Kari S. Wagner-Larsen’s research while affiliated with University of Bergen and other places

Pelvic MRI is essential for evaluating local and regional tumor extent in uterine cervical cancer (CC). Tumor microstructure captured by diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) markers may be closely linked to prognosis in CC. Purpose To explore whether primary tumor ADC markers predict survival in CC. Material and methods CC patients ( n = 179) diagnosed during 2009–2020 with MRI-assessed primary maximum tumor size ≥ 2 cm were included in this retrospective single-center study. Two radiologists read all MRIs independently, measuring mean tumor ADC values in manually drawn regions of interest (ROIs) and mean tumor ADC (tumor ADCmean ) from five measurements for the two readers was used. ADC from ROIs in the myometrium (myometrium ADC ), cervical stroma (cervix ADC ), and bladder (bladder ADC ) were used to calculate ADC ratios. ADC markers were explored in relation to the International Federation of Gynecology and Obstetrics (FIGO) (2018) stage, disease-specific survival (DSS), and recurrence/progression-free survival (RPFS). Results Inter-reader agreement for all ADC measurements was high (ICC:0.59–0.79). Low tumor ADCmean predicted advanced FIGO stage ( P = 0.04) and reduced DSS (hazard ratio (HR): 0.96, P < 0.001; AIC: 441). Myometrium ADC /tumor ADCmean yielded the best Cox regression fit (AIC = 430) among all tumor ADC markers. Patients with high myometrium ADC /tumor ADCmean had significantly reduced 5-year DSS for FIGO stage I, II, and III ( P = 0.01, 0.004, and 0.02, respectively) and tended to the same for FIGO IV ( P = 0.22). Conclusion Low tumor ADCmean predicted reduced DSS in CC. High myometrium ADC /tumor ADCmean was the strongest ADC predictor of poor DSS and a marker of high-risk phenotype independent of FIGO stage.

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.

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.

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).

Objectives 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. Material and methods 1.5 T and 3 T, T1-weighted MRI 2 minutes 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. Patient clustering into two groups based on radiomic profile. Impact of MRI scanning parameters on cluster composition and prognostication by cluster groups were analyzed using Kruskal-Wallis tests, Kaplan-Meier plots, log-rank test and random survival forest time-dependent area under curve (tdAUC) (α = 0.05). Results 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 forest; z-score: mean tdAUC = 0.77/0.78; LRM: mean tdAUC = 0.80/0.75). Conclusions 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 associated with similar DSS in EC and CC patients.

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) ( n T = 89)/validation (V) ( n V = 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 (T2 rad ) and T2WI with DWI (T2 + DWI rad ) 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 (MAX size ), 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 T2 rad and T2 + DWI rad yielded AUC T /AUC V 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 MAX size (AUC T /AUC V : 0.69/0.65) and FIGO (AUC T /AUC V : 0.77/0.64) and were significant predictors of DSS after adjusting for FIGO (HR T /HR V for T2 rad : 4.0/2.5 and T2 + DWI rad : 4.8/2.1). Adding T2 rad and T2 + DWI rad to FIGO significantly improved DSS prediction compared to FIGO alone in cohort (T) (AUC T 0.86 and 0.88 vs. 0.77), and FIGO with T2 + DWI rad tended to the same in cohort (V) (AUC V 0.75 vs. 0.64, p = 0.07). High radiomic score for T2 + DWI rad 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.

Introduction/Background Pelvic magnetic resonance imaging (MRI) is an important part of primary diagnostic workup in cervical cancer (CC), with MRI-assessed tumour size and pelvic tumour extent routinely guiding treatment decisions. Extraction of MRI-derived radiomic tumour features could improve cancer prognostics and may also reveal novel targets for treatment. Methodology We manually segmented 3D volumes in 132 primary CC tumours and extracted 293 whole-volume radiomic MRI features. Unsupervised hierarchical clustering yielded three distinct patient clusters (Cluster 1 [n=52]; 2 [n=46]; and 3 [n=34]). Overlapping clinicopathologic, genomic (whole exome sequencing, n=65), transcriptomic (L1000 arrays, n=73) and molecular biomarker (n=84) data were utilized to characterize each cluster. Results Patients in Cluster 2 and 3 had significantly reduced disease-specific survival (DSS) (hazard rate [HR]: 3.33; p=0.008) compared with patients in Clusters 1, even after adjusting for International Federation of Gynecology and Obstetrics (FIGO) 2018 stage and age (adjusted HR: 2.51; p=0.045). Cluster 3 tumours associate with high stage (p<0.001), large tumours (p<0.001), squamous histology (p=0.015), p53 negative or -overexpressing tumours (p=0.04) and aberrant TP53, -MYC and -MTORC1 signalling. The intermediate-risk Cluster 2 associates with increased and aberrant cell cycle- and Hippo signalling, suggesting CDK2/4 and YAP-TEAD inhibitors as plausible treatment options. The low-risk Cluster 1 associates with increased immune cell signalling. Conclusion This study links radiomic signatures to distinct genomic profiles that may potentially aid in prognostication and tailoring of treatments and follow-up plans for cervical cancer patients. Disclosures The authors declare no conflict interests.

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 abdominal 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 circumference (at L3/L4-level) were also measured. Associations between CT-derived adiposity markers, clinicopathologic 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 (≥44 yrs.; p ≤ 0.017) and high International Federation of Gynecology and Obstetrics (FIGO) (2018) stage (p ≤ 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 discovery 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 metabolic environment induced by visceral adiposity contributes to tumor progression in CC.

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. • Download figure • Open in new tab • Download powerpoint Abstract 2022-RA-1383-ESGO Figure 1 High visceral fat percentage is linked to upregulated inflammatory tumour signalling and predicts poor outcome in uterine cervical cancer. (A) Time-dependent receiver operating characteristic (tdROC) curves for predicting disease-specific survival (DSS) at 5 years after diagnosis based on visceral abdominal fat percentage (VAV%), visceral abdominal fat volume (VAV), total abdominal fat volume (TAV) and subcutaneous abdominal fat volume (SAV). VAV% yielded significantly higher AUC (0.75) than the other morphometric makers (P<0.001 for all). B) Kaplan-Meier plot depicting significantly reduced DSS in patients with VAV%≥29 compared with patients with VAV%<29 (p<0.001). C) Abdominal compared lomography (CT) scans with segmentation of visceral and subcutaneous fat compartments carcinoma, international federation of gynaecology and obstetrics (FIGO) (2018) stage III. Patient I, aged 61 yrs, who had low VAV% (23%) received primary radiation therapy and subsequent chemotherapy with cisplatin. She developed pelvic metastases and died from cervical cancer 14 months after primary treatment. D) Gene set enrichment analysis (GSEA) revealed that patients with VAV%>29 had tumours exhibiting upregulated signalling pathways for gene sets involved in inflammatory signalling and immune response (shown in green) 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.