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closedIPD-NOFeb 2026View details →

Radiomics Based on Multimodal Imaging in Predicting Staging and Prognosis of Pancreatic Cancer

ClinicalTrials.gov study NCT04954378. IPD Sharing: NO. Countries: 0. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →

Radiomic fEatures of Pancreas From Contrast Enhanced CT Image Predict One-Year RecUrrence Risk of Acute PancReatitis

ClinicalTrials.gov study NCT05778929. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →

Ultrasound Radiomics for Predicting Breast Cancer and Axillary Lymph Node Metastasis

ClinicalTrials.gov study NCT05768451. IPD Sharing: Not stated. Countries: 0. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →

Application of MRI Radiomics Features in Neoadjuvant Therapy of Head and Neck Squamous Cell Carcinoma

ClinicalTrials.gov study NCT06755567. IPD Sharing: Not stated. Countries: 0. Publications: 0.

restrictedMar 2020View details →

Dataset related to article "Predicting survival and local control after radiochemotherapy in locally advanced head and neck cancer by means of computed tomography based radiomics."

PURPOSE: To appraise the ability of a radiomics signature to predict clinical outcome after definitive radiochemotherapy (RCT) of stage III-IV head and neck cancer. METHODS: A cohort of 110 patients was included in a retrospective analysis. Radiomics texture features were extracted from the gross tumor volumes contoured on planning computed tomography (CT) images. The cohort of patients was randomly divided into a training (70 patients) and a validation (40 patients) cohorts. Textural features were correlated to survival and control data to build predictive models. All the significant predictors of the univariate analysis were included in a multivariate model. The quality of the models was appraised by means of the concordance index (CI). RESULTS: A signature with 3 features was identified as predictive of overall survival (OS) with CI = 0.88 and 0.90 for the training and validation cohorts, respectively. A signature with 2 features was identified for progression-free survival (PFS; CI = 0.72 and 0.80); 2 features also characterized the signature for local control (LC; CI = 0.72 and 0.82). In all cases, the stratification in high- and low-risk groups for the training and validation cohorts led to significant differences in the actuarial curves. In the validation cohort the mean OS times (in months) were 78.9 ± 2.1 vs 67.4 ± 6.0 in the low- and high-risk groups, respectively, the PFS was 73.1 ± 3.7 and 50.7 ± 7.2, while the LC was 78.7 ± 2.1 and 63.9 ± 6.5. CONCLUSION: CT-based radiomic signatures that correlate with survival and control after RCT were identified and allow low- and high-risk groups of patients to be identified.

restrictedMar 2020View details →

Dataset related to article "PET/CT radiomics in breast cancer: promising tool for prediction of pathological response to neoadjuvant chemotherapy."

PURPOSE: To assess the role of radiomics parameters in predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer. METHODS: Seventy-nine patients who had undergone pretreatment staging 18F-FDG PET/CT and treatment with NAC between January 2010 and January 2018 were included in the study. Primary lesions on PET images were delineated, and extraction of first-, second-, and higher-order imaging features was performed using LIFEx software. The relationship between these parameters and pCR to NAC was analyzed by multiple logistic regression models. RESULTS: Nineteen patients (24%) had pCR to NAC. Different models were generated on complete information and imputed datasets, using univariable and multivariable logistic regression and least absolute shrinkage and selection operator (lasso) regression. All models could predict pCR to NAC, with area under the curve values ranging from 0.70 to 0.73. All models agreed that tumor molecular subtype is the primary predictor of the primary endpoint. CONCLUSIONS: Our models predicted that patients with subtype 2 and subtype 3 (HER2+ and triple negative, respectively) are more likely to have a pCR to NAC than those with subtype 1 (luminal). The association between PET imaging features and pCR suggested that PET imaging features could be considered as potential predictors of pCR in locally advanced breast cancer patients.

restrictedNov 2020View details →

Dataset related to article "Computed tomography (CT)-derived radiomic features differentiate prevascular mediastinum masses as thymic neoplasms versus lymphomas"

