Radiomic profiles improve prognostication and reveal targets for therapy in cervical cancer.

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).

Hyperpolarized [1-13C]-pyruvate MRS evaluates immune potential and predicts response to radiotherapy in cervical cancer.

BACKGROUND: Monitoring pyruvate metabolism in the spleen is important for assessing immune activity and achieving successful radiotherapy for cervical cancer due to the significance of the abscopal effect. We aimed to explore the feasibility of utilizing hyperpolarized (HP) [1-13C]-pyruvate magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) to evaluate pyruvate metabolism in the human spleen, with the aim of identifying potential candidates for radiotherapy in cervical cancer. METHODS: This prospective study recruited six female patients with cervical cancer (median age 55 years; range 39-60) evaluated using HP [1-13C]-pyruvate MRI/MRS at baseline and 2 weeks after radiotherapy. Proton (1H) diffusion-weighted MRI was performed in parallel to estimate splenic cellularity. The primary outcome was defined as tumor response to radiotherapy. The Student t-test was used for comparing 13C data between the groups. RESULTS: The splenic HP [1-13C]-lactate-to-total carbon (tC) ratio was 5.6-fold lower in the responders than in the non-responders at baseline (p = 0.009). The splenic [1-13C]-lactate-to-tC ratio revealed a 1.7-fold increase (p = 0.415) and the splenic [1-13C]-alanine-to-tC ratio revealed a 1.8-fold increase after radiotherapy (p = 0.482). The blood leukocyte differential count revealed an increased proportion of neutrophils two weeks following treatment, indicating enhanced immune activity (p = 0.013). The splenic apparent diffusion coefficient values between the groups were not significantly different. CONCLUSIONS: This exploratory study revealed the feasibility of HP [1-13C]-pyruvate MRS of the spleen for evaluating baseline immune potential, which was associated with clinical outcomes of cervical cancer after radiotherapy. TRIAL REGISTRATION: ClinicalTrials.gov NCT04951921 , registered 7 July 2021. RELEVANCE STATEMENT: This prospective study revealed the feasibility of using HP 13C MRI/MRS for assessing pyruvate metabolism of the spleen to evaluate the patients' immune potential that is associated with radiotherapeutic clinical outcomes in cervical cancer. KEY POINTS: • Effective radiotherapy induces abscopal effect via altering immune metabolism. • Hyperpolarized 13C MRS evaluates patients' immune potential non-invasively. • Pyruvate-to-lactate conversion in the spleen is elevated following radiotherapy.

Clinical feasibility of deep learning-based synthetic CT images from T2-weighted MR images for cervical cancer patients compared to MRCAT.

This work aims to investigate the clinical feasibility of deep learning-based synthetic CT images for cervix cancer, comparing them to MR for calculating attenuation (MRCAT). Patient cohort with 50 pairs of T2-weighted MR and CT images from cervical cancer patients was split into 40 for training and 10 for testing phases. We conducted deformable image registration and Nyul intensity normalization for MR images to maximize the similarity between MR and CT images as a preprocessing step. The processed images were plugged into a deep learning model, generative adversarial network. To prove clinical feasibility, we assessed the accuracy of synthetic CT images in image similarity using structural similarity (SSIM) and mean-absolute-error (MAE) and dosimetry similarity using gamma passing rate (GPR). Dose calculation was performed on the true and synthetic CT images with a commercial Monte Carlo algorithm. Synthetic CT images generated by deep learning outperformed MRCAT images in image similarity by 1.5% in SSIM, and 18.5 HU in MAE. In dosimetry, the DL-based synthetic CT images achieved 98.71% and 96.39% in the GPR at 1% and 1 mm criterion with 10% and 60% cut-off values of the prescription dose, which were 0.9% and 5.1% greater GPRs over MRCAT images.

Pelvic target volume inter-fractional motion during radiotherapy for cervical cancer with daily iterative cone beam computed tomography.

