Abstract Background Effective diagnostic tools for prompt identification of high-risk locally advanced cervical cancer (LACC) patients are needed to facilitate early, individualized treatment. The aim of this work was to assess temporal changes in tumor radiomics (delta radiomics) from T2-weighted imaging (T2WI) during concurrent chemoradiotherapy (CCRT) in LACC patients, and their association with progression-free survival (PFS). Furthermore, to develop, validate, and compare delta- and pretreatment radiomic signatures for prognostic modeling. Methods A total of 110 LACC patients undergoing CCRT with MRI at baseline and mid-treatment were divided into training (cohortT: n = 73) and validation (cohortV: n = 37) cohorts. Radiomic features were extracted from tumors segmented on pre-CCRT and mid-CCRT T2WI and radiomic deltas (delta features) were computed. Two radiomic signatures for predicting PFS were constructed by least absolute shrinkage and selection operator (LASSO) Cox regression: Deltarad (from delta features) and Pre-CCRTrad (from pre-CCRT features). Prognostic performance of the radiomic signatures, 2018 International Federation of Gynecology and Obstetrics (FIGO) stage (I–IV), and baseline MRI-derived maximum tumor diameter (Tumormax: ≤2 cm; >2 and ≤ 4 cm; >4 cm) was evaluated by area under time-dependent receiver operating characteristics (tdROC) curves (AUC) in cohortT and cohortV (AUCT/AUCV). Mann–Whitney U tests assessed differences in radiomic delta features. PFS was evaluated using the Kaplan–Meier method with log-rank tests. Results Deltarad (AUCT/AUCV: 0.74/0.79) marginally outperformed Pre-CCRTrad (0.72/0.75) for predicting 5-year PFS, and both signatures clearly surpassed that of FIGO (0.61/0.61) and Tumormax (0.58/0.65). In total, four features within Deltarad and Pre-CCRTrad significantly differed in delta feature values between progressors and non-progressors, being consistently lower in progressors (p ≤ 0.03 for all). High Deltarad and Pre-CCRTrad radiomic scores were associated with poor PFS (p ≤ 0.04 for Deltarad in cohortT/Pre-CCRTrad in both cohorts; p = 0.11 for Deltarad in cohortV). Conclusions Delta- and pretreatment radiomic signatures equally allow early prognostication in LACC, outperforming FIGO stage and MRI-assessed maximum tumor diameter.

BackgroundRadiomics can capture microscale information in medical images beyond what is visible to the naked human eye. Using a clinically relevant mouse model for endometrial cancer, the objective of this study was to develop and validate a radiomic signature (RS) predicting response to standard chemotherapy.MethodsMice orthotopically implanted with a patient-derived grade 3 endometrioid endometrial cancer organoid model (O-PDX) were allocated to chemotherapy (combined paclitaxel/carboplatin, n=11) or saline/control (n=13). During tumor progression, the mice underwent weekly T2-weighted (T2w) magnetic resonance imaging (MRI). Segmentation of primary tumor volume (vMRI) allowed extraction of radiomic features from whole-volume tumor masks. A radiomic model for predicting treatment response was derived employing least absolute shrinkage and selection operator (LASSO) statistics at endpoint images in the orthotopic O-PDX (RS_O), and subsequently applied on the earlier study timepoints (RS_O at baseline, and week 1-3). For external validation, the radiomic model was tested in a separate T2w-MRI dataset on segmented whole-volume subcutaneous tumors (RS_S) from the same O-PDX model, imaged at three timepoints (baseline, day 3 and day 10/endpoint) after start of chemotherapy (n=8 tumors) or saline/control (n=8 tumors).ResultsThe RS_O yielded rapidly increasing area under the receiver operating characteristic (ROC) curves (AUCs) for predicting treatment response from baseline until endpoint; AUC=0.38 (baseline); 0.80 (week 1), 0.85 (week 2), 0.96 (week 3) and 1.0 (endpoint). In comparison, vMRI yielded AUCs of 0.37 (baseline); 0.69 (w1); 0.83 (week 2); 0.92 (week 3) and 0.97 (endpoint). When tested in the external validation dataset, RS_S yielded high accuracy for predicting treatment response at day10/endpoint (AUC=0.85) and tended to yield higher AUC than vMRI (AUC=0.78, p=0.18). Neither RS_S nor vMRI predicted response at day 3 in the external validation set (AUC=0.56 for both).ConclusionsWe have developed and validated a radiomic signature that was able to capture chemotherapeutic treatment response both in an O-PDX and in a subcutaneous endometrial cancer mouse model. This study supports the promising role of preclinical imaging including radiomic tumor profiling to assess early treatment response in endometrial cancer models.

