80 cc who needed TURP were randomly allocated (1:1) to receive preoperative prostatic artery embolization followed by TURP (Group A—intervention) or TURP alone (Group B—control). The primary outcome measured the postoperative drop in haemoglobin levels, as a surrogate for blood loss. Secondary outcomes studied included resection efficiency (weight of resected tissue per minute) and the rate of postoperative complications.&lt;/p&gt;&lt;p&gt;\u0000&lt;b&gt;Results:&lt;/b&gt;\u0000&lt;/p&gt;&lt;p&gt;There were 10 patients in each group. The median prostate volumes were 119 mL for Group A and 140 mL for Group B, with median preoperative haemoglobin levels of 13.3 g/dL (IQR 12.5–14.3 g/dL) in Group A and 14.4 g/dL (IQR 10.1–15.2 g/dL) in Group B. The decrease in postoperative haemoglobin was significantly greater in Group B compared to Group A (-1.4 g/dL vs + 0.5 g/dL, p = 0.015). There were no significant differences between the groups in terms of the weight of resected prostate tissue (52 g vs 73 g, p = 0.089) and resection efficiency (0.7 g/min vs 0.6 g/min, p = 0.853). Two patients in Group B needed a red blood cell transfusion, compared to one patient in Group A (p = 1.000). One patient from each group required an additional surgery for haemostasis.&lt;/p&gt;&lt;p&gt;\u0000&lt;b&gt;Conclusions:&lt;/b&gt;\u0000&lt;/p&gt;&lt;p&gt;Preoperative PAE significantly decreased TURP blood loss in men with large prostates.&lt;/p&gt;&lt;h3&gt;O2 Improving AI Transparency Using an Uncertainty-inspired Classification Model for Chest Xray Diagnosis&lt;/h3&gt;&lt;h4&gt;Shu Wen Goh&lt;sup&gt;1&lt;/sup&gt;, Yangqin Feng&lt;sup&gt;2&lt;/sup&gt;, Xinxing Xu&lt;sup&gt;2&lt;/sup&gt;, Yong Liu&lt;sup&gt;2&lt;/sup&gt;, Cher Heng Tan&lt;sup&gt;1&lt;/sup&gt;\u0000&lt;/h4&gt;&lt;h5&gt;\u0000&lt;sup&gt;1&lt;/sup&gt;Diagnostic Radiology, Tan Tock Seng Hospital, Singapore; &lt;sup&gt;2&lt;/sup&gt;Institute of High-Performance Computing, A*STAR, Singapore&lt;/h5&gt;&lt;p&gt;\u0000","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"39 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142185435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}