Value of [18F]FDG PET radiomic features and VEGF expression in predicting pelvic lymphatic metastasis and their potential relationship in early-stage cervical squamous cell carcinoma

Predictive value and potential association of PET/CT radiomics on lymph node metastasis of cervical cancer

Objective

Due to the information-rich nature of positron emission tomography/computed tomography (PET/CT) images, the authors hope to explore radiomics features that could distinguish metastatic lymph nodes (LNs) from hypermetabolic benign LNs, in addition to conventional indicators.

Methods

PET/CT images of 106 patients with early-stage cervical cancer from 2019 to 2021 were retrospectively analyzed. The tumor lesions and LN regions of PET/CT images were outlined with SeeIt, and then radiomics features were extracted. The least absolute shrinkage and selection operator (LASSO) was used to select features. The final selected radiomics features of LNs were used as predictors to construct a machine learning model to predict LN metastasis.

Results

The authors determined two morphological coefficient characteristics of cervical lesions (shape - major axis length and shape - mesh volume), one first order characteristics of LNs (first order - 10 percentile) and two gray-level co-occurrence matrix (GLCM) characteristics of LNs (GLCM - id and GLCM - inverse variance) were closely related to LN metastasis. Finally, a neural network was constructed based on the radiomic features of the LNs. The area under the curve of receiver operating characteristic (AUC-ROC) of the model was 0.983 in the training set and 0.860 in the test set.

Conclusion

The authors constructed and demonstrated a neural network based on radiomics features of PET/CT to evaluate the risk of single LN metastasis in early-stage cervical cancer.

Predicting Lymph Node Metastasis From Primary Cervical Squamous Cell Carcinoma Based on Deep Learning in Histopathologic Images

We developed a deep learning framework to accurately predict the lymph node status of patients with cervical cancer based on hematoxylin and eosin-stained pathological sections of the primary tumor. In total, 1524 hematoxylin and eosin-stained whole slide images (WSIs) of primary cervical tumors from 564 patients were used in this retrospective, proof-of-concept study. Primary tumor sections (1161 WSIs) were obtained from 405 patients who underwent radical cervical cancer surgery at the Fudan University Shanghai Cancer Center (FUSCC) between 2008 and 2014; 165 and 240 patients were negative and positive for lymph node metastasis, respectively (including 166 with positive pelvic lymph nodes alone and 74 with positive pelvic and para-aortic lymph nodes). We constructed and trained a multi-instance deep convolutional neural network based on a multiscale attention mechanism, in which an internal independent test set (100 patients, 228 WSIs) from the FUSCC cohort and an external independent test set (159 patients, 363 WSIs) from the Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma cohort of the Cancer Genome Atlas program database were used to evaluate the predictive performance of the network. In predicting the occurrence of lymph node metastasis, our network achieved areas under the receiver operating characteristic curve of 0.87 in the cross-validation set, 0.84 in the internal independent test set of the FUSCC cohort, and 0.75 in the external test set of the Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma cohort of the Cancer Genome Atlas program. For patients with positive pelvic lymph node metastases, we retrained the network to predict whether they also had para-aortic lymph node metastases. Our network achieved areas under the receiver operating characteristic curve of 0.91 in the cross-validation set and 0.88 in the test set of the FUSCC cohort. Deep learning analysis based on pathological images of primary foci is very likely to provide new ideas for preoperatively assessing cervical cancer lymph node status; its true value must be validated with cervical biopsy specimens and large multicenter datasets.

