Search

Objective: Hospital based follow-up has been the standard of care for endometrial cancer. Patient initiated follow-up is a useful adjunct for lower risk cancers. The purpose of this study was to evaluate outcomes of endometrial cancer patients after stratification into risk groupings, with particular attention to salvageable relapses., Methods: All patients treated surgically for International Federation of Gynecology and Obstetrics (FIGO) stage I-IVA endometrial cancer of all histological subtypes, from January 2009 until March 2019, were analyzed. Patient and tumor characteristics, treatment details, relapse, death, and last follow-up dates were collected. Site of relapse, presence of symptoms, and whether relapses were salvageable were also identified. The European Society of Medical Oncology-European Society of Gynecological Oncology 2020 risk stratification was assigned, and relapse free and overall survival were estimated., Results: 900 patients met the eligibility criteria. Median age was 66 years (range 28-96) and follow-up duration was 35 months (interquartile range 19-57). In total, 16% (n=144) of patients relapsed, 1.3% (n=12) from the low risk group, 3.9% (n=35) from the intermediate risk group, 2.2% (n=20) from the high-intermediate risk group, and 8.7% (n=77) from the high risk group. Salvageable relapses were less frequent at 2% (n=18), of which 33% (n=6) were from the low risk group, 22% (n=4) from the intermediate risk group, 11% (n=2) from the high-intermediate risk group, and 33% (n=6) from the high risk group. There were only three asymptomatic relapses in the low risk patients, accounting for 0.33% of the entire cohort., Conclusions: Relapses were infrequent and most presented with symptoms; prognosis after relapse remains favorable. Overall salvageable relapses were infrequent and cannot justify intensive hospital based follow-up. Use of patient initiated follow-up is therefore appropriate, as per the British Gynaecological Cancer Society's guidelines, for all risk groupings., Competing Interests: Competing interests: None declared., (© IGCS and ESGO 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.

Chemoradiation increases the eligibility for sphincter preservation in low rectal cancer, as assessed by MRI., Introduction: We evaluated whether MRI can predict sphincter preservation after chemoradiation (CRT), and whether the feasibility of sphincter preservation increases after CRT, when compared with MRI before neoadjuvant treatment., Methods: 85 patients with low rectal tumour (≤5 cm from anorectal junction (ARJ)) were included. Radiologist and a surgeon measured the tumour distance to ARJ, and assigned confidence level scores (CLS) for the feasibility of sphincter preserving surgery on MRI. Reference standard was the type of surgery, sphincter preserving vs. non-preserving., Results: Tumour distance from the ARJ increased after CRT by 9 mm (p < 0.001). Eligibility for sphincter preservation increased by 21% for the radiologist and 25% for the surgeon, based on CLS. Cut-off for distance to the ARJ after CRT was 28 mm, aiming for optimal specificity. Diagnostic performance after CRT based on CLS yielded an AUC of 0.81 [95%CI 0.70-0.91] for the radiologist and 0.82 [95%CI 0.72-0.92] for the surgeon (p = 0.78). AUCs for tumour distance to the ARJ were 0.85 [95%CI 0.77-0.94] and 0.84 [95%CI 0.75-0.94], respectively (p = 0.84). Interobserver agreement for CLS was moderate before CRT (Κ 0.51; 95%CI 0.36-0.66) and after (K 0.54; 95%CI 0.39-0.69). Measurement of tumour distance to ARJ showed good agreement before (ICC 0.76; 95%CI 0.65-0.84) and after CRT (ICC 0.77; 95%CI 0.66-0.84)., Conclusion: MRI can be a valuable adjunct in the decision making for sphincter preservation after CRT, with distance from the tumour to the ARJ as an accurate and reliable factor. CRT increases the tumour distance to the ARJ, leading to an estimated increase of sphincter preserving surgery in up to 21-25% of patients., (Copyright © 2020 Elsevier B.V. All rights reserved.)

In this study we assessed the repeatability of radiomics features on small prostate tumors using test-retest Multiparametric Magnetic Resonance Imaging (mpMRI). The premise of radiomics is that quantitative image-based features can serve as biomarkers for detecting and characterizing disease. For such biomarkers to be useful, repeatability is a basic requirement, meaning its value must remain stable between two scans, if the conditions remain stable. We investigated repeatability of radiomics features under various preprocessing and extraction configurations including various image normalization schemes, different image pre-filtering, and different bin widths for image discretization. Although we found many radiomics features and preprocessing combinations with high repeatability (Intraclass Correlation Coefficient > 0.85), our results indicate that overall the repeatability is highly sensitive to the processing parameters. Neither image normalization, using a variety of approaches, nor the use of pre-filtering options resulted in consistent improvements in repeatability. We urge caution when interpreting radiomics features and advise paying close attention to the processing configuration details of reported results. Furthermore, we advocate reporting all processing details in radiomics studies and strongly recommend the use of open source implementations.