Cervical cancer (CC) is a major global health problem with 570,000 new cases and 266,000 deaths annually. Prognosis is poor for advanced stage disease, and few effective treatments exist. Preoperative diagnostic imaging is common in high-income countries and MRI measured tumor size routinely guides treatment allocation of cervical cancer patients. Recently, the role of MRI radiomics has been recognized. However, its potential to independently predict survival and treatment response requires further clarification. This retrospective cohort study demonstrates how non-invasive, preoperative, MRI radiomic profiling may improve prognostication and tailoring of treatments and follow-ups for cervical cancer patients. By unsupervised clustering based on 293 radiomic features from 132 patients, we identify three distinct clusters comprising patients with significantly different risk profiles, also when adjusting for FIGO stage and age. By linking their radiomic profiles to genomic alterations, we identify putative treatment targets for the different patient clusters (e.g., immunotherapy, CDK4/6 and YAP-TEAD inhibitors and p53 pathway targeting treatments).

Abstract Widespread clinical use of MRI radiomic tumor profiling for prognostication and treatment planning in cancers faces major obstacles due to limitations in standardization of radiomic features. The purpose of the current work was to assess the impact of different MRI scanning- and normalization protocols for the statistical analyses of tumor radiomic data in two patient cohorts with uterine endometrial-(EC) (n = 136) and cervical (CC) (n = 132) cancer. 1.5 T and 3 T, T1-weighted MRI 2 min post-contrast injection, T2-weighted turbo spin echo imaging, and diffusion-weighted imaging were acquired. Radiomic features were extracted from within manually segmented tumors in 3D and normalized either using z-score normalization or a linear regression model (LRM) accounting for linear dependencies with MRI acquisition parameters. Patients were clustered into two groups based on radiomic profile. Impact of MRI scanning parameters on cluster composition and prognostication were analyzed using Kruskal–Wallis tests, Kaplan–Meier plots, log-rank test, random survival forests and LASSO Cox regression with time-dependent area under curve (tdAUC) (α = 0.05). A large proportion of the radiomic features was statistically associated with MRI scanning protocol in both cohorts (EC: 162/385 [42%]; CC: 180/292 [62%]). A substantial number of EC (49/136 [36%]) and CC (50/132 [38%]) patients changed cluster when clustering was performed after z-score-versus LRM normalization. Prognostic modeling based on cluster groups yielded similar outputs for the two normalization methods in the EC/CC cohorts (log-rank test; z-score: p = 0.02/0.33; LRM: p = 0.01/0.45). Mean tdAUC for prognostic modeling of disease-specific survival (DSS) by the radiomic features in EC/CC was similar for the two normalization methods (random survival forests; z-score: mean tdAUC = 0.77/0.78; LRM: mean tdAUC = 0.80/0.75; LASSO Cox; z-score: mean tdAUC = 0.64/0.76; LRM: mean tdAUC = 0.76/0.75). Severe biases in tumor radiomics data due to MRI scanning parameters exist. Z-score normalization does not eliminate these biases, whereas LRM normalization effectively does. Still, radiomic cluster groups after z-score- and LRM normalization were similarly associated with DSS in EC and CC patients.