Novel 5-aminopyrazoles endowed with anti-angiogenetic properties: Design, synthesis and biological evaluation

The promising anti-angiogenetic properties of previously synthesized pyrazolyl ureas provided the rationale for the synthesis of novel 5-aminopyrazoles 2-5, differently decorated on the pyrazole nucleus. All the derivatives were tested by MTT assays and proved to be non-cytotoxic against eight different tumor cell lines and normal fibroblasts. An EdU proliferation assay was carried out on human foreskin fibroblasts and VEGF stimulated human umbilical vein endothelial cells which confirmed the absence of cytotoxicity of the compounds on human cells up to 20 μM concentration. To evaluate the influence of the newly synthesized pyrazoles on MAPK and PI3K signaling pathways, the phosphorylation of ERK1/2 and Akt was analyzed by Western blots from HFF and HUVEC cell lysates stimulated with growth factors in the presence or absence of the compounds. Pyrazoles 3b and 3c showed a significant inhibition of Akt phosphorylation in both tested cell lines with lower phosphorylation levels than the reference compound GeGe-3 in HUVEC. Furthermore, derivatives 2 and 3 appeared to strongly affect the migration of HFF cells in a wound healing assay, confirming their potential ability to interfere with the angiogenesis process. The new pyrazole library extends the structure-activity relationships of the previously isolated compounds and highlights the attractiveness of this chemical class for pathological cell migration and angiogenesis.

A Multicenter Study on Preoperative Assessment of Lymphovascular Space Invasion in Early-Stage Cervical Cancer Based on Multimodal MR Radiomics

BACKGROUND

As lymphovascular space invasion (LVSI) was closely related to lymph node metastasis and prognosis, the preoperative assessment of LVSI in early-stage cervical cancer is crucial for patients.

PURPOSE

To develop and validate nomogram based on multimodal MR radiomics to assess LVSI status in cervical cancer patients.

STUDY TYPE

Retrospective.

POPULATION

The study included 168 cervical cancer patients, of whom 129 cases (age 51.36 ± 9.99 years) from institution 1 were included as the training cohort and 39 cases (age 52.59 ± 10.23 years) from institution 2 were included as the external test cohort.

FIELD STRENGTH/SEQUENCE

There were 1.5 T and 3.0 T MRI scans (T1-weighted imaging [T1WI], fat-saturated T2-weighted imaging [FS-T2WI], and contrast-enhanced [CE]).

ASSESSMENT

Six machine learning models were built and selected to construct the radiomics signature. The nomogram model was constructed by combining the radiomics signature with the clinical signature, which was then validated for discrimination, calibration, and clinical usefulness.

STATISTICAL TESTS

The clinical characteristics were compared using t-tests, Mann-Whitney U tests, or chi-square tests. The Spearman and LASSO methods were used to select radiomics features. The receiver operating characteristic (ROC) analysis was performed, and the area under the curve (AUC), accuracy, sensitivity, and specificity were calculated.

RESULTS

The logistic regression (LR) model performed best in each sequence. The AUC of CE-T1-T2WI-combined was the highest in the LR model, with an AUC of 0.775 (95% CI: 0.570-0.979) in external test cohort. The nomogram showed high predictive performance in the training (AUC: 0.883 [95% CI: 0.823-0.943]) and test cohort (AUC: 0.830 [95% CI: 0.657-1.000]) for predicting LVSI. Decision curve analysis demonstrated that the nomogram was clinically useful.

DATA CONCLUSION

Our findings suggest that the proposed nomogram model based on multimodal MRI of CE T1WI-T2WI-combined could be used to assess LVSI status in early cervical cancer.

EVIDENCE LEVEL

4.

TECHNICAL EFFICACY

Stage 2.

Multidimensional outlook on the pathophysiology of cervical cancer invasion and metastasis

Cervical cancer being one of the primary causes of high mortality rates among women is an area of concern, especially with ineffective treatment strategies. Extensive studies are carried out to understand various aspects of cervical cancer initiation, development and progression; however, invasive cervical squamous cell carcinoma has poor outcomes. Moreover, the advanced stages of cervical cancer may involve lymphatic circulation with a high risk of tumor recurrence at distant metastatic sites. Dysregulation of the cervical microbiome by human papillomavirus (HPV) together with immune response modulation and the occurrence of novel mutations that trigger genomic instability causes malignant transformation at the cervix. In this review, we focus on the major risk factors as well as the functionally altered signaling pathways promoting the transformation of cervical intraepithelial neoplasia into invasive squamous cell carcinoma. We further elucidate genetic and epigenetic variations to highlight the complexity of causal factors of cervical cancer as well as the metastatic potential due to the changes in immune response, epigenetic regulation, DNA repair capacity, and cell cycle progression. Our bioinformatics analysis on metastatic and non-metastatic cervical cancer datasets identified various significantly and differentially expressed genes as well as the downregulation of potential tumor suppressor microRNA miR-28-5p. Thus, a comprehensive understanding of the genomic landscape in invasive and metastatic cervical cancer will help in stratifying the patient groups and designing potential therapeutic strategies.