Segmentation and image intensity discretization impact on radiomics workflow. The aim of this study is to investigate the influence of interobserver segmentation variability and intensity discretization methods on the reproducibility of MRI-based radiomic features in lipoma and atypical lipomatous tumor (ALT). Thirty patients with lipoma or ALT were retrospectively included. Three readers independently performed manual contour-focused segmentation on T1-weighted and T2-weighted sequences, including the whole tumor volume. Additionally, a marginal erosion was applied to segmentations to evaluate its influence on feature reproducibility. After image pre-processing, with included intensity discretization employing both fixed bin number and width approaches, 1106 radiomic features were extracted from each sequence. Intraclass correlation coefficient (ICC) 95% confidence interval lower bound ≥ 0.75 defined feature stability. In contour-focused vs. margin shrinkage segmentation, the rates of stable features extracted from T1-weighted and T2-weighted images ranged from 92.68 to 95.21% vs. 90.69 to 95.66% after fixed bin number discretization and from 95.75 to 97.65% vs. 95.39 to 96.47% after fixed bin width discretization, respectively, with no difference between the two segmentation approaches (p ≥ 0.175). Higher stable feature rates and higher feature ICC values were found when implementing discretization with fixed bin width compared to fixed bin number, regardless of the segmentation approach (p < 0.001). In conclusion, MRI radiomic features of lipoma and ALT are reproducible regardless of the segmentation approach and intensity discretization method, although a certain degree of interobserver variability highlights the need for a preliminary reliability analysis in future studies., (© 2024. The Author(s).)

Objective: The purpose of this study was to develop and validate a nomogram combined multiparametric MRI and clinical indicators for identifying the WHO grade of meningioma., Materials and Methods: Five hundred and sixty-eight patients were included in this study, who were diagnosed pathologically as having meningiomas. Firstly, radiomics features were extracted from CE-T1, T2, and 1-cm-thick tumor-to-brain interface (BTI) images. Then, difference analysis and the least absolute shrinkage and selection operator were orderly used to select the most representative features. Next, the support vector machine algorithm was conducted to predict the WHO grade of meningioma. Furthermore, a nomogram incorporated radiomics features and valuable clinical indicators was constructed by logistic regression. The performance of the nomogram was assessed by calibration and clinical effectiveness, as well as internal validation., Results: Peritumoral edema volume and gender are independent risk factors for predicting meningioma grade. The multiparametric MRI features incorporating CE-T1, T2, and BTI features showed the higher performance for prediction of meningioma grade with a pooled AUC = 0.885 (95% CI, 0.821-0.946) and 0.860 (95% CI, 0.788-0.923) in the training and test groups, respectively. Then, a nomogram with a pooled AUC = 0.912 (95% CI, 0.876-0.961), combined radiomics score, peritumoral edema volume, and gender improved diagnostic performance compared to radiomics model or clinical model and showed good calibration as the true results. Moreover, decision curve analysis demonstrated satisfactory clinical effectiveness of the proposed nomogram., Conclusions: A novel nomogram is simple yet effective in differentiating WHO grades of meningioma and thus can be used in patients with meningiomas., Clinical Relevance Statement: We proposed a nomogram that included clinical indicators and multi-parameter radiomics features, which can accurately, objectively, and non-invasively differentiate WHO grading of meningioma and thus can be used in clinical work., Key Points: • The study combined radiomics features and clinical indicators for objectively predicting the meningioma grade. • The model with CE-T1 + T2 + brain-to-tumor interface features demonstrated the best predictive performance by investigating seven different radiomics models. • The nomogram potentially has clinical applications in distinguishing high-grade and low-grade meningiomas., (© 2023. The Author(s).)

Cardiotoxicity refers to the damage induced by antineoplastic treatments, leading to various cardiovascular conditions. [18F]FDG PET radiomics analysis could provide relevant information on early onset changes occurring in cardiac metabolism of chemotherapy-induced cardiotoxicity. Patients' sociodemographic data, cardiovascular risk factors, laboratory parameters, and left ventricle [18F]FDG PET radiomic features are analyzed. The HRad index for the quantification of the heterogeneity of the metabolic uptake patterns is proposed. Statistical analysis is performed by separating patients according to the diagnosis of cancer therapy-related cardiac dysfunction (CTRCD). Baseline, intermediate, and end-of-treatment scans are evaluated as separate groups. Overall, CTRCD+ patients show lower overall mean standardized uptake values (SUVmean) compared to CTRCD− patients, with statistically significant differences between groups only observed in the intermediate PET study (p = 0.025). A total of 34 radiomic features show statistically significant differences between the CTRCD+ and CTRCD− groups in the intermediate imaging studies. In the CTRCD− group, greater overall heterogeneity of metabolic uptake is observed in the intermediate PET image compared to the CTRCD+ groups (p = 0.025). The assessment of CTRCD through [18F]FDG PET radiomics analysis could be a potential tool for the identification of a predisposition to the later development of cardiac complications after cardiotoxic treatment. [ABSTRACT FROM AUTHOR]

