[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.

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.

[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.

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.

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.

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 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.

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.

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.

Results of Definitive (Chemo)radiotherapy Using Computed Tomography-based Brachytherapy for Cervical Cancer.

BACKGROUND/AIM: Concurrent cisplatin-based chemoradiotherapy (CCRT) is the standard treatment for locally advanced cervical cancer. Especially, CCRT with magnetic resonance imaging (MRI) or computed tomography-based image-guided brachytherapy (CT-based 3D-IGBT) for cervical cancer has resulted in good LC rates. However, progression-free survival (PFS) and overall survival (OS) rates for locally advanced cervical cancer are still low and could be improved. The aim of the study was to evaluate treatment efficacy and late toxicity of external beam radiotherapy (EBRT) and CT-based IGBT with or without concurrent chemotherapy in patients with squamous cell carcinoma of the uterine cervix and investigate patterns of failure. PATIENTS AND METHODS: We retrospectively analyzed clinical data of cervical squamous cell carcinoma patients treated with definitive radiotherapy with or without concurrent chemotherapy at Saitama Medical University International Medical Center. Local control (LC), PFS, patterns of failure, and late toxicity were the evaluated outcomes. RESULTS: Overall, 290 patients were enrolled in the study. Median follow-up was 51.5 months. During follow-up, 74 patients developed recurrence: 10 patients with intra-pelvic failure only, 45 with extra-pelvic failure only, and 19 with both. The 3-year LC was 100% for T1b-T2a, 96.8% for T2b, 89.5% for T3b, and 88.5% for T4 disease. The 3-year PFS was 100% for stage IB-IIA, 89.0% for stage IIB, 70.7% for stage IIIB, 72.6% for stage IIIC1r, and 40.1% for stage IVA. The incidence of grade 3-4 gastrointestinal and genitourinary toxicities was 3.0% and 1.7%, respectively. CONCLUSION: Combination of EBRT and CT-based IGBT with or without concurrent chemotherapy produced favorable LC with acceptable rates of late toxicities. However, extra-pelvic failures frequently occurred and PFS was less satisfactory in patients with stage III-IVA disease, which indicated the need for additional treatment in these patients.

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.

Apparent diffusion coefficient values predict response to brachytherapy in bulky cervical cancer.

BACKGROUND: Diffusion-weighted magnetic resonance imaging (DWI) provides a measurement of tumor cellularity. We evaluated the potential of apparent diffusion coefficient (ADC) values obtained from post-external beam radiation therapy (EBRT) DWI and prior to brachytherapy (BT) to predict for complete metabolic response (CMR) in bulky cervical cancer. METHODS: Clinical and DWI (b value = 500 s/mm2) data were obtained from patients undergoing interstitial BT with high-risk clinical target volumes (HR-CTVs) > 30 cc. Volumes were contoured on co-registered T2 weighted images and 90th percentile ADC values were calculated. Patients were stratified by CMR (defined by PET-CT at three months post-BT). Relation of CMR with 90th percentile ADC values and other clinical factors (International Federation of Gynecology and Obstetrics (FIGO) stage, histology, tumor and HR-CTV size, pre-treatment hemoglobin, and age) was assessed both in univariate and multivariate logistic regression analyses. Youden's J statistic was used to identify a threshold value. RESULTS: Among 45 patients, twenty-eight (62%) achieved a CMR. On univariate analysis for CMR, only 90th percentile ADC value was significant (p = 0.029) while other imaging and clinical factors were not. Borderline significant factors were HR-CTV size (p = 0.054) and number of chemotherapy cycles (p = 0.078). On multivariate analysis 90th percentile ADC (p < 0.0001) and HR-CTV size (p < 0.003) were highly significant. Patients with 90th percentile ADC values above 2.10 × 10- 3 mm2/s were 5.33 (95% CI, 1.35-24.4) times more likely to achieve CMR. CONCLUSIONS: Clinical DWI may serve to risk-stratify patients undergoing interstitial BT for bulky cervical cancer.

Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma.