Active angiogenesis may be assessed by immunohistochemistry using Nestin, a marker of newly formed vessels, combined with Ki67 for proliferating cells. Here, we studied microvascular proliferation by Nestin-Ki67 co-expression in prostate cancer, focusing on relations to quantitative imaging parameters from anatomically matched areas obtained by preoperative mpMRI, clinico-pathological features and prognosis. Tumour slides from 67 patients (radical prostatectomies) were stained for Nestin-Ki67. Proliferative microvessel density (pMVD) and presence of glomeruloid microvascular proliferation (GMP) were recorded. From mpMRI, forward volume transfer constant (Ktrans), reverse volume transfer constant (kep), volume of EES (ve), blood flow, and apparent diffusion coefficient (ADC) were obtained. High pMVD was associated with high blood flow (p = 0.008) and low ADC (p = 0.032). High Ktrans, kep, and blood flow were associated with high Gleason score. High pMVD, GMP, and low ADC were associated with most adverse clinico-pathological factors. Regarding prognosis, high pMVD, Ktrans, kep, and low ADC were associated with reduced biochemical recurrence-free- and metastasis-free survival (p ≤ 0.044) and high blood flow with reduced time to biochemical- and clinical recurrence (p < 0.026). In multivariate analyses however, microvascular proliferation was a stronger predictor compared with blood flow. Indirect, dynamic markers of angiogenesis from mpMRI and direct, static markers of angiogenesis from immunohistochemistry may aid in the stratification and therapy planning of prostate cancer patients.

Abstract Background Accurate pretherapeutic prognostication is important for tailoring treatment in cervical cancer (CC). Purpose To investigate whether pretreatment MRI‐based radiomic signatures predict disease‐specific survival (DSS) in CC. Study Type Retrospective. Population CC patients (n = 133) allocated into training(T) (nT = 89)/validation(V) (nV = 44) cohorts. Field Strength/Sequence T2‐weighted imaging (T2WI) and diffusion‐weighted imaging (DWI) at 1.5T or 3.0T. Assessment Radiomic features from segmented tumors were extracted from T2WI and DWI (high b‐value DWI and apparent diffusion coefficient (ADC) maps). Statistical Tests Radiomic signatures for prediction of DSS from T2WI (T2rad) and T2WI with DWI (T2 + DWIrad) were constructed by least absolute shrinkage and selection operator (LASSO) Cox regression. Area under time‐dependent receiver operating characteristics curves (AUC) were used to evaluate and compare the prognostic performance of the radiomic signatures, MRI‐derived maximum tumor size ≤/> 4 cm (MAXsize), and 2018 International Federation of Gynecology and Obstetrics (FIGO) stage (I–II/III–IV). Survival was analyzed using Cox model estimating hazard ratios (HR) and Kaplan–Meier method with log‐rank tests. Results The radiomic signatures T2rad and T2 + DWIrad yielded AUCT/AUCV of 0.80/0.62 and 0.81/0.75, respectively, for predicting 5‐year DSS. Both signatures yielded better or equal prognostic performance to that of MAXsize (AUCT/AUCV: 0.69/0.65) and FIGO (AUCT/AUCV: 0.77/0.64) and were significant predictors of DSS after adjusting for FIGO (HRT/HRV for T2rad: 4.0/2.5 and T2 + DWIrad: 4.8/2.1). Adding T2rad and T2 + DWIrad to FIGO significantly improved DSS prediction compared to FIGO alone in cohort(T) (AUCT 0.86 and 0.88 vs. 0.77), and FIGO with T2 + DWIrad tended to the same in cohort(V) (AUCV 0.75 vs. 0.64, p = 0.07). High radiomic score for T2 + DWIrad was significantly associated with reduced DSS in both cohorts. Data Conclusion Radiomic signatures from T2WI and T2WI with DWI may provide added value for pretreatment risk assessment and for guiding tailored treatment strategies in CC.