Aims: Based on the quantitative and qualitative fusion data of traditional Chinese medicine (TCM) and Western medicine, a diabetic foot (DF) prediction model was established through combining the objectified parameters of TCM and Western medicine. Methods: The ResNet-50 deep neural network (DNN) was used to extract depth features of tongue demonstration, and then a fully connected layer (FCL) was used for feature extraction to obtain aggregate features. Finally, a non-invasive DF prediction model based on tongue features was realized. Results: Among the 391 patients included, there were 267 DF patients, with their BMI (25.2 vs. 24.2) and waist-to-hip ratio (0.953 vs. 0.941) higher than those of type 2 diabetes mellitus (T2DM) group. The diabetes (15 years vs. 8 years) and hypertension durations (10 years vs. 7.5 years) in DF patients were significantly higher than those in T2DM group. Moreover, the plantar hardness in DF patients was higher than that in T2DM patients. The accuracy and sensitivity of the multi-mode DF prediction model reached 0.95 and 0.9286, respectively. Conclusion: We established a DF prediction model based on clinical features and objectified tongue color, which showed the unique advantages and important role of objectified tongue demonstration in the DF risk prediction, thus further proving the scientific nature of TCM tongue diagnosis. Based on the qualitative and quantitative fusion data, we combined tongue images with DF indicators to establish a multi-mode DF prediction model, in which tongue demonstration and objectified foot data can correct the subjectivity of prior knowledge. The successful establishment of the feature fusion diagnosis model can demonstrate the clinical practical value of objectified tongue demonstration. According to the results, the model had better performance to distinguish between T2DM and DF, and by comparing the performance of the model with and without tongue images, it was found that the model with tongue images performed better. [ABSTRACT FROM AUTHOR]

Current imaging methods for diagnosing breast cancer (BC) are associated with limited sensitivity and specificity and modest positive predictive power. The recent progress in image analysis using artificial intelligence (AI) has created great promise to improve BC diagnosis and subtype differentiation. In this case, novel quantitative computational methods, such as radiomics, have been developed to enhance the sensitivity and specificity of early BC diagnosis and classification. The potential of radiomics in improving the diagnostic efficacy of imaging studies has been shown in several studies. In this review article, we discuss the radiomics workflow and current handcrafted radiomics methods in the diagnosis and classification of BC based on the most recent studies on different imaging modalities, e.g., MRI, mammography, contrast-enhanced spectral mammography (CESM), ultrasound imaging, and digital breast tumosynthesis (DBT). We also discuss current challenges and potential strategies to improve the specificity and sensitivity of radiomics in breast cancer to help achieve a higher level of BC classification and diagnosis in the clinical setting. The growing field of AI incorporation with imaging information has opened a great opportunity to provide a higher level of care for BC patients. [ABSTRACT FROM AUTHOR]

Background: This study aims to develop and validate an integrated predictive model combining CT radiomics and clinical parameters for early assessment of acute pancreatitis severity. Methods: A retrospective cohort of 246 patients with acute pancreatitis was analyzed, with a 70%-30% split for training and validation groups. CT image segmentation was performed using ITK-SNAP, followed by the extraction of radiomics features. The stability of the radiomics features was assessed through inter-observer Intraclass Correlation Coefficient analysis. Feature selection was carried out using univariate analysis and least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation. A radiomics model was constructed through logistic regression to compute the radiomics score. Concurrently, univariate and multivariate logistic regression were employed to identify independent clinical risk factors for the clinical model. The radiomics score and clinical variables were integrated into a combined model, which was visualized with a nomogram. Model performance and net clinical benefit were evaluated through the area under the receiver operating characteristic curve (AUC), the DeLong test, and decision curve analysis. Results: A total of 913 radiomics features demonstrated satisfactory consistency. Eight features were selected for the radiomics model. Serum calcium, C-reactive protein, and white blood cell count were identified as independent clinical predictors. The AUC of the radiomics model was 0.871 (95% CI, 0.793–0.949) in the training cohort and 0.859 (95% CI, 0.751–0.967) in the validation cohort. The clinical model achieved AUCs of 0.833 (95% CI, 0.756–0.910) and 0.810 (95% CI, 0.692–0.929) for the training and validation cohorts, respectively. The combined model outperformed both the radiomics and clinical models, with an AUC of 0.905 (95% CI, 0.837–0.973) in the training cohort and 0.908 (95% CI, 0.824–0.992) in the validation cohort. The DeLong test confirmed superior predictive performance of the combined model over both the radiomics and clinical models in the training cohort, and over the clinical model in the validation cohort. Decision curve analysis further demonstrated that the combined model provided greater net clinical benefit than the radiomics or clinical models alone. Conclusion: The clinical-radiomics model offers a novel tool for the early prediction of acute pancreatitis severity, providing valuable support for clinical decision-making. [ABSTRACT FROM AUTHOR]