BACKGROUND: Tumor regression and organ movements indicate that a large margin is used to ensure target volume coverage during radiotherapy. This study aimed to quantify inter-fractional movements of the uterus and cervix in patients with cervical cancer undergoing radiotherapy and to evaluate the clinical target volume (CTV) coverage. METHODS: This study analyzed 303 iterative cone beam computed tomography (iCBCT) scans from 15 cervical cancer patients undergoing external beam radiotherapy. CTVs of the uterus (CTV-U) and cervix (CTV-C) contours were delineated based on each iCBCT image. CTV-U encompassed the uterus, while CTV-C included the cervix, vagina, and adjacent parametrial regions. Compared with the planning CTV, the movement of CTV-U and CTV-C in the anterior-posterior, superior-inferior, and lateral directions between iCBCT scans was measured. Uniform expansions were applied to the planning CTV to assess target coverage. RESULTS: The motion (mean ± standard deviation) in the CTV-U position was 8.3 ± 4.1 mm in the left, 9.8 ± 4.4 mm in the right, 12.6 ± 4.0 mm in the anterior, 8.8 ± 5.1 mm in the posterior, 5.7 ± 5.4 mm in the superior, and 3.0 ± 3.2 mm in the inferior direction. The mean CTV-C displacement was 7.3 ± 3.2 mm in the left, 8.6 ± 3.8 mm in the right, 9.0 ± 6.1 mm in the anterior, 8.4 ± 3.6 mm in the posterior, 5.0 ± 5.0 mm in the superior, and 3.0 ± 2.5 mm in the inferior direction. Compared with the other tumor (T) stages, CTV-U and CTV-C motion in stage T1 was larger. A uniform CTV planning treatment volume margin of 15 mm failed to encompass the CTV-U and CTV-C in 11.1% and 2.2% of all fractions, respectively. The mean volume change of CTV-U and CTV-C were 150% and 51%, respectively, compared with the planning CTV. CONCLUSIONS: Movements of the uterine corpus are larger than those of the cervix. The likelihood of missing the CTV is significantly increased due to inter-fractional motion when utilizing traditional planning margins. Early T stage may require larger margins. Personal radiotherapy margining is needed to improve treatment accuracy.

Exploratory study of using Magnetic resonance Prognostic Imaging markers for Radiotherapy In Cervix cancer (EMPIRIC): a prospective cohort study protocol.

INTRODUCTION: Radical chemoradiotherapy represents the gold standard for locally advanced cervical cancer. However, despite significant progress in improving local tumour control, distant relapse continues to impact overall survival. The development of predictive and prognostic biomarkers is consequently important to risk-stratify patients and identify populations at higher risk of poorer treatment response and survival outcomes. Exploratory study of using Magnetic resonance Prognostic Imaging markers for Radiotherapy In Cervix cancer (EMPIRIC) is a prospective exploratory cohort study, which aims to investigate the role of multiparametric functional MRI (fMRI) using diffusion-weighed imaging (DWI), dynamic contrast-enhanced (DCE) and blood oxygen level-dependent imaging (BOLD) MRI to assess treatment response and predict outcomes in patients undergoing radical chemoradiotherapy for cervical cancer. METHODS AND ANALYSIS: The study aims to recruit 40 patients across a single-centre over 2 years. Patients undergo multiparametric fMRI (DWI, DCE and BOLD-MRI) at three time points: before, during and at the completion of external beam radiotherapy. Tissue and liquid biopsies are collected at diagnosis and post-treatment to identify potential biomarker correlates against fMRI. The primary outcome is to evaluate sensitivity and specificity of quantitative parameters derived from fMRI as predictors of progression-free survival at 2 years following radical chemoradiotherapy for cervical cancer. The secondary outcome is to investigate the roles of fMRI as predictors of overall survival at 2 years and tumour volume reduction across treatment. Statistical analyses using regression models and survival analyses are employed to evaluate the relationships between the derived parameters, treatment response and clinical outcomes. ETHICS AND DISSEMINATION: The EMPIRIC study received ethical approval from the NHS Health Research Authority (HRA) on 14 February 2022 (protocol number RD2021-29). Confidentiality and data protection measures are strictly adhered to throughout the study. The findings of this study will be disseminated through peer-reviewed publications and scientific conferences, aiming to contribute to the growing body of evidence on the use of multiparametric MRI in cervical cancer management. TRIAL REGISTRATION NUMBER: NCT05532930.

Wavelet scattering transform and entropy features in fluorescence spectral signal analysis for cervical cancer diagnosis.

Cervical cancer is a prevalent malignant tumor within the female reproductive system and is regarded as a prominent cause of female mortality on a global scale. Timely and precise detection of various phases of cervical cancer holds the potential to substantially enhance both the rate of successful treatment and the duration of patient survival. Fluorescence spectroscopy is a highly sensitive method for detecting the biochemical changes that arise during cancer progression. In our study, fluorescence spectral data is collected from a diverse group of 110 subjects. The potential of the scattering transform technique for the purpose of cancer detection is explored. The processed signal undergoes an initial decomposition into scattering coefficients using the wavelet scattering transform (WST). Subsequently, the scattering coefficients are subjected to computation for fuzzy entropy, dispersion entropy, phase entropy, and spectral entropy, for effectively characterizing the fluorescence spectral signals. These combined features generated through the proposed approach are then fed to 1D convolutional neural network (CNN) classifier to classify them into normal, pre-cancerous, and cancerous categories, thereby evaluating the effectiveness of the proposed methodology. We obtained mean classification accuracy of 97% using 5-fold cross-validation. This demonstrates the potential of combining WST and entropic features for analyzing fluorescence spectroscopy signals using 1D CNN classifier that enables early cancer detection in contrast to prevailing diagnostic methods.