Objective. The prognostic role of adiposity in uterine cervical cancer (CC) is largely unknown. Abdominal fat distribution may better reflect obesity than body mass index. This study aims to describe computed tomography (CT)-assessed abdominal fat distribution in relation to clinicopathologic characteristics, survival, and tumor gene expression in CC.Methods. The study included 316 CC patients diagnosed during 2004-2017 who had pre-treatment abdomi-nal CT. CT-based 3D segmentation of total-, subcutaneous-and visceral abdominal fat volumes (TAV, SAV and VAV) allowed for calculation of visceral fat percentage (VAV% = VAV/TAV). Liver density (LD) and waist circum-ference (at L3/L4-level) were also measured. Associations between CT-derived adiposity markers, clinicopatho-logic characteristics and disease-specific survival (DSS) were explored. Gene set enrichment of primary tumors were examined in relation to fat distribution in a subset of 108 CC patients.Results. High TAV, VAV and VAV% and low LD were associated with higher age (& GE;44 yrs.; p & LE; 0.017) and high International Federation of Gynecology and Obstetrics (FIGO) (2018) stage (p & LE; 0.01). High VAV% was the only CT-marker predicting high-grade histology (p = 0.028), large tumor size (p = 0.016) and poor DSS (HR 1.07, p < 0.001). Patients with high VAV% had CC tumors that exhibited increased inflammatory signaling (false dis-covery rate [FDR] < 5%).Conclusions. High VAV% is associated with high-risk clinical features and predicts reduced DSS in CC patients. Furthermore, patients with high VAV% had upregulated inflammatory tumor signaling, suggesting that the met-abolic environment induced by visceral adiposity contributes to tumor progression in CC.& COPY; 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).

This study presents the diagnostic performance of four different preoperative imaging workups (IWs) for prediction of lymph node metastases (LNMs) in endometrial cancer (EC): pelvic MRI alone (IW1), MRI and [18F]FDG-PET/CT in all patients (IW2), MRI with selective [18F]FDG-PET/CT if high-risk preoperative histology (IW3), and MRI with selective [18F]FDG-PET/CT if MRI indicates FIGO stage ≥ 1B (IW4). In 361 EC patients, preoperative staging parameters from both pelvic MRI and [18F]FDG-PET/CT were recorded. Area under receiver operating characteristic curves (ROC AUC) compared the diagnostic performance for the different imaging parameters and workups for predicting surgicopathological FIGO stage. Survival data were assessed using Kaplan-Meier estimator with log-rank test. MRI and [18F]FDG-PET/CT staging parameters yielded similar AUCs for predicting corresponding FIGO staging parameters in low-risk versus high-risk histology groups (p ≥ 0.16). The sensitivities, specificities, and AUCs for LNM prediction were as follows: IW1—33

Introduction/Background The aim of this study was to explore abdominal fat distribution markers from computed tomography (CT) in relation to clinicopathologic characteristics and patient outcome in uterine cervical cancer (CC). By unravelling possible links between fat distribution profiles and altered tumour signalling pathways, potential molecular targets for treatment based on body composition profiles may be identified, which may enable more individualized treatment strategies in CC. Methodology The study included 316 CC patients diagnosed during 2004–2017 who had pre-treatment abdominal CT scans. CT images were analysed to quantify total abdominal fat volume (TAV), subcutaneous abdominal fat volume (SAV), visceral abdominal fat volume (VAV), visceral fat percentage (VAV% = VAV/TAV x100), liver density (LD) and waist circumference (WC). CT morphometric markers were explored in relation to clinicopathologic characteristics and disease-specific survival (DSS), and to gene expression profiles (L1000 mRNA) in a subset of 108 patients. Results High TAV, VAV and VAV% and low LD were all associated with high (≥44 years) patient age (p≤0.017) and high International Federation of Gynaecology and Obstetrics (FIGO) (2018) stage (p≤0.01). High VAV% was the only CT marker predicting high-grade histology (p=0.028), large tumour size (p=0.016) and poor DSS (HR 1.06, p<0.001). VAV% was strongly positively correlated with age (r=0.68, p<0.001) and VAV (r=0.65, p<0.001). Patients with high VAV% had CC tumours with enrichment of gene sets (false discovery rate [FDR] <5%) related to inflammatory signalling with 65% (13/20) of the top ranked Gene Ontology gene sets related to interferon signalling, viral- or immune response. Conclusion High VAV% is associated with high-risk clinical features and predicts reduced disease-specific survival in CC patients. CC patients with high VAV% have tumours with upregulated genes involved in inflammatory signalling, suggesting that the metabolic environment induced by visceral adiposity influences the regulatory signalling pathways relevant for tumour progression in CC.