This record contains raw data related to article "Computed tomography (CT)-derived radiomic features differentiate prevascular mediastinum masses as thymic neoplasms versus lymphomas" Objectives: We aimed to assess the ability of radiomics, applied to not-enhanced computed tomography (CT), to differentiate mediastinal masses as thymic neoplasms vs lymphomas. Methods: The present study was an observational retrospective trial. Inclusion criteria were pathology-proven thymic neoplasia or lymphoma with mediastinal localization, availability of CT. Exclusion criteria were age < 16 years and mediastinal lymphoma lesion < 4 cm. We selected 108 patients (M:F = 47:61, median age 48 years, range 17-79) and divided them into a training and a validation group. Radiomic features were used as predictors in linear discriminant analysis. We built different radiomic models considering segmentation software and resampling setting. Clinical variables were used as predictors to build a clinical model. Scoring metrics included sensitivity, specificity, accuracy and area under the curve (AUC). Wilcoxon paired test was used to compare the AUCs. Results: Fifty-five patients were affected by thymic neoplasia and 53 by lymphoma. In the validation analysis, the best radiomics model sensitivity, specificity, accuracy and AUC resulted 76.2 ± 7.0, 77.8 ± 5.5, 76.9 ± 6.0 and 0.84 ± 0.06, respectively. In the validation analysis of the clinical model, the same metrics resulted 95.2 ± 7.0, 88.9 ± 8.9, 92.3 ± 8.5 and 0.98 ± 0.07, respectively. The AUCs of the best radiomic and the clinical model not differed. Conclusions: We developed and validated a CT-based radiomic model able to differentiate mediastinal masses on non-contrast-enhanced images, as thymic neoplasms or lymphoma. The proposed method was not affected by image postprocessing. Therefore, the present image-derived method has the potential to noninvasively support diagnosis in patients with prevascular mediastinal masses with major impact on management of asymptomatic cases.

restrictedOct 2022View details →

Dataset related to article "Radiomics-based prognosis classification for high-risk prostate cancer treated with radiotherapy "

This record contains raw data related to article “Radiomics-based prognosis classification for high-risk prostate cancer treated with radiotherapy" Abstract: Objective: The present study aimed to investigate if CT-based radiomics features could correlate to the risk of metastatic progression in high-risk prostate cancer patients treated with radical RT and long-term androgen deprivation therapy (ADT). Materials and methods: A total of 157 patients were investigated and radiomics features extracted from the contrast-free treatment planning CT series. Three volumes were segmented: the prostate gland only (CTV_p), the prostate gland with seminal vesicles (CTV_psv), and the seminal vesicles only (CTV_sv). The patients were split into two subgroups of 100 and 57 patients for training and validation. Five clinical and 62 radiomics features were included in the analysis. Considering metastases-free survival (MFS) as an endpoint, the predictive model was used to identify the subgroups with favorable or unfavorable prognoses (separated by a threshold selected according to the Youden method). Pure clinical, pure radiomic, and combined predictive models were investigated. Results: With a median follow-up of 30.7 months, the MFS at 1 and 3 years was 97.2% ± 1.5 and 92.1% ± 2.0, respectively. Univariate analysis identified seven potential predictors for MFS in the CTV_p group, 11 in the CTV_psv group, and 9 in the CTV_sv group. After elastic net reduction, these were 4 predictors for MFS in the CTV_p group (positive lymph nodes, Gleason score, H_Skewness, and NGLDM_Contrast), 5 in the CTV_psv group (positive lymph nodes, Gleason score, H_Skewnesss, Shape_Surface, and NGLDM_Contrast), and 6 in the CTV_sv group (positive lymph nodes, Gleason score, H_Kurtosis, GLCM_Correlation, GLRLM_LRHGE, and GLZLM_SZLGE). The patients' group of the training and validation cohorts were stratified into favorable and unfavorable prognosis subgroups. For the combined model, for CTV_p, the mean MFS was 134 ± 14.5 vs. 96.9 ± 22.2 months for the favorable and unfavorable subgroups, respectively, and 136.5 ± 14.6 vs. 70.5 ± 4.3 months for CTV_psv and 150.0 ± 4.2 vs. 91.1 ± 8.6 months for CTV_sv, respectively. Conclusion: Radiomic features were able to predict the risk of metastatic progression in high-risk prostate cancer. Combining the radiomic features and clinical characteristics can classify high-risk patients into favorable and unfavorable prognostic groups.