Background: Multidrug-resistant organisms (MDRO) pose a significant threat to public health worldwide. The ability to identify antimicrobial resistance determinants, to assess changes in molecular types, and to detect transmission are essential for surveillance and infection prevention of MDRO. Molecular characterization based on long-read sequencing has emerged as a promising alternative to short-read sequencing. The aim of this study was to characterize MDRO for surveillance and transmission studies based on long-read sequencing only. Methods: Genomic DNA of 356 MDRO was automatically extracted using the Maxwell-RSC48. The MDRO included 106 Klebsiella pneumoniae isolates, 85 Escherichia coli, 15 Enterobacter cloacae complex, 10 Citrobacter freundii, 34 Pseudomonas aeruginosa, 16 Acinetobacter baumannii, and 69 methicillin-resistant Staphylococcus aureus (MRSA), of which 24 were from an outbreak. MDRO were sequenced using both short-read (Illumina NextSeq 550) and long-read (Nanopore Rapid Barcoding Kit-24-V14, R10.4.1) whole-genome sequencing (WGS). Basecalling was performed for two distinct models using Dorado-0.3.2 duplex mode. Long-read data was assembled using Flye, Canu, Miniasm, Unicycler, Necat, Raven, and Redbean assemblers. Long-read WGS data with > 40 × coverage was used for multi-locus sequence typing (MLST), whole-genome MLST (wgMLST), whole-genome single-nucleotide polymorphisms (wgSNP), in silico multiple locus variable-number of tandem repeat analysis (iMLVA) for MRSA, and identification of resistance genes (ABRicate). Results: Comparison of wgMLST profiles based on long-read and short-read WGS data revealed > 95% of wgMLST profiles within the species-specific cluster cut-off, except for P. aeruginosa. The wgMLST profiles obtained by long-read and short-read WGS differed only one to nine wgMLST alleles or SNPs for K. pneumoniae, E. coli, E. cloacae complex, C. freundii, A. baumannii complex, and MRSA. For P. aeruginosa, differences were up to 27 wgMLST alleles between long-read and short-read wgMLST and 0–10 SNPs. MLST sequence types and iMLVA types were concordant between long-read and short-read WGS data and conventional MLVA typing. Antimicrobial resistance genes were detected in long-read sequencing data with high sensitivity/specificity (92–100%/99–100%). Long-read sequencing enabled analysis of an MRSA outbreak. Conclusions: We demonstrate that molecular characterization of automatically extracted DNA followed by long-read sequencing is as accurate compared to short-read sequencing and suitable for typing and outbreak analysis as part of genomic surveillance of MDRO. However, the analysis of P. aeruginosa requires further improvement which may be obtained by other basecalling algorithms. The low implementation costs and rapid library preparation for long-read sequencing of MDRO extends its applicability to resource-constrained settings and low-income countries worldwide. [ABSTRACT FROM AUTHOR]

Predicting H3K27M mutation status in midline gliomas non-invasively is of considerable interest, particularly using deep learning with 11C-methionine (MET) and 18F-fluoroethyltyrosine (FET) positron emission tomography (PET). To optimise prediction efficiency, we derived an assistance training (AT) scheme to allow mutual benefits between MET and FET learning to boost the predictability but still only require either PET as inputs for predictions. Our method significantly surpassed conventional convolutional neural network (CNN), radiomics-based, and MR-based methods, achieved an area under the curve (AUC) of 0.9343 for MET, and an AUC of 0.8619 for FET during internal cross-validation (n = 90). The performance remained high in hold-out testing (n = 19) and consecutive testing cohorts (n = 21), with AUCs of 0.9205 and 0.7404. The clinical feasibility of the proposed method was confirmed by the agreements to multi-departmental decisions and outcomes in pathology-uncertain cases. The findings positions our method as a promising tool for aiding treatment decisions in midline glioma. [ABSTRACT FROM AUTHOR]

Objective: Differentiating intramedullary spinal cord tumor (IMSCT) from spinal cord tumefactive demyelinating lesion (scTDL) remains challenging with standard diagnostic approaches. This study aims to develop and evaluate the effectiveness of a magnetic resonance imaging (MRI)-based radiomics model for distinguishing scTDL from IMSCT before treatment initiation. Methods: A total of 75 patients were analyzed in this retrospective study, comprising 55 with IMSCT and 20 with scTDL. Radiomics features were extracted from T1- and T2-weighted imaging (T1&T2WI) scans upon admission. Ten classification algorithms were employed: logistic regression (LR); naive bayes (NaiveBayes); support vector machine (SVM); k nearest neighbors (KNN); random forest (RF); extra trees (ExtraTrees); eXtreme gradient boosting (XGBoost); light gradient boosting machine (LightGBM); gradient boosting (GradientBoosting); and multi-Layer perceptron (MLP). The performance of the optimal model was then compared to radiologists' assessments. Results: This study developed 30 predictive models using ten classifiers across two imaging sequences. The MLP model with two sequences (T1&T2WI) emerged as the most effective one, showing superior accuracy in MRI analysis with an area under the curve (AUC) of 0.991 in training and 0.962 in testing. Moreover, statistical analyses highlighted the radiomics model significantly outperformed radiologists' assessments (p < 0.05) in distinguishing between IMSCT and scTDL. Conclusion: We present an MRI-based radiomics model with high diagnostic accuracy in differentiating IMSCT from scTDL. The model's performance was comparable to junior radiologists, highlighting its potential as an effective diagnostic aid in clinical practice. [ABSTRACT FROM AUTHOR]

Incomplete Atypical Femoral Fracture (IAFF) is a precursor to Atypical Femoral Fracture (AFF). If untreated, it progresses to a complete fracture, increasing mortality risk. However, due to their small and ambiguous features, IAFFs are often misdiagnosed even by specialists. In this paper, we propose a novel approach for accurately classifying IAFFs in X-ray images across various radiographic views. We design a Dual Context-aware Complementary Extractor (DCCE) to capture both the overall femur characteristics and IAFF details with the surrounding context, minimizing information loss. We also develop a Level-wise Perspective-preserving Fusion Network (LPFN) that preserves the perspective of features while integrating them at different levels to enhance model representation and sensitivity by learning complex correlations and features that are difficult to obtain independently. Additionally, we incorporate the Spatial Anomaly Focus Enhancer (SAFE) to emphasize anomalous regions, preventing the model bias toward normal regions, and reducing False Negatives and missed IAFFs. Experimental results show significant improvements across all evaluation metrics, demonstrating high reliability in terms of accuracy (0.931), F1-score (0.9456), and AUROC (0.9692), proving the model's potential for application in real medical settings. [ABSTRACT FROM AUTHOR]