Background & aims: Visceral adipose tissue (VAT) volume is associated with common lifestyle diseases. Dietary quality, including food matrix and degree of carbohydrate cellularity, as well as the carbohydrate/ fat ratio, may influence VAT volume. We aimed to determine the effects of isocaloric diets differing in either "cellularity", a novel marker of dietary carbohydrate quality, or carbohydrate amount on visceral fat volume and anthropometric measures in adults with obesity.Methods: In a randomized controlled trial of 193 people with obesity/central adiposity, we compared changes in VAT volume after 6 and 12 months, measured by abdominal computed tomography, on three isocaloric eating patterns based on "acellular" carbohydrate sources (e.g., flour-based whole-grain products; comparator arm), "cellular" carbohydrate sources (minimally processed foods with intact cellular structures such as fruits, potatoes/tubers, and rice), or low-carbohydrate high-fat (LCHF) principles. Outcomes were compared by an intention-to-treat (ITT) analysis using constrained linear mixedeffects modelling (cLMM) providing baseline-adjusted change scores and proper missing data handling without imputation.Results: 78 and 57 participants completed 6 and 12 months, respectively, with similar intakes of energy (females: 1820-2060 kcal, males: 2480-2550 kcal) and protein (16-17 energy percent, E%) throughout the intervention, and only modest reductions in energy from baseline. Reported dietary intakes were 42 -44, 41-42, and 11-15 E% carbohydrate and 36-38, 37-38, and 66-70 E% fat in the acellular, cellular and LCHF groups, respectively. There were no significant between-group differences in VAT volume after 6 months (cellular vs. acellular [95% CI]: -55 cm3 [-545, 436]; LCHF vs. acellular [95% CI]: -225 cm3 [-703, 253]) or after 12 months (cellular vs. acellular [95% CI]: -122 cm3 [-757, 514]; LCHF vs. acellular [95% CI]: -317 cm3 [-943, 309]). VAT volume decreased significantly within all groups by 14-18% and 12 -17% after 6 and 12 months, respectively. Waist circumference was reduced to a significantly greater degree in the LCHF vs. acellular group at 6 months (LCHF vs. acellular [95% CI]: -2.78 cm [-5.54, -0.017]).Conclusions: Despite modest energy restriction, the three isocaloric eating patterns, differing in carbohydrate cellularity and amount, decreased visceral fat volume significantly and to a similar clinically relevant degree.Clinical trials identifier: NCT03401970. https://clinicaltrials.gov/ct2/show/NCT03401970. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Objective To investigate the relationship between obesity and sarcopenia in relation to overall survival (OS) and disease-specific survival (DSS) in high-grade endometrial cancer patients. Methods We conducted a retrospective study in women diagnosed with high-grade endometrial cancer (EC) between February 2006 and August 2017 in the Royal Cornwall Hospital who had abdominal computerized tomography (CT)-scan as part of routine staging work-up. Sarcopenia was assessed by measuring psoas-, paraspinal- and abdominal wall muscles on CT and defined by skeletal muscle index <= 41 cm(2)/m(2). Sarcopenic obesity was defined as sarcopenia combined with body mass index (BMI) >= 30 kg/m(2). Results A total of 176 patients with median age of 70 years and median BMI of 29.4 kg/m(2) were included in the study. The majority of patients (38%) had endometrioid type histology. Sarcopenia was not associated with OS (P = 0.951) or DSS (P = 0.545) However, in multivariate analysis, sarcopenic obesity was associated with reduced OS in endometrioid endometrial cancer (EEC) patients (P = 0.048). Conclusion Sarcopenic obesity is associated with OS in high-grade EEC patients, while sarcopenia without obesity is not related to OS or DSS in high-grade EC. In non-endometrioid endometrial cancer, there is no association between sarcopenic obesity and survival.