Habitat-based radiomics enhances the ability to predict lymphovascular space invasion in cervical cancer: a multi-center study

Introduction

Lymphovascular space invasion (LVSI) is associated with lymph node metastasis and poor prognosis in cervical cancer. In this study, we investigated the potential of radiomics, derived from magnetic resonance (MR) images using habitat analysis, as a non-invasive surrogate biomarker for predicting LVSI in cervical cancer.

Methods

This retrospective study included 300 patients with cervical cancer who underwent surgical treatment at two centres (centre 1 = 198 and centre 2 = 102). Using the k-means clustering method, contrast-enhanced T1-weighted imaging (CE-T1WI) images were segmented based on voxel and entropy values, creating sub-regions within the volume ofinterest. Radiomics features were extracted from these sub-regions. Pearson correlation coefficient and least absolute shrinkage and selection operator LASSO) regression methods were used to select features associated with LVSI in cervical cancer. Support vector machine (SVM) model was developed based on the radiomics features extracted from each sub-region in the training cohort.

Results

The voxels and entropy values of the CE-T1WI images were clustered into three sub-regions. In the training cohort, the AUCs of the SVM models based on radiomics features derived from the whole tumour, habitat 1, habitat 2, and habitat 3 models were 0.805 (95% confidence interval [CI]: 0.745-0.864), 0.873(95% CI: 0.824-0.922), 0.869 (95% CI: 0.821-0.917), and 0.870 (95% CI: 0.821-0.920), respectively. Compared with whole tumour model, the predictive performances of habitat 3 model was the highest in the external test cohort (0.780 [95% CI: 0.692-0.869]).

Conclusions

The radiomics model based on the tumour sub-regional habitat demonstrated superior predictive performance for an LVSI in cervical cancer than that of radiomics model derived from the whole tumour.

Radiomics systematic review in cervical cancer: gynecological oncologists' perspective

OBJECTIVE

Radiomics is the process of extracting quantitative features from radiological images, and represents a relatively new field in gynecological cancers. Cervical cancer has been the most studied gynecological tumor for what concerns radiomics analysis. The aim of this study was to report on the clinical applications of radiomics combined and/or compared with clinical-pathological variables in patients with cervical cancer.

METHODS

A systematic review of the literature from inception to February 2023 was performed, including studies on cervical cancer analysing a predictive/prognostic radiomics model, which was combined and/or compared with a radiological or a clinical-pathological model.

RESULTS

A total of 57 of 334 (17.1%) screened studies met inclusion criteria. The majority of studies used magnetic resonance imaging (MRI), but positron emission tomography (PET)/computed tomography (CT) scan, CT scan, and ultrasound scan also underwent radiomics analysis. In apparent early-stage disease, the majority of studies (16/27, 59.3%) analysed the role of radiomics signature in predicting lymph node metastasis; six (22.2%) investigated the prediction of radiomics to detect lymphovascular space involvement, one (3.7%) investigated depth of stromal infiltration, and one investigated (3.7%) parametrial infiltration. Survival prediction was evaluated both in early-stage and locally advanced settings. No study focused on the application of radiomics in metastatic or recurrent disease.

CONCLUSION

Radiomics signatures were predictive of pathological and oncological outcomes, particularly if combined with clinical variables. These may be integrated in a model using different clinical-pathological and translational characteristics, with the aim to tailor and personalize the treatment of each patient with cervical cancer.