restrictedJan 2023View details →

Dataset related to article "PET/CT-based radiomics of mass-forming intrahepatic cholangiocarcinoma improves prediction of pathology data and survival "

This record contains raw data related to article "PET/CT-based radiomics of mass-forming intrahepatic cholangiocarcinoma improves prediction of pathology data and survival" Abstract Purpose: Intrahepatic cholangiocarcinoma (IHC) is an aggressive disease with few reliable preoperative biomarkers. This study aims to elucidate if radiomics extracted from preoperative [18F]FDG PET/CT may grant a non-invasive biological characterization of IHC and predict outcome after complete resection of the tumor. Methods: All patients preoperatively imaged by [18F]FDG PET/CT who underwent hepatectomy for mass-forming IHC in the period 2010-2019 were retrospectively evaluated. On PET images, manual slice-by-slice segmentation of IHC was performed (Tumor-VOI). A 5-mm margin region was semi-automatically generated around the tumor (Margin-VOI). Textural analysis was performed using the LifeX software. Analyzed outcomes included tumor grading (G3 vs. G1-2), microvascular invasion (MVI), overall survival (OS), and progression-free survival (PFS). The performances of the combined clinical-radiomic models were compared with those of standard clinical models. Results: Overall, 74 patients (40 females, median age 68 years) were included. Considering tumor grading and MVI, the models combining the clinical data and radiomics of the Tumor-VOI had better performances than the clinical ones (AUC = 0.78 vs. 0.72 for grading; 0.87 vs. 0.78 for MVI). The inclusion into the models of radiomics of the Margin-VOI further improved the prediction of grading (AUC = 0.83), but not of MVI. Considering OS and PFS, the models including the preoperative clinical data and radiomics of the Tumor-VOI and Margin-VOI had better performances than the pure clinical ones (C-index = 0.81 vs. 0.76 for OS; 0.81 vs. 0.72 for PFS) and similar to the models including the pathology and postoperative data (C-index = 0.81 for OS; 0.79 for PFS). No model retained the standard SUV measures. Conclusion: The PET-based radiomics of IHC can predict pathology data and allow a reliable preoperative evaluation of prognosis. The radiomics of both the tumoral and peritumoral areas had clinical relevance. The combined clinical-radiomic models outperformed the pure preoperative clinical ones and achieved performances non-inferior to the postoperative models.

restrictedFeb 2023View details →

Danish study of Radiomics

This dataset contains sensitive data and is therefore not openly available. A description of the dataset is provided below: This dataset contains pseudonymized data for the Danish study of Radiomics (Dan-R), and will undergo updates with addition of data. Dan-R is an ongoing retrospective multicenter study with patient data from The North Denmark Region, The Central Denmark Region, The Region of Southern Denmark, and The Western Denmark Heart Registry. The data consist of imaging data, coronary artery calcium score, and corresponding reported outcomes from patients reporting with symptoms of severe arteriosclerotic vascular disease referred to cardiac CT imaging. Dan-R aims to facilitate the development of machine learning solutions for detection of e.g., atherosclerosis or coronary artery disease based on cardiac CT imaging.

restrictedMar 2020View details →

Dataset related to article "Computed tomography based radiomic signature as predictive of survival and local control after stereotactic body radiation therapy in pancreatic carcinoma"