A Comparative Study of Deep Learning Dose Prediction Models for Cervical Cancer Volumetric Modulated Arc Therapy.

Purpose: Deep learning (DL) is widely used in dose prediction for radiation oncology, multiple DL techniques comparison is often lacking in the literature. To compare the performance of 4 state-of-the-art DL models in predicting the voxel-level dose distribution for cervical cancer volumetric modulated arc therapy (VMAT). Methods and Materials: A total of 261 patients' plans for cervical cancer were retrieved in this retrospective study. A three-channel feature map, consisting of a planning target volume (PTV) mask, organs at risk (OARs) mask, and CT image was fed into the three-dimensional (3D) U-Net and its 3 variants models. The data set was randomly divided into 80% as training-validation and 20% as testing set, respectively. The model performance was evaluated on the 52 testing patients by comparing the generated dose distributions against the clinical approved ground truth (GT) using mean absolute error (MAE), dose map difference (GT-predicted), clinical dosimetric indices, and dice similarity coefficients (DSC). Results: The 3D U-Net and its 3 variants DL models exhibited promising performance with a maximum MAE within the PTV 0.83% ± 0.67% in the UNETR model. The maximum MAE among the OARs is the left femoral head, which reached 6.95% ± 6.55%. For the body, the maximum MAE was observed in UNETR, which is 1.19 ± 0.86%, and the minimum MAE was 0.94 ± 0.85% for 3D U-Net. The average error of the Dmean difference for different OARs is within 2.5 Gy. The average error of V40 difference for the bladder and rectum is about 5%. The mean DSC under different isodose volumes was above 90%. Conclusions: DL models can predict the voxel-level dose distribution accurately for cervical cancer VMAT treatment plans. All models demonstrated almost analogous performance for voxel-wise dose prediction maps. Considering all voxels within the body, 3D U-Net showed the best performance. The state-of-the-art DL models are of great significance for further clinical applications of cervical cancer VMAT.

Dynamic contrast-enhanced magnetic resonance imaging in cervical cancer: correlation between quantitative parameters and molecular markers hypoxia-inducible factors-1-alpha, vascular endothelial growth factor, and Ki-67.

AIM: To investigate whether dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has the potential to non-invasively detect microenvironmental condition by quantitatively measuring blood perfusion, vessel wall permeability, and vascularity, and to elucidate the possible correlations between DCE-MRI quantitative parameters and the expression level of hypoxia, vascularity, and cell proliferation related molecular biomarkers. MATERIALS AND METHODS: In this prospective single center clinical study, 58 patients diagnosed with cervical cancer underwent DCE-MRI before anticancer treatment were enrolled. Ktrans, Kep, Ve, and Vp were generated from Extended Toft's model. Then patients conducted colposcopy biopsy within 1 week after DCE-MRI. Pretreatment expression levels of HIF-1α, VEGF and Ki-67 were assessed and scored by immunohistochemistry on colposcopy obtained tumor specimens. RESULTS: In HIF-1α low-expression group, Ktrans (p=0.031) and Kep (p=0.012) values were significantly higher than the high-expression group. In VEGF high-expression group, Ktrans (p=0.044) and Ve values (p=0.021) were significantly higher than the low-expression group. In Ki-67 high-expression group, Ktrans (p=0.026) and Kep (p=0.033) were significantly higher than the low-expression group. Multiple linear regression analyses and Pearson correlation revealed that Ktrans independently negatively correlated with HIF-1α expression, Ve independently positively correlated with VEGF, and Kep independently positively correlated with Ki-67. The area under the ROC curves of Ktrans for HIF-1α, Ve for VEGF, and Kep for Ki-67 were 0.728, 0.743, 0.730, respectively. CONCLUSION: Our results suggest that DCE-MRI quantitative parameters could be potentially used as imaging markers for non-invasively detecting microenvironmental hypoxia, vascularity and proliferation in cervical cancer patients.

[Application of optical coherence tomography in the evaluation of cervical lesions: a multicenter study].