Preoperative MR imaging in endometrial cancer patients provides valuable information on local tumor extent, which routinely guides choice of surgical procedure and adjuvant therapy. Furthermore, whole-volume tumor analyses of MR images may provide radiomic tumor signatures potentially relevant for better individualization and optimization of treatment. We apply a convolutional neural network for automatic tumor segmentation in endometrial cancer patients, enabling automated extraction of tumor texture parameters and tumor volume. The network was trained, validated and tested on a cohort of 139 endometrial cancer patients based on preoperative pelvic imaging. The algorithm was able to retrieve tumor volumes comparable to human expert level (likelihood-ratio test, p = 0.06 ). The network was also able to provide a set of segmentation masks with human agreement not different from inter-rater agreement of human experts (Wilcoxon signed rank test, p=0.08 , p=0.60 , and p=0.05 ). An automatic tool for tumor segmentation in endometrial cancer patients enables automated extraction of tumor volume and whole-volume tumor texture features. This approach represents a promising method for automatic radiomic tumor profiling with potential relevance for better prognostication and individualization of therapeutic strategy in endometrial cancer.

The purpose of this study was to establish a gene signature that may predict CIN3 regression and that may aid in selecting patients who may safely refrain from conization. Oncomine mRNA data including 398 immune-related genes from 21 lesions with confirmed regression and 28 with persistent CIN3 were compared. L1000 mRNA data from a cervical cancer cohort was available for validation (n = 239). Transcriptomic analyses identified TDO2 (p = 0.004), CCL5 (p < 0.001), CCL3 (p = 0.04), CD38 (p = 0.02), and PRF1 (p = 0.005) as upregulated, and LCK downregulated (p = 0.01) in CIN3 regression as compared to persistent CIN3 lesions. From these, a gene signature predicting CIN3 regression with a sensitivity of 91% (AUC = 0.85) was established. Transcriptomic analyses revealed proliferation as significantly linked to persistent CIN3. Within the cancer cohort, high regression signature score associated with immune activation by Gene Set enrichment Analyses (GSEA) and immune cell infiltration by histopathological evaluation (p < 0.001). Low signature score was associated with poor survival (p = 0.007) and large tumors (p = 0.01). In conclusion, the proposed six-gene signature predicts CIN regression and favorable cervical cancer prognosis and points to common drivers in precursors and cervical cancer lesions.

Prognostication is critical for accurate diagnosis and tailored treatment in endometrial cancer (EC). We employed radiogenomics to integrate preoperative magnetic resonance imaging (MRI, n = 487 patients) with histologic-, transcriptomic- and molecular biomarkers ( n = 550 patients) aiming to identify aggressive tumor features in a study including 866 EC patients. Whole-volume tumor radiomic profiling from manually (radiologists) segmented tumors ( n = 138 patients) yielded clusters identifying patients with high-risk histological features and poor survival. Radiomic profiling by a fully automated machine learning (ML)-based tumor segmentation algorithm ( n = 336 patients) reproduced the same radiomic prognostic groups. From these radiomic risk-groups, an 11-gene high-risk signature was defined, and its prognostic role was reproduced in orthologous validation cohorts ( n = 554 patients) and aligned with The Cancer Genome Atlas (TCGA) molecular class with poor survival (copy-number-high/p53-altered). We conclude that MRI-based integrated radiogenomics profiling provides refined tumor characterization that may aid in prognostication and guide future treatment strategies in EC.