PURPOSE: To appraise the ability of a radiomics signature to predict clinical outcome after stereotactic body radiation therapy (SBRT) for pancreas carcinoma. METHODS: A cohort of 100 patients was included in this retrospective, single institution analysis. Radiomics texture features were extracted from computed tomography (CT) images obtained for the clinical target volume. The cohort of patients was randomly divided into two separate groups for the training (60 patients) and validation (40 patients). Cox regression models were built to predict overall survival and local control. The significant predictors at univariate analysis were included in a multivariate model. The quality of the models was appraised by means of area under the curve and concordance index. RESULTS: A clinical-radiomic signature associated with Overall Survival (OS) was found significant in both training and validation sets (p = 0.01 and 0.05 and concordance index 0.73 and 0.75 respectively). Similarly, a signature was found for Local Control (LC) with p = 0.007 and 0.004 and concordance index 0.69 and 0.75. In the low risk group, the median OS and LC in the validation group were 14.4 and 28.6 months while in the high-risk group were 9.0 and 17.5 months respectively. CONCLUSION: A CT based radiomic signature was identified which correlate with OS and LC after SBRT and allowed to identify low and high-risk groups of patients.

restrictedNov 2023View details →

Dataset related to article "Predictive value of clinical and radiomic features for radiation therapy response in patients with lymph node-positive head and neck cancer"

AbstractBackground: Prediction of survival and radiation therapy response is challenging in head and neck cancer with metastatic lymph nodes (LNs). Here we developed novel radiomics- and clinical-based predictive models.Methods: Volumes of interest of LNs were employed for radiomic features extraction. Radiomic and clinical features were investigated for their predictive value relatively to locoregional failure (LRF), progression-free survival (PFS), and overall survival (OS) and used to build multivariate models.Results: Hundred and six subjects were suitable for final analysis. Univariate analysis identified two radiomic features significantly predictive for LRF, and five radiomic features plus two clinical features significantly predictive for both PFS and OS. The area under the curve of receiver operating characteristic curve combining clinical and radiomic predictors for PFS and OS resulted 0.71 (95%CI: 0.60-0.83) and 0.77 (95%CI: 0.64-0.89).Conclusions: Radiomic and clinical features resulted to be independent predictive factors, but external independent validation is mandatory to support these findin

restrictedJan 2022View details →

Dataset related to article "Contrast Administration Impacts CT-Based Radiomics of Colorectal Liver Metastases and Non-Tumoral Liver Parenchyma Revealing the "Radiological" Tumour Microenvironment"

This record contains raw data related to article "Contrast Administration Impacts CT-Based Radiomics of Colorectal Liver Metastases and Non-Tumoral Liver Parenchyma Revealing the "Radiological" Tumour Microenvironment"   The impact of the contrast medium on the radiomic textural features (TF) extracted from the CT scan is unclear. We investigated the modification of TFs of colorectal liver metastases (CLM), peritumoral tissue, and liver parenchyma. One hundred and sixty-two patients with 409 CLMs undergoing resection (2017-2020) into a single institution were considered. We analyzed the following volumes of interest (VOIs): The CLM (Tumor-VOI); a 5-mm parenchyma rim around the CLM (Margin-VOI); and a 2-mL sample of parenchyma distant from CLM (Liver-VOI). Forty-five TFs were extracted from each VOI (LIFEx®®). Contrast enhancement affected most TFs of the Tumor-VOI (71%) and Margin-VOI (62%), and part of those of the Liver-VOI (44%, p = 0.010). After contrast administration, entropy increased and energy decreased in the Tumor-VOI (0.93 ± 0.10 vs. 0.85 ± 0.14 in pre-contrast; 0.14 ± 0.03 vs. 0.18 ± 0.04, p < 0.001) and Margin-VOI (0.89 ± 0.11 vs. 0.85 ± 0.12; 0.16 ± 0.04 vs. 0.18 ± 0.04, p < 0.001), while remaining stable in the Liver-VOI. Comparing the VOIs, pre-contrast Tumor and Margin-VOI had similar entropy and energy (0.85/0.18 for both), while Liver-VOI had lower values (0.76/0.21, p < 0.001). In the portal phase, a gradient was observed (entropy: Tumor > Margin > Liver; energy: Tumor < Margin < Liver, p < 0.001). Contrast enhancement affected TFs of CLM, while it did not modify entropy and energy of parenchyma. TFs of the peritumoral tissue had modifications similar to the Tumor-VOI despite its radiological aspect being equal to non-tumoral parenchyma.