BACKGROUND: Surgery combined with radiotherapy substantially escalates the likelihood of encountering complications in early-stage cervical squamous cell carcinoma(ESCSCC). We aimed to investigate the feasibility of Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in ESCSCC and minimize the occurrence of adverse events associated with the treatment. METHODS: A dataset comprising MR images was obtained from 289 patients who underwent radical hysterectomy and pelvic lymph node dissection between January 2019 and April 2022. The dataset was randomly divided into two cohorts in a 4:1 ratio.The postoperative radiotherapy options were evaluated according to the Peter/Sedlis standard. We extracted clinical features, as well as intratumoral and peritumoral radiomic features, using the least absolute shrinkage and selection operator (LASSO) regression. We constructed the Clinical Signature (Clinic_Sig), Radiomics Signature (Rad_Sig) and the Deep Transformer Learning Signature (DTL_Sig). Additionally, we fused the Rad_Sig with the DTL_Sig to create the Deep Learning Radiomic Signature (DLR_Sig). We evaluated the prediction performance of the models using the Area Under the Curve (AUC), calibration curve, and Decision Curve Analysis (DCA). RESULTS: The DLR_Sig showed a high level of accuracy and predictive capability, as demonstrated by the area under the curve (AUC) of 0.98(95% CI: 0.97-0.99) for the training cohort and 0.79(95% CI: 0.67-0.90) for the test cohort. In addition, the Hosmer-Lemeshow test, which provided p-values of 0.87 for the training cohort and 0.15 for the test cohort, respectively, indicated a good fit. DeLong test showed that the predictive effectiveness of DLR_Sig was significantly better than that of the Clinic_Sig(P < 0.05 both the training and test cohorts). The calibration plot of DLR_Sig indicated excellent consistency between the actual and predicted probabilities, while the DCA curve demonstrating greater clinical utility for predicting the pathological features for adjuvant radiotherapy. CONCLUSION: DLR_Sig based on intratumoral and peritumoral MRI images has the potential to preoperatively predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma (ESCSCC).

Tumor and metastatic lymph nodes metabolic activity on 18F-FDG-PET/CT to predict progression-free survival in locally advanced cervical cancer.

OBJECTIVE: The present study investigated the predictive diseases progression value of preoperative fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) in patients with local advanced cervical cancer (LACC). METHODS: In total, 267 patients [median age 58 (range: 27-85) years old] with LACC underwent 18F-FDG PET/CT prior to any treatment. The maximum standardized uptake values (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) of the primary lesion and metastatic lymph nodes were measured on PET/CT and correlated with clinicopathological features and progression-free survival (PFS). RESULTS: The median follow-up was 36.52 (range: 3.09-61.29) months. During the observation period, 80 (30.0%) patients exhibited disease progression. Univariate analysis showed that FIGO stage, concurrent chemoradiotherapy (CRT), serum level of carcinoembryonic antigen (CEA) and squamous cell carcinoma antigen (SCC-Ag), primary tumor MTV (pMTV) and TLG (pTLG), lymph nodes SUVmax (nSUVmax) and TLG (nTLG), and total metabolic activity (sMTV, sTLG) were associated with PFS. nSUVmax ≥ 5.29, CEA ≥ 7.11 ng/ml and deficiency of concurrent CRT were independent risk factor for PFS (p = 0.006, p = 0.008, p = 0.014). The 3-year PFS for patients with high nSUVmax were 42.2% compared to 56.3% for low nSUVmax values. CONCLUSION: Pretreatment cervical and lymph nodes metabolic parameters were associated with PFS in patients with LACC.

End-to-end dosimetry audit for three-dimensional image-guided brachytherapy for cervical cancer.

BACKGROUND: End-to-end dosimetry audit for brachytherapy is challenging due to the steep dose gradient. However, it is an efficient method to detect unintended errors in actual clinical practice. PURPOSE: We aimed to develop an on-site end-to-end test phantom for three-dimensional image-guided brachytherapy (IGBT) for cervical cancer. METHODS: The test phantom we developed consisted of a water tank with an applicator/detector holder. The holder was designed to accommodate the applicator and insert an ionization chamber (PinPoint; PTW, Freiburg, Germany) to measure the dose at point A. Imaging and reconstruction were performed in the same way as performed for a patient. The feasibility of our test phantom was assessed in two different hospitals using tandem and ovoid (made of either metal or carbon) applicators that the hospitals provided. RESULTS: The measured and calculated doses at point A were compared for each applicator. We observed that the values obtained using metal applicators were consistently lower, on an average by -2.3%, than the calculated values, while those obtained using carbon applicators were comparable to the calculated values. This difference can be attributed to the attenuation of the dose by the metal applicators, resulting in a lower dose at point A. The majority of treatment planning system, including the one used in this study, do not account for the material of applicator. CONCLUSIONS: An end-to-end test phantom for IGBT was developed, tested, and applied in a dosimetry audit in hospitals and showed favorable results for evaluating the point A dose.