Objective: To explore the value of optical coherence tomography (OCT) imaging system in evaluating cervical lesions in vivo. Methods: A total of 1 214 patients with cervical lesions were collected from January 2020 to December 2021 in the Third Affiliated Hospital of Zhengzhou University, Maternal and Chlid Heaith Hospital of Gushi County, Xinyang City, Henan Province, and Maternal and Chlid Heaith Hospital of Sui County, Shangqiu City, Henan Province. The age of the patients was (38.9±10.5) years (range: 16-77 years). All patients underwent in vivo cervical OCT examination and cervical biopsy pathology examination, and summarized the OCT image features of in vivo cervical lesions. Using the pathological diagnosis as the "gold standard", the accuracy, specificity, sensitivity, positive predictive value (PPV) and negative predictive value (NPV) of OCT image interpretation results were evaluated, as well as the consistency of OCT image diagnosis and pathological diagnosis. At the same time, the in vivo cervical OCT imaging system, as a newly developed screening tool, was compared with the traditional combined screening of human papillomavirus (HPV) and Thinprep cytologic test (TCT), to assess the screening effect. Results: By comparing the OCT images of the cervix in vivo with the corresponding HE images, the OCT image characteristics of the normal cervix and various types of cervical lesions in vivo were summarized. The accuracy, sensitivity, specificity, PPV and NPV of OCT image in the diagnosis of high-grade squamous intraepithelial lesion (HSIL) and above (HSIL+) were 93.4%, 88.5%, 95.0%, 85.0% and 96.2%, respectively. The accuracy, sensitivity, specificity, PPV and NPV of OCT for low-grade squamous intraepithelial lesion (LSIL) were 84.7%, 61.7%, 96.3%, 89.3% and 83.2%, respectively. The consistency between OCT image diagnosis and pathological diagnosis was strong (Kappa value was 0.701).The accuracy, sensitivity and specificity of OCT screening, HPV and TCT combined screening were 83.7% vs 64.9% (χ²=128.82, P<0.001), 77.8% vs 64.5% (χ²=39.01, P<0.001), 91.8% vs 65.4% (χ²=98.12, P<0.001), respectively. The differences were statistically significant. Conclusions: OCT imaging system has high sensitivity and specificity in the evaluation of cervical lesions in vivo, and has the characteristics of non-invasive, real-time and high efficiency. OCT examination is expected to become an effective method for the diagnosis of cervical lesions and cervical cancer screening.

Double-layer hollow mesoporous silica nanoparticles for ultrasound-guided photodynamic treatment.

Cervical carcinoma persists as a major global public health burden. While conventional therapeutic modalities inevitably cause ablation of adjacent non-tumorous tissues, photodynamic therapy (PDT) offers a targeted cytotoxic strategy through a photosensitizing agent (PS). However, the hydrophobicity and lack of selective accumulation of promising PS compounds such as zinc(II) phthalocyanine (ZnPc) impedes their clinical translation as standalone agents. The present study sought to incorporate ZnPc within double-layer hollow mesoporous silica nanoparticles (DHMSN) as nanocarriers to enhance aqueous dispersibility and tumor specificity. Owing to their compartmentalized design, the hollow mesoporous silica nanoparticles (HMSN) demonstrated enhanced ultrasonic imaging contrast. Combined with the vaporization of the perfluorocarbon perfluoropentane (PFP), the HMSN-encapsulated ZnPc enabled real-time ultrasound monitoring of PDT treatment.In vivo, the innate thermal energy induced vaporization of the DHMSN-carried PFP to significantly amplify ultrasound signals from the tumor site. Results demonstrated biocompatibility, efficient PFP microbubble generation, and robust photocatalytic activity. Collectively, this investigation establishes ultrasound-guided PDT utilizing multi-layer HMSN as a targeted therapeutic strategy for cervical malignancies with mitigated toxicity.

[18F]FDG-PET/CT-based risk stratification in women with locally advanced uterine cervical cancer.

BACKGROUND: [18F]FDG-PET/CT is used for staging and treatment planning in patients with locally advanced cervical cancer (LACC). We studied if a PET-based prediction model could provide additional risk stratification beyond International Federation of Gynaecology and Obstetrics (FIGO) staging in our population with LACC to aid treatment decision making. METHODS: In total, 183 patients with LACC treated with chemoradiation between 2013 and 2018 were included. Patients were treated according to FIGO 2009 and retrospectively reclassified according to FIGO 2018 staging system. After validation of an existing PET-based prediction model, the predicted recurrent free survival (RFS), disease specific survival (DSS) and overall survival (OS) at 1, 3, and 5 years, based on metabolic tumor volume (MTV), maximum standardized uptake value (SUVmax) and highest level of [18F]FDG-positive node was calculated. Then the observed survival was compared to the predicted survival. An area under the curve (AUC) close to or higher than 0.7 was considered adequate for accurate prediction. The Youden (J) index defined survival chance cutoff values for low and high risk groups. RESULTS: All AUC values for the comparison between predicted and observed outcomes were > 0.7 except for 5-year RFS and for 5-year OS which were close to 0.7 (0.684 and 0.650 respectively). Cutoff values for low and high risk survival chance were 0.44 for the 3-year RFS and 0.47 for the 5-year OS. The FIGO 2009 system could not differentiate between the risk profiles. After reclassification according to FIGO 2018, all patients with stage IIIC2 and IVB fell in the high risk and almost all patients with stages IB2-IIIB and IVA in the low risk group. In patients with stage IIIC1 disease the FIGO stage cannot discriminate between the risk profiles. CONCLUSIONS: Low and high risk patients with LACC can be identified with the PET-based prediction model. In particular patients with stage IIIC1 need additional risk stratification besides the FIGO 2018 staging. The Kidd model could be a useful tool to aid treatment decision making in these patients. Our results also support the choice of [18F]FDG-PET/CT imaging in patients with LACC.