restrictedJan 2022View details →

Dataset related to article "Virtual Biopsy for Diagnosis of Chemotherapy-Associated Liver Injuries and Steatohepatitis: A Combined Radiomic and Clinical Model in Patients with Colorectal Liver Metastases "

This record contains raw data related to article "Virtual Biopsy for Diagnosis of Chemotherapy-Associated Liver Injuries and Steatohepatitis: A Combined Radiomic and Clinical Model in Patients with Colorectal Liver Metastases " Non-invasive diagnosis of chemotherapy-associated liver injuries (CALI) is still an unmet need. The present study aims to elucidate the contribution of radiomics to the diagnosis of sinusoidal dilatation (SinDil), nodular regenerative hyperplasia (NRH), and non-alcoholic steatohepatitis (NASH). Patients undergoing hepatectomy for colorectal metastases after chemotherapy (January 2018-February 2020) were retrospectively analyzed. Radiomic features were extracted from a standardized volume of non-tumoral liver parenchyma outlined in the portal phase of preoperative post-chemotherapy computed tomography. Seventy-eight patients were analyzed: 25 had grade 2-3 SinDil, 27 NRH, and 14 NASH. Three radiomic fingerprints independently predicted SinDil: GLRLM_f3 (OR = 12.25), NGLDM_f1 (OR = 7.77), and GLZLM_f2 (OR = 0.53). Combining clinical, laboratory, and radiomic data, the predictive model had accuracy = 82%, sensitivity = 64%, and specificity = 91% (AUC = 0.87 vs. AUC = 0.77 of the model without radiomics). Three radiomic parameters predicted NRH: conventional_HUQ2 (OR = 0.76), GLZLM_f2 (OR = 0.05), and GLZLM_f3 (OR = 7.97). The combined clinical/laboratory/radiomic model had accuracy = 85%, sensitivity = 81%, and specificity = 86% (AUC = 0.91 vs. AUC = 0.85 without radiomics). NASH was predicted by conventional_HUQ2 (OR = 0.79) with accuracy = 91%, sensitivity = 86%, and specificity = 92% (AUC = 0.93 vs. AUC = 0.83 without radiomics). In the validation set, accuracy was 72%, 71%, and 91% for SinDil, NRH, and NASH. Radiomic analysis of liver parenchyma may provide a signature that, in combination with clinical and laboratory data, improves the diagnosis of CALI.

restrictedNov 2020View details →

MRI radiomics-based machine-learning classification of bone chondrosarcoma

Purpose: To evaluate the diagnostic performance of machine learning for discrimination between low-grade and high-grade cartilaginous bone tumors based on radiomic parameters extracted from unenhanced magnetic resonance imaging (MRI). Methods: We retrospectively enrolled 58 patients with histologically-proven low-grade/atypical cartilaginous tumor of the appendicular skeleton (n = 26) or higher-grade chondrosarcoma (n = 32, including 16 appendicular and 16 axial lesions). They were randomly divided into training (n = 42) and test (n = 16) groups for model tuning and testing, respectively. All tumors were manually segmented on T1-weighted and T2-weighted images by drawing bidimensional regions of interest, which were used for first order and texture feature extraction. A Random Forest wrapper was employed for feature selection. The resulting dataset was used to train a locally weighted ensemble classifier (AdaboostM1). Its performance was assessed via 10-fold cross-validation on the training data and then on the previously unseen test set. Thereafter, an experienced musculoskeletal radiologist blinded to histological and radiomic data qualitatively evaluated the cartilaginous tumors in the test group. Results: After feature selection, the dataset was reduced to 4 features extracted from T1-weighted images. AdaboostM1 correctly classified 85.7 % and 75 % of the lesions in the training and test groups, respectively. The corresponding areas under the receiver operating characteristic curve were 0.85 and 0.78. The radiologist correctly graded 81.3 % of the lesions. There was no significant difference in performance between the radiologist and machine learning classifier (P = 0.453). Conclusions: Our machine learning approach showed good diagnostic performance for classification of low-to-high grade cartilaginous bone tumors and could prove a valuable aid in preoperative tumor characterization.

