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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 FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Simple Summary: Through implementation of HPV testing as standard primary screening method, the number of women diagnosed with high-grade cervical intraepithelial neoplasia (CIN2-3) has increased. Although only one third of CIN3 will progress to cancer, conization is standard treatment in high-income countries. The aim of this study was to identify tools with which to predict CIN regression relevant for individualizing treatment within this patient group. We compared the transcriptomic immune-profile from 21 lesions with confirmed regression and 28 lesions with confirmed persistent CIN3. A gene signature with high sensitivity to identify CIN3 lesions that regressed during follow-up was identified. When tested in a cervical cancer cohort (n = 239) with available transcriptomic data, a high regression signature score was associated with favorable survival, small tumors, and immune infiltration. This study presents a gene signature with the capacity to predict CIN regression, that may potentially guide treatment, and identifies common disease drivers in CIN and cervical 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. [ABSTRACT FROM AUTHOR]

Assess the added prognostic value of the updated International Federation of Gynecology and Obstetrics (FIGO) 2018 staging system, and to identify clinicopathological and radiological biomarkers for improved FIGO 2018 prognostication. Patient data were retrieved from a prospectively collected patient cohort including all consenting patients with cervical cancer diagnosed and treated at Haukeland University Hospital during 2001–2022 (n = 948). All patients were staged according to the FIGO 2009 and FIGO 2018 guidelines based on available data for individual patients. MRI-assessed maximum tumor diameter and stromal tumor invasion, as well as histopathologically assessed lymphovascular space invasion were applied to categorize patients according to the Sedlis criteria. FIGO 2018 stage yielded the highest area under the receiver operating characteristic (ROC) curve (AUC) (0.86 versus 0.81 for FIGO 2009) for predicting disease-specific survival. The most common stage migration in FIGO 2018 versus FIGO 2009 was upstaging from stages IB/II to stage IIIC due to suspicious lymph nodes identified by PET/CT and/or MRI. In FIGO 2018 stage III patients, extent and size of primary tumor (p = 0.04), as well as its histological type (p = 0.003) were highly prognostic. Sedlis criteria were prognostic within FIGO 2018 IB patients (p = 0.04). Incorporation of cross-sectional imaging increases prognostic precision, as suggested by the FIGO 2018 guidelines. The 2018 FIGO IIIC stage could be refined by including the size and extent of primary tumor and histological type. The FIGO IB risk prediction could be improved by applying MRI-assessed tumor size and stromal invasion. [Display omitted] • FIGO 2018 improves cervical cancer risk classification. • The 2018 FIGO IIIC stage could be refined by including the size and extent of primary tumor. • Preoperative MRI-assessed tumor size and stromal invasion is prognostic and may guide treatment for FIGO IB patients. • Adenocarcinoma is not prognostic in FIGO I and II tumors, yet highly prognostic in FIGO III tumors. [ABSTRACT FROM AUTHOR]

Background: Cervical cancer is a major health problem worldwide. Identification of effective clinicopathologic and molecular markers is vital to improve treatment stratification.Objectives: The purpose of this study was to validate a set of well-defined clinicopathologic features in a large population-based, prospectively collected cervical cancer cohort to support their use in the clinic. Further, we explored p53 and human epidermal growth factor receptor 2 as potential prognostic markers in cervical cancer.Study Design: Tissue was collected from 401 patients with cervical cancer. Clinical data that included follow-up evaluations were collected from patient journals. Histopathologic data were evaluated and revised by an expert pathologist. The prognostic impact of selected clinicopathologic variables was analyzed in the whole cohort. Tissue microarrays were prepared from 292 carcinomas, and p53 and human epidermal growth factor receptor 2 protein levels were evaluated by immunohistochemistry. Fresh frozen samples from overlapping cervical carcinomas previously were subjected to human papilloma virus typing (n=94), whole exome (n=100) and RNA (n=79) sequencing; the results were available for our analyses.Results: Among the clinicopathologic variables, vascular space invasion, histologic type, and tumor size were verified as strong independent prognostic markers. High p53 protein levels were associated significantly with markers for aggressive phenotype and survival, also in multivariate survival analysis, but did not reflect TP53 mutational status. High human epidermal growth factor receptor 2 protein levels were identified in 21% of all tumors. ERBB2 amplification was associated with poor outcome (P=.003); human epidermal growth factor receptor 2 protein level was not.Conclusions: Our findings support that the Féderation Internationale de Gynécologie et d'Obstétrique s guidelines should include vascular space invasion and tumor size 2-4 cm and that careful selection of histologic type is essential for stratification of patient risk groups. High p53 levels independently predict poor survival yet do not reflect mutational status in cervical cancer. Amplified ERBB2 significantly links to poor survival, while HercepTest does not. With optimal stratification, human epidermal growth factor receptor 2-based therapy may improve cervical cancer treatment. [ABSTRACT FROM AUTHOR]