Background: Obesity is an important risk factor for the development of endometrial cancer (EC). Recent data showed that body fat distribution might be more relevant than Body Mass Index (BMI). High visceral fat percentage was shown to be an independent predictor for survival in EC, but mainly included grade 12 EC. Objective: To evaluate body fat distribution and its relation to outcome in high-grade endometrial cancer. Methods: Retrospective study in women diagnosed with high-grade EC between February 2006 and August 2017 at the Royal Cornwall Hospital who had abdominal CT-scan as part of routine diagnostic work-up. Subcutaneous abdominal fat volumes and visceral abdominal fat volumes were quantified based on CT-scan measurements, and visceral fat percentage calculated. Results: A total of 176 patients with high-grade EC were included. The median age was 70 years and median BMI was 29.4 kg/m(2). The majority of patients had non-endometrioid endometrial cancer (NEEC; 62 %). High visceral fat percentage was associated with poor overall- and disease-specific survival (p = 0.006 and p = 0.026 respectively) in NEEC patients, but not in high-grade endometrioid EC (EEC). The most frequent obesity comorbidities hypertension and diabetes mellitus were significantly associated with high BMI and high visceral fat percentage. Conclusion: In high-grade EC, high visceral fat percentage was an independent predictor of poor survival only in NEEC. The strong correlation between high visceral fat and obesity-related comorbidities might be reflective of an unhealthy macroenvironment. (C) 2020 Published by Elsevier B.V.

Integrative tumor characterization linking radiomic profiles to corresponding gene expression profiles has the potential to identify specific genetic alterations based on non-invasive radiomic profiling in cancer. The aim of this study was to develop and validate a radiomic prognostic index (RPI) based on preoperative magnetic resonance imaging (MRI) and assess possible associations between the RPI and gene expression profiles in endometrial cancer patients. Tumor texture features were extracted from preoperative 2D MRI in 177 endometrial cancer patients. The RPI was developed using least absolute shrinkage and selection operator (LASSO) Cox regression in a study cohort (n = 95) and validated in an MRI validation cohort (n = 82). Transcriptional alterations associated with the RPI were investigated in the study cohort. Potential prognostic markers were further explored for validation in an mRNA validation cohort (n = 161). The RPI included four tumor texture features, and a high RPI was significantly associated with poor disease-specific survival in both the study cohort (p < 0.001) and the MRI validation cohort (p = 0.030). The association between RPI and gene expression profiles revealed 46 significantly differentially expressed genes in patients with a high RPI versus a low RPI (p < 0.001). The most differentially expressed genes, COMP and DMBT1, were significantly associated with disease-specific survival in both the study cohort and the mRNA validation cohort. In conclusion, a high RPI score predicts poor outcome and is associated with specific gene expression profiles in endometrial cancer patients. The promising link between radiomic tumor profiles and molecular alterations may aid in developing refined prognostication and targeted treatment strategies in endometrial cancer.

Background In endometrioid endometrial cancer (EEC), current clinical algorithms do not accurately predict patients with lymph node metastasis (LNM), leading to both under- and over-treatment. We aimed to develop models that integrate protein data with clinical information to identify patients requiring more aggressive surgery, including lymphadenectomy. Methods Protein expression profiles were generated for 399 patients using reverse-phase protein array. Three generalised linear models were built on proteins and clinical information (model 1), also with magnetic resonance imaging included (model 2), and on proteins only (model 3), using a training set, and tested in independent sets. Gene expression data from the tumours were used for confirmatory testing. Results LNM was predicted with area under the curve 0.72–0.89 and cyclin D1; fibronectin and grade were identified as important markers. High levels of fibronectin and cyclin D1 were associated with poor survival ( p = 0.018), and with markers of tumour aggressiveness. Upregulation of both FN1 and CCND1 messenger RNA was related to cancer invasion and mesenchymal phenotype. Conclusions We demonstrate that data-driven prediction models, adding protein markers to clinical information, have potential to significantly improve preoperative identification of patients with LNM in EEC.