Serum squamous cell carcinoma antigen level and magnetic resonance imaging for the prognosis of locally advanced cervical cancer.

OBJECTIVE: Squamous cell carcinoma antigen (SCC-ag) and magnetic resonance imaging (MRI) were explored to serve as biomarkers to predict the prognosis of cervical cancer (CC) patients treated with neoadjuvant chemotherapy (NACT) prior to radical surgery, with the aim of identifying the subgroup that least benefits from the combined therapy. PATIENTS AND METHODS: All patients were treated with NACT prior to radical surgery and received MRI and SCC-ag examinations before and after NACT. For these three cycles of NACT, patients were treated with intravenous paclitaxel at 150 mg/m2 over a period of 3 hours and carboplatin, with the area under the sera concentration-time curve of 5 over a period of 30 minutes on the first day of each cycle. Meanwhile, the blood pressure, ECG, and blood oxygen saturation of the patients were observed during the infusion. A discovery cohort and a validation cohort were applied to examine the prognostic performance of SCC-ag, MRI, and their combination. The endpoints of our study were overall survival (OS) and progression-free survival (PFS). RESULTS: A total of 384 patients diagnosed between August 2006 and December 2010 were enrolled in our research, with 206 patients in the discovery cohort and 178 patients in the validation cohort. The high-risk group identified by MRI had a worse OS [hazard ratio (HR), 3.567; 95% confidence interval (CI), 1.466-8.677; log-rank p=0.0027) and PFS (HR, 4.062; 95% CI, 2.171-7.6; log-rank p<0.0001) than the low-risk group. Meanwhile, the SCC-RC could serve as a strong prognostic factor to predict OS (HR, 5.614; 95% CI, 2.473-12.744; log-rank p<0.0001) and PFS (HR, 7.481; 95% CI, 4.194-13.344; log-rank p<0.0001) for CC. In addition, the combined MRI and SCC-ag had greater prognostic efficiency and were used to divide the whole patient population into three groups. Compared with patients in the low-risk group, patients in the high-risk group had a worse OS (HR, 8.216; 95% CI, 2.98-22.651; log-rank p<0.0001) and PFS (HR, 11.757; 95% CI, 5.735-24.104; log-rank p<0.0001). Multivariate analyses revealed that MRI, SCC-ag, and their combination were independent prognostic factors. CONCLUSIONS: SCC-ag and MRI, individually or in combination, were bound up with OS and PFS in CC. Additionally, the predictive efficiency improved when SCC-ag and MRI were combined in a risk model that predicted the OS and PFS of SCC compared with the predictive efficiency of either SCC-ag or MRI alone, revealing that the combination of these two biomarkers could help to ameliorate prognostic stratification and to guide personalized therapy for SCC patients.

Radiomics Analysis of Multiparametric PET/MRI for N- and M-Staging in Patients with Primary Cervical Cancer.