Objectives: Radiomics-based analyses encompass multiple steps, leading to ambiguity regarding the optimal approaches for enhancing model performance. This study compares the effect of several feature selection methods, machine learning (ML) classifiers, and sources of radiomic features, on models' performance for the diagnosis of clinically significant prostate cancer (csPCa) from bi-parametric MRI. Methods: Two multi-centric datasets, with 465 and 204 patients each, were used to extract 1246 radiomic features per patient and MRI sequence. Ten feature selection methods, such as Boruta, mRMRe, ReliefF, recursive feature elimination (RFE), random forest (RF) variable importance, L1-lasso, etc., four ML classifiers, namely SVM, RF, LASSO, and boosted generalized linear model (GLM), and three sets of radiomics features, derived from T2w images, ADC maps, and their combination, were used to develop predictive models of csPCa. Their performance was evaluated in a nested cross-validation and externally, using seven performance metrics. Results: In total, 480 models were developed. In nested cross-validation, the best model combined Boruta with Boosted GLM (AUC = 0.71, F1 = 0.76). In external validation, the best model combined L1-lasso with boosted GLM (AUC = 0.71, F1 = 0.47). Overall, Boruta, RFE, L1-lasso, and RF variable importance were the top-performing feature selection methods, while the choice of ML classifier didn't significantly affect the results. The ADC-derived features showed the highest discriminatory power with T2w-derived features being less informative, while their combination did not lead to improved performance. Conclusion: The choice of feature selection method and the source of radiomic features have a profound effect on the models' performance for csPCa diagnosis. Critical relevance statement: This work may guide future radiomic research, paving the way for the development of more effective and reliable radiomic models; not only for advancing prostate cancer diagnostic strategies, but also for informing broader applications of radiomics in different medical contexts. Key Points: Radiomics is a growing field that can still be optimized. Feature selection method impacts radiomics models' performance more than ML algorithms. Best feature selection methods: RFE, LASSO, RF, and Boruta. ADC-derived radiomic features yield more robust models compared to T2w-derived radiomic features. [ABSTRACT FROM AUTHOR]

Objective: Ovarian cancer, a leading cause of death among gynecological malignancies, often eludes early detection, leading to diagnoses at advanced stages. The objective of this bibliometric analysis is to map the landscape of ovarian cancer radiomics research from 2010 to 2024, emphasizing its growth, global contributions, and the impact of emerging technologies on early diagnosis and treatment strategies. Methods: A comprehensive search was conducted using the Web of Science Core Collection (WoSCC), focusing on publications related to radiomics and ovarian cancer within the specified period. Analytical tools such as VOSviewer and CiteSpace were employed to visualize trends, collaborations, and key contributions, while the R programming environment offered further statistical insights. Results: From the initial dataset, 149 articles were selected, showing a significant increase in research output, especially in the years 2021-2023. The analysis revealed a dominant contribution from China, with significant inputs from England. Major institutional contributors included the University of Cambridge and GE Healthcare. 'Frontiers in Oncology' emerged as a crucial journal in the field, according to Bradford's Law. Keyword analysis highlighted the focus on advanced imaging techniques and machine learning. Conclusions: The steady growth in ovarian cancer radiomics research reflects its critical role in advancing diagnostic and prognostic methodologies, underscoring the potential of radiomics in the shift towards personalized medicine. Despite some methodological challenges, the field's dynamic evolution suggests a promising future for radiomics in enhancing the accuracy of ovarian cancer diagnosis and treatment, contributing to improved patient care and outcomes. [ABSTRACT FROM AUTHOR]

Purpose: To develop a predictive nomogram based on computed tomography (CT) radiomics to distinguish pulmonary tuberculosis (PTB) from communityacquired pneumonia (CAP). Methods: A total of 195 PTB patients and 163 CAP patients were enrolled from three hospitals. It is divided into a training cohort, a testing cohort and validation cohort. Clinical models were established by using significantly correlated clinical features. Radiomics features were screened by the least absolute shrinkage and selection operator (LASSO) algorithm. Radiomics scores (Radscore) were calculated from the formula of radiomics features. Clinical radiomics conjoint nomogram was established according to Radscore and clinical features, and the diagnostic performance of the model was evaluated by receiver operating characteristic (ROC) curve analysis. Results: Two clinical features and 12 radiomic features were selected as optimal predictors for the establishment of clinical radiomics conjoint nomogram. The results showed that the predictive nomogram had an outstanding ability to discriminate between the two diseases, and the AUC of the training cohort was 0.947 (95% CI, 0.916-0.979), testing cohort was 0.888 (95% CI, 0.814-0.961) and that of the validation cohort was 0.850 (95% CI, 0.778-0.922). Decision curve analysis (DCA) indicated that the nomogram has outstanding clinical value. Conclusions: This study developed a clinical radiomics model that uses radiomics features to identify PTB from CAP. This model provides valuable guidance to clinicians in identifying PTB. [ABSTRACT FROM AUTHOR]