Radiomics signature for dynamic changes of tumor-infiltrating CD8+ T cells and macrophages in cervical cancer during chemoradiotherapy.

BACKGROUND: Our previous study suggests that tumor CD8+ T cells and macrophages (defined as CD68+ cells) infiltration underwent dynamic and heterogeneous changes during concurrent chemoradiotherapy (CCRT) in cervical cancer patients, which correlated with their short-term tumor response. This study aims to develop a CT image-based radiomics signature for such dynamic changes. METHODS: Thirty cervical squamous cell carcinoma patients, who were treated with CCRT followed by brachytherapy, were included in this study. Pre-therapeutic CT images were acquired. And tumor biopsies with immunohistochemistry at primary sites were performed at baseline (0 fraction (F)) and immediately after 10F. Radiomics features were extracted from the region of interest (ROI) of CT images using Matlab. The LASSO regression model with ten-fold cross-validation was utilized to select features and construct an immunomarker classifier and a radiomics signature. Their performance was evaluated by the area under the curve (AUC). RESULTS: The changes of tumor-infiltrating CD8+T cells and macrophages after 10F radiotherapy as compared to those at baseline were used to generate the immunomarker classifier (AUC= 0.842, 95% CI:0.680-1.000). Additionally, a radiomics signature was developed using 4 key radiomics features to predict the immunomarker classifier (AUC=0.875, 95% CI:0.753-0.997). The patients stratified based on this signature exhibited significant differences in treatment response (p = 0.004). CONCLUSION: The radiomics signature could be used as a potential predictor for the CCRT-induced dynamic alterations of CD8+ T cells and macrophages, which may provide a less invasive approach to appraise tumor immune status during CCRT in cervical cancer compared to tissue biopsy.

A prior-information-based automatic segmentation method for the clinical target volume in adaptive radiotherapy of cervical cancer.

OBJECTIVE: Adaptive planning to accommodate anatomic changes during treatment often requires repeated segmentation. In this study, prior patient-specific data was integrateda into a registration-guided multi-channel multi-path (Rg-MCMP) segmentation framework to improve the accuracy of repeated clinical target volume (CTV) segmentation. METHODS: This study was based on CT image datasets for a total of 90 cervical cancer patients who received two courses of radiotherapy. A total of 15 patients were selected randomly as the test set. In the Rg-MCMP segmentation framework, the first-course CT images (CT1) were registered to second-course CT images (CT2) to yield aligned CT images (aCT1), and the CTV in the first course (CTV1) was propagated to yield aligned CTV contours (aCTV1). Then, aCT1, aCTV1, and CT2 were combined as the inputs for 3D U-Net consisting of a channel-based multi-path feature extraction network. The performance of the Rg-MCMP segmentation framework was evaluated and compared with the single-channel single-path model (SCSP), the standalone registration methods, and the registration-guided multi-channel single-path (Rg-MCSP) model. The Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and average surface distance (ASD) were used as the metrics. RESULTS: The average DSC of CTV for the deformable image DIR-MCMP model was found to be 0.892, greater than that of the standalone DIR (0.856), SCSP (0.837), and DIR-MCSP (0.877), which were improvements of 4.2%, 6.6%, and 1.7%, respectively. Similarly, the rigid body DIR-MCMP model yielded an average DSC of 0.875, which exceeded standalone RB (0.787), SCSP (0.837), and registration-guided multi-channel single-path (0.848), which were improvements of 11.2%, 4.5%, and 3.2%, respectively. These improvements in DSC were statistically significant (p < 0.05). CONCLUSION: The proposed Rg-MCMP framework achieved excellent accuracy in CTV segmentation as part of the adaptive radiotherapy workflow.

Prospective validation of a machine learning model for applicator and hybrid interstitial needle selection in high-dose-rate (HDR) cervical brachytherapy.