PURPOSE: The aim of this study was to investigate the potential of multiparametric 18F-FDG PET/MR imaging as a platform for radiomics analysis and machine learning algorithms based on primary cervical cancers to predict N- and M-stage in patients. MATERIALS AND METHODS: A total of 30 patients with histopathological confirmation of primary and untreated cervical cancer were prospectively enrolled for a multiparametric 18F-FDG PET/MR examination, comprising a dedicated protocol for imaging of the female pelvis. The primary tumor in the uterine cervix was manually segmented on post-contrast T1-weighted images. Quantitative features were extracted from the segmented tumors using the Radiomic Image Processing Toolbox for the R software environment for statistical computing and graphics. 45 different image features were calculated from non-enhanced as well as post-contrast T1-weighted TSE images, T2-weighted TSE images, the ADC map, the parametric Ktrans, Kep, Ve and iAUC maps and PET images, respectively. Statistical analysis and modeling was performed using Python 3.5 and the scikit-learn software machine learning library for the Python programming language. RESULTS: Prediction of M-stage was superior when compared to N-stage. Prediction of M-stage using SVM with SVM-RFE as feature selection obtained the highest performance providing sensitivity of 91 % and specificity of 92 %. Using receiver operating characteristic (ROC) analysis of the pooled predictions, the area under the curve (AUC) was 0.97. Prediction of N-stage using RBF-SVM with MIFS as feature selection reached sensitivity of 83 %, specificity of 67 % and an AUC of 0.82. CONCLUSION: M- and N-stage can be predicted based on isolated radiomics analyses of the primary tumor in cervical cancers, thus serving as a template for noninvasive tumor phenotyping and patient stratification using high-dimensional feature vectors extracted from multiparametric PET/MRI data. KEY POINTS: · Radiomics analysis based on multiparametric PET/MRI enables prediction of the metastatic status of cervical cancers. · Prediction of M-stage is superior to N-stage. · Multiparametric PET/MRI displays a valuable platform for radiomics analyses .

Patterns of practice survey for cervical cancer brachytherapy in Morocco.

PURPOSE: This study surveyed radiation oncologists in Morocco to explore current practices and perspectives on brachytherapy for cervix cancer. METHODS AND MATERIALS: A 37-question survey was conducted in April 2023 among 165 Moroccan radiation oncologists using Google Forms. RESULTS: Of the 93 respondents, 39% treated over 20 patients in 2022 using 3D image-guided brachytherapy (BT) through the HDR technique; 2D techniques were not reported in the last five years. Intracavitary BT is uniformly applied with a tandem and ovoid applicator. Only 14% utilized interstitial needles for hybrid BT. Iridium-192 was the primary radioactive source (63%), followed by cobalt (37%). Ultrasound-guided 47% of applicator insertions. All used CT scans for planning, but only 6% used MRI fusion due to limited availability. Guidelines for target volume and dose prescription were mostly based on GEC-ESTRO recommendations (74%), followed by Manchester Point A (30.4%) and ABS (11%). Over 90% delineated CTV-HR and CTV-IR; 30% delineated GTV. All marked the bladder and rectum, while 52% marked the sigmoid, 5% the small bowel, and 3% the recto-vaginal point. For dosimetry, 12% used ICRU 89 points, 54% used dose-volume histograms (DVH), and 36% used both. Most reported EQD2cc for OARs for the rectum and bladder, with nine still using ICRU point doses. The most common fractionation schema was 7 Gy in four fractions (60%) and 7 Gy in three fractions (55%). CONCLUSIONS: Brachytherapy remains essential for treating cervical cancer in Morocco. Key areas for improvement include MRI fusion-guided brachytherapy, access to advanced applicators, expanding interstitial techniques, and professional training and national referential.

HVS-Unsup: Unsupervised cervical cell instance segmentation method based on human visual simulation.

Instance segmentation plays an important role in the automatic diagnosis of cervical cancer. Although deep learning-based instance segmentation methods can achieve outstanding performance, they need large amounts of labeled data. This results in a huge consumption of manpower and material resources. To solve this problem, we propose an unsupervised cervical cell instance segmentation method based on human visual simulation, named HVS-Unsup. Our method simulates the process of human cell recognition and incorporates prior knowledge of cervical cells. Specifically, firstly, we utilize prior knowledge to generate three types of pseudo labels for cervical cells. In this way, the unsupervised instance segmentation is transformed to a supervised task. Secondly, we design a Nucleus Enhanced Module (NEM) and a Mask-Assisted Segmentation module (MAS) to address problems of cell overlapping, adhesion, and even scenarios involving visually indistinguishable cases. NEM can accurately locate the nuclei by the nuclei attention feature maps generated by point-level pseudo labels, and MAS can reduce the interference from impurities by updating the weight of the shallow network through the dice loss. Next, we propose a Category-Wise droploss (CW-droploss) to reduce cell omissions in lower-contrast images. Finally, we employ an iterative self-training strategy to rectify mislabeled instances. Experimental results on our dataset MS-cellSeg, the public datasets Cx22 and ISBI2015 demonstrate that HVS-Unsup outperforms existing mainstream unsupervised cervical cell segmentation methods.