Simple Summary: There is still an overdiagnosis of indolent prostate cancer (iPCa) lesions using the Prostate Imaging-Reporting and Data System (PI-RADS), and radiomics has emerged as a promising tool to improve the diagnosis of clinically significant prostate cancer (csPCa) lesions. However, the current state and applicability of radiomics remains a challenge. This systematic review aims at evaluating the evidence of handcrafted and deep radiomics in differentiating lesions at risk of having csPCa from those with iPCa and benign pathology. The review highlighted a good performance of radiomics but without significant differences with radiologist assessment (PI-RADS), as well as several methodological limitations in the reported studies, which might induce bias. Future studies should improve methodological aspects to ensure the clinical applicability of radiomics, especially the need for clinical prospective studies and the comparison with PI-RADS. Early detection of clinically significant prostate cancer (csPCa) has substantially improved with the latest PI-RADS versions. However, there is still an overdiagnosis of indolent lesions (iPCa), and radiomics has emerged as a potential solution. The aim of this systematic review is to evaluate the role of handcrafted and deep radiomics in differentiating lesions with csPCa from those with iPCa and benign lesions on prostate MRI assessed with PI-RADS v2 and/or 2.1. The literature search was conducted in PubMed, Cochrane, and Web of Science databases to select relevant studies. Quality assessment was carried out with Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2), Radiomic Quality Score (RQS), and Checklist for Artificial Intelligence in Medical Imaging (CLAIM) tools. A total of 14 studies were deemed as relevant from 411 publications. The results highlighted a good performance of handcrafted and deep radiomics methods for csPCa detection, but without significant differences compared to radiologists (PI-RADS) in the few studies in which it was assessed. Moreover, heterogeneity and restrictions were found in the studies and quality analysis, which might induce bias. Future studies should tackle these problems to encourage clinical applicability. Prospective studies and comparison with radiologists (PI-RADS) are needed to better understand its potential. [ABSTRACT FROM AUTHOR]

This study investigated the relationship between mediastinal fat and pulmonary nodule status, aiming to develop a deep learning-based radiomics model for diagnosing benign and malignant pulmonary nodules. We proposed a combined model using CT images of both pulmonary nodules and the fat around the chest (mediastinal fat). Patients from three centers were divided into training, validation, internal testing, and external testing sets. Quantitative radiomics and deep learning features from CT images served as predictive factors. A logistic regression model was used to combine data from both pulmonary nodules and mediastinal adipose regions, and personalized nomograms were created to evaluate the predictive performance. The model incorporating mediastinal fat outperformed the nodule-only model, with C-indexes of 0.917 (training), 0.903 (internal testing), 0.942 (external testing set 1), and 0.880 (external testing set 2). The inclusion of mediastinal fat significantly improved predictive performance (NRI = 0.243, p < 0.05). A decision curve analysis indicated that incorporating mediastinal fat features provided greater patient benefits. Mediastinal fat offered complementary information for distinguishing benign from malignant nodules, enhancing the diagnostic capability of this deep learning-based radiomics model. This model demonstrated strong diagnostic ability for benign and malignant pulmonary nodules, providing a more accurate and beneficial approach for patient care. [ABSTRACT FROM AUTHOR]

(1) Background: head and neck squamous cell carcinoma (HNSCC) is a common cancer whose prognosis is affected by its heterogeneous nature. We aim to predict 5-year overall survival in HNSCC radiotherapy (RT) patients by integrating radiomic and clinical information in machine-learning models; (2) Methods: HNSCC radiotherapy planning computed tomography (CT) images with RT structures were obtained from The Cancer Imaging Archive. Radiomic features and clinical data were independently analyzed by five machine-learning algorithms. The results were enhanced through a voted ensembled approach. Subsequently, a probability-weighted enhanced model (PWEM) was generated by incorporating both models; (3) Results: a total of 299 cases were included in the analysis. By receiver operating characteristic (ROC) curve analysis, PWEM achieved an area under the curve (AUC) of 0.86, which outperformed both radiomic and clinical factor models. Mean decrease accuracy, mean decrease Gini, and a chi-square test identified T stage, age, and disease site as the most important clinical factors in prognosis prediction; (4) Conclusions: our radiomic–clinical combined model revealed superior performance when compared to radiomic and clinical factor models alone. Further prospective research with a larger sample size is warranted to implement the model for clinical use. [ABSTRACT FROM AUTHOR]