PURPOSE: To Demonstrate the clinical validation of a machine learning (ML) model for applicator and interstitial needle prediction in gynecologic brachytherapy through a prospective clinical study in a single institution. METHODS: The study included cervical cancer patients receiving high-dose-rate brachytherapy using intracavitary (IC) or hybrid interstitial (IC/IS) applicators. For each patient, the primary radiation oncologist contoured the high-risk clinical target volume on a pre-brachytherapy MRI, indicated the approximate applicator location, and made a clinical determination of the first fraction applicator. A pre-trained ML model predicted the applicator and IC/IS needle arrangement using tumor geometry. Following the first fraction, ML and radiation oncologist predictions were compared and a replanning study determined the applicator providing optimal organ-at-risk (OAR) dosimetry. The ML-predicted applicator and needle arrangement and the clinical determination were compared to this dosimetric ground truth. RESULTS: Ten patients were accrued from December 2020 to October 2022. Compared to the dosimetrically optimal applicator, both the radiation oncologist and ML had an accuracy of 70%. ML demonstrated better identification of patients requiring IC/IS applicators and provided balanced IC and IC/IS predictions. The needle selection model achieved an average accuracy of 82.5%. ML-predicted needle arrangements matched or improved plan quality when compared to clinically selected arrangements. Overall, ML predictions led to an average total improvement of 2.0 Gy to OAR doses over three treatment fractions when compared to clinical predictions. CONCLUSION: In the context of a single institution study, the presented ML model demonstrates valuable decision-support for the applicator and needle selection process with the potential to provide improved dosimetry. Future work will include a multi-center study to assess generalizability.

MRI-guided dynamic risk assessment in cervical cancer based on tumor hypoxia at diagnosis and volume response at brachytherapy.

BACKGROUND AND PURPOSE: Improvements in treatment outcome for patients with locally advanced cervical cancer (LACC) require a better classification of patients according to their risk of recurrence. We investigated whether an imaging-based approach, combining pretreatment hypoxia and tumor response during therapy, could improve risk classification. MATERIAL AND METHODS: Ninety-three LACC patients with T2-weigthed (T2W)-, dynamic contrast enhanced (DCE)- and diffusion weighted (DW)-magnetic resonance (MR) images acquired before treatment, and T2W- and, for 64 patients, DW-MR images, acquired at brachytherapy, were collected. Pretreatment hypoxic fraction (HFpre) was determined from DCE- and DW-MR images using the consumption and supply-based hypoxia (CSH)-imaging method. Volume regression at brachytherapy was assessed from T2W-MR images and combined with HFpre. In 17 patients with adequate DW-MR images at brachytherapy, the apparent diffusion coefficient (ADC), reflecting tumor cell density, was calculated. Change in ADC during therapy was combined with volume regression yielding functional regression as explorative response measure. Endpoint was disease free survival (DFS). RESULTS: HFpre was the strongest predictor of DFS, but a significant correlation with outcome was found also for volume regression. The combination of HFpre and volume regression showed a stronger association with DFS than HFpre alone. Patients with disease recurrence were selected to either the intermediate- or high-risk group with a 100 % accuracy. Functional regression showed a stronger correlation to HFpre than volume regression. CONCLUSION: The combination of pretreatment hypoxia and volume regression at brachytherapy improved patient risk classification. Integration of ADC with volume regression showed promise as a new tumor response parameter.

Multiparametric MRI-based radiomics nomogram for identifying cervix-corpus junction cervical adenocarcinoma from endometrioid adenocarcinoma.

OBJECTIVE: To developed a magnetic resonance imaging (MRI) radiomics nomogram to identify adenocarcinoma at the cervix-corpus junction originating from the endometrium or cervix in order to better guide clinical treatment. METHODS: Between February 2011 and September 2021, the clinicopathological data and MRI in 143 patients with histopathologically confirmed cervical adenocarcinoma (CAC, n = 86) and endometrioid adenocarcinoma (EAC, n = 57) were retrospectively analyzed at the cervix-corpus junction. Radiomics features were extracted from fat-suppressed T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) maps, and delayed phase contrast-enhanced T1-weighted imaging (CE-T1WI) sequences. A radiomics nomogram was developed integrating radscore with independent clinical risk factors. The area under the curve (AUC) was used to evaluate the diagnostic efficacy of the radscore, nomogram and two different experienced radiologists in differentiating CAC from EAC at the cervix-corpus junction, and Delong test was applied to compare the differences of their diagnostic performance. RESULTS: In the training cohort, the AUC was 0.93 for radscore; 0.97 for radiomics nomograms; 0.85 and 0.86 for radiologists 1 and 2, respectively. Delong test showed that the differential efficacy of nomogram was significant better than those of radiologists in the training cohort (both P < 0.05). CONCLUSIONS: The nomogram based on radscore and clinical risk factors could better differentiate CAC from EAC at the cervix-corpus junction than radiologists, and preoperatively and non-invasively identify the origin of adenocarcinoma at the cervix-corpus junction, which facilitates clinicians to make individualized treatment decision.