restrictedApr 2024View details →

MRI dataset for susceptibility-based radiomic feature extraction in healthy controls and patients with multiple sclerosis

This dataset include multiparametric MRI brain images and susceptiblity-based radiomic features within a mixed sample of 100 patients with multiple sclerosis and 50 healthy controls. We focused on the normal appearing white matter and its tracts. Imaging and radiomic data are organized according to the Brain Imaging Directory Structure (BIDS) and for each subjects the following data are provided: anatomical T1w and T2w  images; DWI after the correction for EPI distortions and susceptibility effects, eddy currents, and signal dropout, b-values and b-vectors and registration matrix to T1w; QSM QSM reconstruction, already registered in T1w space, raw magnitude and phase maps for the 5 echo times and registration matrix to T1w; volumes of interest (normal appearing white matter and tracts, divided for the two hemispheres); 107 radiomic features for each volume of interest. All images were anonymized and de-identified.   Additionally, in the database there is the ‘code’ folder containing the available scripts used for the processing (image registration, radiomic feature extraction, and robustness evaluation) and the participants file (containing for each subject the ID number, age, sex, date of scan, clinical condition (‘HC’ and ‘MS’) and the of the DWI sequence used, i.e. single- or multi-shell) and the dataset description. Data were provided by the Functional and Molecular Neuroimaging Unit at IRCCS Istituto delle Scienze Neurologiche di Bologna, Italy. <a href="https://doi.org/10.5281/zenodo.11278906">Here</a> you can find the Data Use Agreement (DUA) to sign before using the dataset, together with the instructions to have the access. More details about image acquisition and processing may be found in Fiscone et al. (2024) Multiparametric MRI database for susceptibility-based radiomic feature extraction and analysis, Scientific Data. Please cite this paper if you use this dataset for publications or presentations. For any further information, please email cristiana.fiscone@gmail.com, davidneil.manners@unibo.it or giovanni.sighinolfi3@unibo.it.

restrictedJul 2024View details →

Dataset related to article "The predictive role of radiomics in breast cancer patients im-aged by [18F]FDG PET: preliminary results from a prospective cohort"

This record contains raw data related to article “The predictive role of radiomics in breast cancer patients im-aged by [18F]FDG PET: preliminary results from a prospective cohort” Abstract: Background: In the last decade, radiomics emerged as a source of image-derived biomarkers. However, existing data predominantly stem from retrospective analyses. We aimed to prospectively assess the predictive role of [18F]FDG PET radiomics in breast cancer (BC) patients. Methods: we prospectively enrolled stage I-III BC patients eligible for neoadjuvant chemotherapy (NAC), who underwent staging [18F]FDG PET/CT. All patients had data regarding pathological treatment response assessed in the post-NAC surgical specimen and were grouped in pathological complete responders (pCR) and pathological residual disease (non-pCR). Radiomic PET features were extracted from the volume of interest drawn on the primary breast lesion. The predictive role of clinical, histological, and radiomic data with respect to pCR was assessed. Univariate and multivariate statistics were used for inference; principal component analysis (PCA) was used for dimensionality reduction. Results: We analyzed 53 HER2+, and 40 triple-negative (TNBC) BC patients. pCR was obtained in 24/53(45%) HER2+ and 20/40(50%) TNBC patients. Age, molecular subtype, ki-67, and stage were not statistically different between classes and couldn’t predict pCR at multivariate analysis. At univariate analysis, 10 radiomic features resulted with a p < 0.1. 3/22 radiomic principal components (PC) were found to be discriminative for pCR. Using a cross-validation approach, the radiomic PC failed to discriminate pCR vs non-pCR groups but were able to predict the stage (mean accuracy = 0.79±0.08); Conclusions: These preliminary results demonstrate the potential of radiomic features extracted from PET for staging purposes in BC patients, while their possible role in predicting pCR to NAC needs to be further investigated.