Simple Summary: This review investigates how preprocessing parameters are related to the reproducibility and reliability of radiomic features derived from multimodality imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI), cone-beam CT (CBCT), and positron emission tomography (PET)/CT. Radiomics, which involves extracting quantitative features from medical images, shows great potential as a source of non-invasive clinical biomarkers but is hindered by variability in imaging parameters, especially during acquisition, reconstruction, and preprocessing. Standardizing and reporting preprocessing procedures is essential for consistent extraction of radiomic features, given their significant role in determining the robustness and reproducibility of these features. Background: Lately, radiomic studies featuring the development of a signature to use in prediction models in diagnosis or prognosis outcomes have been increasingly published. While the results are shown to be promising, these studies still have many pitfalls and limitations. One of the main issues of these studies is that radiomic features depend on how the images are preprocessed before their computation. Since, in widely known and used software for radiomic features calculation, it is possible to set these preprocessing parameters before the calculation of the radiomic feature, there are ongoing studies assessing the stability and repeatability of radiomic features to find the most suitable preprocessing parameters for every used imaging modality. Materials and Methods: We performed a comprehensive literature search using four electronic databases: PubMed, Cochrane Library, Embase, and Scopus. Mesh terms and free text were modeled in search strategies for databases. The inclusion criteria were studies where preprocessing parameters' influence on feature values and model predictions was addressed. Records lacking information on image acquisition parameters were excluded, and any eligible studies with full-text versions were included in the review process, while conference proceedings and monographs were disregarded. We used the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) tool to investigate the risk of bias. We synthesized our data in a table divided by the imaging modalities subgroups. Results: After applying the inclusion and exclusion criteria, we selected 43 works. This review examines the impact of preprocessing parameters on the reproducibility and reliability of radiomic features extracted from multimodality imaging (CT, MRI, CBCT, and PET/CT). Standardized preprocessing is crucial for consistent radiomic feature extraction. Key preprocessing steps include voxel resampling, normalization, and discretization, which influence feature robustness and reproducibility. In total, 44% of the included works studied the effects of an isotropic voxel resampling, and most studies opted to employ a discretization strategy. From 2021, several studies started selecting the best set of preprocessing parameters based on models' best performance. As for comparison metrics, ICC was the most used in MRI studies in 58% of the screened works. Conclusions: From our work, we highlighted the need to harmonize the use of preprocessing parameters and their values, especially in light of future studies of prospective studies, which are still lacking in the current literature. [ABSTRACT FROM AUTHOR]

Background: Oral Squamous Cell Carcinoma (OSCC) presents significant diagnostic challenges in its early and late stages. This study aims to utilize preoperative MRI and biochemical indicators of OSCC patients to predict the stage of tumors. Methods: This study involved 198 patients from two medical centers. A detailed analysis of contrast-enhanced T1-weighted (ceT1W) and T2-weighted (T2W) MRI were conducted, integrating these with biochemical indicators for a comprehensive evaluation. Initially, 42 clinical biochemical indicators were selected for consideration. Through univariate analysis and multivariate analysis, only those indicators with p-values less than 0.05 were retained for model development. To extract imaging features, machine learning algorithms in conjunction with Vision Transformer (ViT) techniques were utilized. These features were integrated with biochemical indicators for predictive modeling. The performance of model was evaluated using the Receiver Operating Characteristic (ROC) curve. Results: After rigorously screening biochemical indicators, four key markers were selected for the model: cholesterol, triglyceride, very low-density lipoprotein cholesterol and chloride. The model, developed using radiomics and deep learning for feature extraction from ceT1W and T2W images, showed a lower Area Under the Curve (AUC) of 0.85 in the validation cohort when using these imaging modalities alone. However, integrating these biochemical indicators improved the model’s performance, increasing the validation cohort AUC to 0.87. Conclusion: In this study, the performance of the model significantly improved following multimodal fusion, outperforming the single-modality approach. Clinical relevance statement: This integration of radiomics, ViT models, and lipid metabolite analysis, presents a promising non-invasive technique for predicting the staging of OSCC. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of our study is to validate the robustness and accuracy of consensus contour in 2-deoxy-2-[ 18 F]fluoro-D-glucose ( 18 F-FDG) PET radiomic features. Methods: 225 nasopharyngeal carcinoma (NPC) and 13 extended cardio-torso (XCAT) simulated data were enrolled. All segmentation were performed with four segmentation methods under two different initial masks, respectively. Consensus contour (ConSeg) was then developed using the majority vote rule. 107 radiomic features were extracted by Pyradiomics based on segmentation and the intraclass correlation coefficient (ICC) was calculated for each feature between masks or among segmentation, respectively. In XCAT ICC between segmentation and simulated ground truth were also calculated to access the accuracy. Results: ICC varied with the dataset, segmentation method, initial mask and feature type. ConSeg presented higher ICC for radiomic features in robustness tests and similar ICC in accuracy tests, compared with the average of four segmentation results. Higher ICC were also generally observed in irregular initial masks compared with rectangular masks in both robustness and accuracy tests. Furthermore, 19 features (17.76%) had ICC ≥ 0.75 in both robustness and accuracy tests for any of the segmentation methods or initial masks. The dataset was observed to have a large impact on the correlation relationships between radiomic features, but not the segmentation method or initial mask. Conclusions: The consensus contour combined with irregular initial mask could improve the robustness and accuracy in radiomic analysis to some extent. The correlation relationships between radiomic features and feature clusters largely depended on the dataset, but not segmentation method or initial mask. [ABSTRACT FROM AUTHOR]

Brain organoids represent a useful tool for modeling of neurodevelopmental disorders and can recapitulate brain volume alterations such as microcephaly. To monitor organoid growth, brightfield microscopy images are frequently used and evaluated manually which is time-consuming and prone to observer-bias. Recent software applications for organoid evaluation address this issue using classical or AI-based methods. These pipelines have distinct strengths and weaknesses that are not evident to external observers. We provide a dataset of more than 1,400 images of 64 trackable brain organoids from four clones differentiated from healthy and diseased patients. This dataset is especially powerful to test and compare organoid analysis pipelines because of (1) trackable organoids (2) frequent imaging during development (3) clone diversity (4) distinct clone development (5) cross sample imaging by two different labs (6) common imaging distractors, and (6) pixel-level ground truth organoid annotations. Therefore, this dataset allows to perform differentiated analyses to delineate strengths, weaknesses, and generalizability of automated organoid analysis pipelines as well as analysis of clone diversity and similarity. [ABSTRACT FROM AUTHOR]