Integrated pretreatment diffusion kurtosis imaging and serum squamous cell carcinoma antigen levels: a biomarker strategy for early assessment of radiotherapy outcomes in cervical cancer.

OBJECTIVE: This study aims to explore the utility of pretreatment DKI parameters and serum SCC-Ag in evaluating the early therapeutic response of cervical cancer to radiotherapy. MATERIALS AND METHODS: A total of 33 patients diagnosed with cervical cancer, including 31 cases of cervical squamous cell carcinoma and two cases of adenosquamous carcinoma, participated in the study. All patients underwent conventional MRI and DKI scans on a 3T magnetic resonance scanner before radiotherapy and after ten sessions of radiotherapy. The therapeutic response was evaluated based on the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. Patients were categorized into a response group (RG), comprising Complete Remission (CR) and Partial Remission (PR), and a non-response group (NRG), comprising Stable Disease (SD) and Progressive Disease (PD). LASSO was employed to select pretreatment DKI parameters, and ROC curves were generated for the selected parameters and serum SCC-Ag. RESULTS: Significant differences were observed in pretreatment MD, Da, Dr, MK, Ka, Kr, and SCC-Ag between the RG and NRG groups (P < 0.01). However, no significant differences were noted for FA and FAK (P = 0.441&0.928). The two selected parameters (MD and MK) demonstrated area under the curve (AUC), sensitivity, and specificity of 0.810, 0.769, 0.850 and 0.827, 0.846, 0.750, respectively. The combination of MD and MK exhibited an improved AUC of 0.901, sensitivity of 0.692, and specificity of 1.000, with a higher Youden index compared to the individual parameters. Conversely, the AUC, sensitivity, and specificity of the combination of MD, MK, and SCC-Ag were 0.852, 0.615, and 1.000, with a Youden index of 0.615. CONCLUSION: Pretreatment MD, MK, and SCC-Ag demonstrate potential clinical utility, with the combined application of MD and MK showing enhanced efficacy in assessing the early therapeutic response of cervical cancer to radiotherapy. The addition of SCC-Ag did not contribute further to the assessment efficacy.

3D MR elastography-based stiffness as a marker for predicting tumor grade and subtype in cervical cancer.

BACKGROUND: Increasing evidence has indicated that high tissue stiffness (TS) may be a potential biomarker for evaluation of tumor aggressiveness. PURPOSE: To investigate the value of magnetic resonance elastography (MRE)-based quantitative parameters preoperatively predicting the tumor grade and subtype of cervical cancer (CC). STUDY TYPE: Retrospective. POPULATION: Twenty-five histopathology-proven CC patients and 7 healthy participants. FIELD STRENGTH/SEQUENCE: 3.0T, magnetic resonance imaging (MRI) (LAVA-flex) and MRE with a three-dimensional spin-echo echo-planar imaging. ASSESSMENT: The regions of interest (ROIs) were manually drawn by two observers in tumors to measure mean TS, storage modulus (G'), loss modulus (G″) and damping ratio (DR) values. Surgical specimens were evaluated for tumor grades and subtypes. STATISTICAL TESTS: Intraclass correlation coefficient (ICC) was expressed in terms of inter-observer agreements. t-test or Mann-Whitney nonparametric test was used to compare the complex modulus and apparent diffusion coefficient (ADC) values between different tumor groups. Area under the receiver operating characteristic curve (AUC) analysis was used to evaluate the diagnostic performance. RESULTS: The TS of endocervical adenocarcinoma (ECA) group was significantly higher than that in squamous cell carcinoma (SCC) group (5.27 kPa vs. 3.44 kPa, P = 0.042). The TS also showed significant difference between poorly and well/moderately differentiated CC (5.21 kPa vs. 3.47 kPa, P = 0.038), CC patients and healthy participants (4.18 kPa vs. 1.99 kPa, P < 0.001). The cutoff value of TS to discriminate ECA from SCC was 4.10 kPa (AUC: 0.80), while it was 4.42 kPa to discriminate poorly from well/moderately differentiated CC (AUC: 0.83), and 2.25 kPa to distinguish normal cervix from CC (AUC: 0.88), respectively. There were no significant difference in G″, DR and ADC values between any subgroups except for comparison of healthy participants and CC patients (P = 0.001, P = 0.004, P < 0.001, respectively). DATA CONCLUSION: 3D MRE-assessed TS shows promise as a potential biomarker to preoperatively assess tumor grade and subtype of CC.

