Uterine cervical cancer (CC) is the most common gynecologic malignancy worldwide. Whole-volume radiomic profiling from pelvic MRI may yield prognostic markers for tailoring treatment in CC. However, radiomic profiling relies on manual tumor segmentation which is unfeasible in the clinic. We present a fully automatic method for the 3D segmentation of primary CC lesions using state-of-the-art deep learning (DL) techniques. In 131 CC patients, the primary tumor was manually segmented on T2-weighted MRI by two radiologists (R1, R2). Patients were separated into a train/validation (n = 105) and a test- (n = 26) cohort. The segmentation performance of the DL algorithm compared with R1/R2 was assessed with Dice coefficients (DSCs) and Hausdorff distances (HDs) in the test cohort. The trained DL network retrieved whole-volume tumor segmentations yielding median DSCs of 0.60 and 0.58 for DL compared with R1 (DL-R1) and R2 (DL-R2), respectively, whereas DSC for R1-R2 was 0.78. Agreement for primary tumor volumes was excellent between raters (R1-R2: intraclass correlation coefficient (ICC) = 0.93), but lower for the DL algorithm and the raters (DL-R1: ICC = 0.43; DL-R2: ICC = 0.44). The developed DL algorithm enables the automated estimation of tumor size and primary CC tumor segmentation. However, segmentation agreement between raters is better than that between DL algorithm and raters.

Background: Tumor size assessment by MRI is central for staging uterine cervical cancer. However, the optimal role of MRI-derived tumor measurements for prognostication is still unclear. Material and methods: This retrospective cohort study included 416 women (median age: 43 years) diagnosed with cervical cancer during 2002–2017 who underwent pretreatment pelvic MRI. The MRIs were independently read by three radiologists, measuring maximum tumor diameters in three orthogonal planes and maximum diameter irrespective of plane (MAX_imaging). Inter-reader agreement for tumor size measurements was assessed by intraclass correlation coefficients (ICCs). Size was analyzed in relation to age, International Federation of Gynecology and Obstetrics (FIGO) (2018) stage, histopathological markers, and disease-specific survival using Kaplan–Meier-, Cox regression-, and time-dependent receiver operating characteristics (tdROC) analyses. Results: All MRI tumor size variables (cm) yielded high areas under the tdROC curves (AUCs) for predicting survival (AUC 0.81–0.84) at 5 years after diagnosis and predicted outcome (hazard ratios [HRs] of 1.42–1.76, p < 0.001 for all). Only MAX_imaging independently predicted survival (HR = 1.51, p = 0.03) in the model including all size variables. The optimal cutoff for maximum tumor diameter (≥ 4.0 cm) yielded sensitivity (specificity) of 83% (73%) for predicting disease-specific death after 5 years. Inter-reader agreement for MRI-based primary tumor size measurements was excellent, with ICCs of 0.83–0.85. Conclusion: Among all MRI-derived tumor size measurements, MAX_imaging was the only independent predictor of survival. MAX_imaging ≥ 4.0 cm represents the optimal cutoff for predicting long-term disease-specific survival in cervical cancer. Inter-reader agreement for MRI-based tumor size measurements was excellent.

: 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: 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.

Despite several clinical trials, treatment with corticosteroids in patients with Immunoglobulin A nephropathy (IgAN) remains controversial [ 1, 2]. The recently updated KDIGO guidelines suggests that treatment with corticosteroids could be considered in patients with persistent proteinuria above 0.75 to 1.0 g per day in patients with estimated glomerular filtration rate (eGFR) above 30 mL/min/1.73 m² despite intensive supportive care [ 3]. This is a more liberal approach compared with the 2012 guidelines, which suggested that patients selected for treatment with steroids should have eGFR above 50 mL/min/1.73 m² [ 4]. It could be valuable to identify clinicians decision on when to initiate immunosuppressive treatment, since this could have implication on how future therapeutics might be used in patients with IgAN [ 5]. Our aim is to describe the use of corticosteroids and evaluate the effect of this treatment using a Norwegian cohort of patients with IgAN. We identified patients with biopsy-proven IgAN from the Norwegian Kidney Biopsy Registry and the Norwegian Renal Registry, who all had progressed to end-stage renal disease (ESRD) and collected clinical, treatment and biochemical data from patient records. A total of 151 patients with a diagnostic kidney biopsy performed between 1988 and 2010 were included. The median time from biopsy to ESRD was 5 years. A majority of 94% of the patients received supportive treatment with Renin–Angiotensin–Aldosterone System blockade. A total of 40 patients (27%) received treatment with corticosteroids, with a median eGFR and proteinuria at time of biopsy at 49.5 mL/min/1.73 m² and 5.1 g per day, respectively. Median time from biopsy to steroid treatment was 12 months. Patients treated with corticosteroids had a significant decrease in proteinuria from 5.6 to 3 g per day (P = .02), but there was no difference in time to progression to ESRD between the two groups (P = .58). The vast majority (87.5%) of the patients treated with corticosteroids reported on adverse events. We found that the use of corticosteroids had significant impact on proteinuria but did not delay progression to ESRD. Adverse events were common in patients treated with corticosteroids. The patients were treated according to the current guidelines

Magnetic resonance imaging (MRI) has emerged as an important part of the diagnostic work-up in cervical cancer, with tumor volume and extent as important prognosticators. MRI radiomic tumor features may aid in prognostication and tailoring of treatment in cervical cancer. We extracted whole-volume radiomic texture features from 124 manually segmented tumors and performed unsupervised clustering yielding two distinct clusters. Overlapping clinicopathologic, genomic (whole exome sequencing, n=61), transcriptomic (L1000 arrays, n= 65) and molecular biomarker (n=82) data were applied to characterize the clusters. Independent of tumor size parameters, patients in cluster II had significantly reduced disease-specific survival as compared to those in cluster I (p>0.001), also within squamous cell carcinomas (n=96, p<0.001). Cluster II associated with high age (p=0.02), high FIGO-2009 stage (p<0.001), high BMI (p=0.03) and PR negative tumors (p=0.004). Distinct mutational and copy-number profiles were detected for the two clusters. By gene set enrichment analyses, gene sets associated to immune activation were enriched in cluster I tumors, whilst gene sets associated with oxidative phosphorylation and epithelial to mesenchymal transition associated to cluster II tumors. This study links radiomic signatures to distinct genomic profiles that may potentially aid in prognostication and tailoring of treatments in cervical cancer patients. Citation Format: Mari Kyllesø Halle, Erlend Hodneland, Erling Hoivik, Kari Wagner-Larsen, Njål G. Lura, Julie Dybvik, David Forsse, Bjørn I. Bertelsen, Camilla Krakstad, Ingfrid S. Haldorsen, Olivera Bozickovic, Kathrine Woie. Radiomic profiles revealing targets for therapy in cervical cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 513.