Objective: Unusual clinical course Background: Papillary thyroid carcinoma is usually an indolent disease, with an almost 80% 5-year survival rate for metastatic disease. Conversely, anaplastic thyroid cancer is much more aggressive, with median overall survival rates of 4 months. Case Report: A 67-year-old woman presented with metastatic papillary thyroid cancer with bone metastasis, including an unstable L4 pathological fracture. Initially, she underwent lumbar stabilization surgery, followed by high-dose palliative radiotherapy to the lumbar spine. Subsequently, a total thyroidectomy was performed, followed by an ablative dose of radioiodine and supraphysiological doses of levothyroxine to achieve TSH suppression to less than 0.1 mU/L. The treatment dose of radioiodine was administered 4 times at 6-month intervals. The treatment was well tolerated, with a dramatic thyroglobulin response, and the disease remained radioiodine-sensitive. Prior to a fifth planned dose of radioiodine, our patient presented with cauda equina syndrome and underwent urgent decompressive surgery. Further oncological treatment was planned; however, she deteriorated rapidly following surgery, and repeat imaging showed progressive disease at the surgical site. Histopathology from the lumbar decompression revealed anaplastic thyroid cancer. Our patient died 5 weeks after surgery. Conclusions: This is the first published case of transformation from papillary to anaplastic thyroid cancer presenting as cauda equina compression. Transformation from papillary to anaplastic thyroid cancer has been previously described in the literature; however, it is rarely present distant from the neck, and has an aggressive course. Malignant transformation should be considered in cases of differentiated thyroid cancer that do not fit the previous disease trajectory. [ABSTRACT FROM AUTHOR]

Objectives: This study aims to develop an automated radiomics-based model to grade the pathological differentiation of head and neck squamous cell carcinoma (HNSCC) and to assess the influence of various magnetic resonance imaging (MRI) sequences on the model's performance., Materials and Methods: We retrospectively analyzed MRI data from 256 patients across two medical centers, including both contrast-enhanced T1-weighted images (CET1WI) and T2-weighted images (T2WI). Regions of interest were delineated for radiomics feature extraction, followed by dimensionality reduction. An XGBoost classifier was then employed to build the predictive model, with its classification efficiency assessed using receiver operating characteristic curves and the area under the curve (AUC)., Results: In validation cohort, the AUC (macro/micro) values for models utilizing CET1WI, T2WI, and the combination of CET1WI and T2WI were 0.801/0.814, 0.741/0.798, and 0.885/0.895, respectively. The AUC for the three differentiations, ranging from well-differentiated to poorly differentiated, were 0.867, 0.909, and 0.837, respectively. The macro/micro precision, recall, and F1 scores of 0.688/0.736, 0.744/0.828, and 0.685/0.779 for the CET1WI + T2WI model., Conclusion: This study demonstrates that constructing a radiomics model based on CET1WI and T2WI sequences can be used to predict the pathological differentiation grading of HNSCC patients., Clinical Relevance: This study suggests that a radiomics model integrating CET1WI and T2WI MRI sequences can effectively predict the pathological differentiation of HNSCC, providing an alternative diagnostic approach through non-invasive preoperative methods., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: This study was aimed to establish a prediction model for spread through air spaces (STAS) in early-stage non-small cell lung cancer based on imaging and genomic features., Methods: We retrospectively collected 204 patients (47 STAS+ and 157 STAS-) with non-small cell lung cancer who underwent surgical treatment in the Jinling Hospital from January 2021 to December 2021. Their preoperative CT images, genetic testing data (including next-generation sequencing data from other hospitals), and clinical data were collected. Patients were randomly divided into training and testing cohorts (7:3)., Results: The study included a total of 204 eligible patients. STAS were found in 47 (23.0%) patients, and no STAS were found in 157 (77.0%) patients. The receiver operating characteristic curve showed that radiomics model, clinical genomics model, and mixed model had good predictive performance (area under the curve [AUC] = 0.85; AUC = 0.70; AUC = 0.85)., Conclusions: The prediction model based on radiomics and genomics features has a good prediction performance for STAS., (© 2024 John Wiley & Sons Australia, Ltd.)

Phenotypic profiling by high throughput microscopy, including Cell Painting, has become a leading tool for screening large sets of perturbations in cellular models. To efficiently analyze this big data, available open-source software requires computational resources usually not available to most laboratories. In addition, the cell-to-cell variation of responses within a population, while collected and analyzed, is usually averaged and unused. We introduce SPACe (Swift Phenotypic Analysis of Cells), an open-source platform for analysis of single-cell image-based morphological profiles produced by Cell Painting. We highlight several advantages of SPACe, including processing speed, accuracy in mechanism of action recognition, reproducibility across biological replicates, applicability to multiple models, sensitivity to variable cell-to-cell responses, and biological interpretability to explain image-based features. We illustrate SPACe in a defined screening campaign of cell metabolism small-molecule inhibitors tested in seven cell lines to highlight the importance of analyzing perturbations across models., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)