Intraepithelial tumor-infiltrating lymphocytes shape loco-regional PET/CT spread of locally advanced cervical cancer.

BACKGROUND: Data suggest an association between positron emission tomography/CT (PET/CT) metabolic metrics and tumor microenvironment in several malignancies, and a potential role of PET/CT to monitor response to immunotherapy. OBJECTIVE: To evaluate the correlation between tumor loco-regional extension and tumor-infiltrating lymphocyte infiltration in locally advanced cervical cancer prior to concurrent chemo-radiotherapy.The secondary objective was to assess the association between tumor-infiltrating lymphocytes and PET/CT metabolic metrics. METHODS: Patients with locally advanced cervical cancer and negative para-aortic extensions on PET/CT were included. Two senior nuclear medicine physicians specializing in gynecologic oncology reviewed all PET/CT exams, and extracted tumor maximum standardized uptake value, metabolic tumor volume, and total lesion glycolysis, as well as pelvic lymph node involvement. One senior gynecologic oncology pathologist assessed intraepithelial tumor-infiltrating lymphocytes and stromal tumor-infiltrating lymphocytes. Intraepithelial tumor-infiltrating lymphocytes were categorized following previous studies as <1% and >1%. The cut-off for stromal tumor-infiltrating lymphocytes was chosen empirically: intermediate <60% and high >60%. RESULTS: 86 patients were included. Intraepithelial tumor-infiltrating lymphocytes were not significantly associated with tumor metabolic metrics. Intraepithelial tumor-infiltrating lymphocytes were not significantly associated with maximum standard uptake value (p=0.16), or metabolic tumor volume (p=0.19). Tumors with <1% intraepithelial tumor-infiltrating lymphocytes score were associated with a higher MRI tumor size (≥ median) (63.3% vs 39.3%, p=0.04). Patients with pelvic lymph node uptake were significantly more frequent in patients with high stromal tumor-infiltrating lymphocytes score (≥60%) (61.5% vs 31.7%, p=0.009). CONCLUSIONS: Poor or absent intraepithelial tumor-infiltrating lymphocytes were associated with more advanced disease at diagnosis and larger tumor size. Tumor-infiltrating lymphocytes were not associated with tumor metabolic activity. Intraepithelial and stroma tumor-infiltrating lymphocytes are not redundant and should be assessed separately. Further work is needed to evaluate the association between tumor metabolic profile and immune populations, including different T-cell subtypes for patient selection for immunotherapy strategies.

MRI-based volumetric tumor parameters before and during chemoradiation predict tumor recurrence and patient survival in locally advanced cervical cancer: a subgroup analysis of a phase II prospective trial.

BACKGROUND: This subgroup analysis of a prospective phase II trial aimed to identify valuable and accessible prognostic factors for overall survival (OS) and progression-free survival (PFS) of patients with locally advanced cervical cancer (LACC). METHODS: Patients with FIGO II to IVA cervical cancer were assessed in this study. All patients underwent concurrent chemoradiotherapy (CCRT) followed by brachytherapy. Tumor parameters based on MRI scans before and during CCRT were evaluated for Overall survival (OS) and Progression-free survival (PFS). RESULTS: A total of 86 patients were included in this analysis with a median follow-up period of 31.7 months. Three-year OS and PFS rates for all patients were 87.1% and 76.5%, respectively. Univariate Cox regression analysis showed that restaging tumor size (rTS) over 2.55 cm (p < 0.001), initial tumor volume (iTV) over 55.99 cc (p < 0.001), downstaging (p = 0.042), and restaging tumor volume (rTV) over 6.25 cc (p = 0.006) were significantly associated with OS. rTS (p < 0.001), iTV (p < 0.001), downstaging (p = 0.027), and rTV (p < 0.001) were identified as significant prognostic factors for PFS. In the stepwise multivariable analysis, only rTS > 2.55 cm showed statistically significant with OS (HR: 5.47, 95% CI 1.80-9.58, p = 0.035) and PFS (HR: 3.83, 95% CI 1.50-11.45; p = 0.025). CONCLUSIONS: Initial tumor size and restaging tumor volume that are easily accessible during radiotherapy provide valuable prognostic information for cervical cancer. MRI-based measurable volumetric scoring system can be readily applied in real-world practice of cervical cancer. CLINICAL TRIAL INFORMATION: This study is a subgroup analysis of prospective trial registered at ClinicalTrials.gov Identifier: NCT02993653.