Multicentric development and evaluation of 18F-FDG PET/CT and MRI radiomics models to predict para-aortic lymph node involvement in locally advanced cervical cancer

PURPOSE

To develop machine learning models to predict para-aortic lymph node (PALN) involvement in patients with locally advanced cervical cancer (LACC) before chemoradiotherapy (CRT) using 18F-FDG PET/CT and MRI radiomics combined with clinical parameters.

METHODS

We retrospectively collected 178 patients (60% for training and 40% for testing) in 2 centers and 61 patients corresponding to 2 further external testing cohorts with LACC between 2010 to 2022 and who had undergone pretreatment analog or digital 18F-FDG PET/CT, pelvic MRI and surgical PALN staging. Only primary tumor volumes were delineated. Radiomics features were extracted using the Radiomics toolbox®. The ComBat harmonization method was applied to reduce the batch effect between centers. Different prediction models were trained using a neural network approach with either clinical, radiomics or combined models. They were then evaluated on the testing and external validation sets and compared.

RESULTS

In the training set (n = 102), the clinical model achieved a good prediction of the risk of PALN involvement with a C-statistic of 0.80 (95% CI 0.71, 0.87). However, it performed in the testing (n = 76) and external testing sets (n = 30 and n = 31) with C-statistics of only 0.57 to 0.67 (95% CI 0.36, 0.83). The ComBat-radiomic (GLDZM_HISDE_PET_FBN64 and Shape_maxDiameter2D3_PET_FBW0.25) and ComBat-combined (FIGO 2018 and same radiomics features) models achieved very high predictive ability in the training set and both models kept the same performance in the testing sets, with C-statistics from 0.88 to 0.96 (95% CI 0.76, 1.00) and 0.85 to 0.92 (95% CI 0.75, 0.99), respectively.

CONCLUSIONS

Radiomic features extracted from pre-CRT analog and digital 18F-FDG PET/CT outperform clinical parameters in the decision to perform a para-aortic node staging or an extended field irradiation to PALN. Prospective validation of our models should now be carried out.

Prognostic Value of Axillary Lymph Node Texture Parameters Measured by Pretreatment 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Locally Advanced Breast Cancer with Neoadjuvant Chemotherapy

Background: This study investigated the prognostic value of axillary lymph node (ALN) heterogeneity texture features through ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) in patients with locally advanced breast cancer (LABC). Methods: We retrospectively analyzed 158 LABC patients with FDG-avid, pathology-proven, metastatic ALN who underwent neoadjuvant chemotherapy (NAC) and curative surgery. Tumor and ALN texture parameters were extracted from pretreatment ¹⁸F-FDG PET/CT using Chang-Gung Image Texture Analysis software. The least absolute shrinkage and selection operator regression was performed to select the most significant predictive texture parameters. The predictive impact of texture parameters was evaluated for both progression-free survival and pathologic NAC response. Results: The median follow-up period of 36.8 months and progression of disease (PD) was observed in 36 patients. In the univariate analysis, ALN textures (minimum standardized uptake value (SUV) (p = 0.026), SUV skewness (p = 0.038), SUV bias-corrected Kurtosis (p = 0.034), total lesion glycolysis (p = 0.011)), tumor textures (low-intensity size zone emphasis (p = 0.045), minimum SUV (p = 0.047), and homogeneity (p = 0.041)) were significant texture predictors. On the Cox regression analysis, ALN SUV skewness was an independent texture predictor of PD (p = 0.016, hazard ratio 2.3, 95% confidence interval 1.16–4.58). Conclusions: ALN texture feature from pretreatment ¹⁸F-FDG PET/CT is useful for the prediction of LABC progression.

Feasibility of Predicting Pelvic Lymph Node Metastasis Based on IVIM-DWI and Texture Parameters of the Primary Lesion and Lymph Nodes in Patients with Cervical Cancer

RATIONALE AND OBJECTIVES

To investigate the feasibility and value of intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) and texture parameters of primary lesions and lymph nodes for predicting pelvic lymph node metastasis in patients with cervical cancer.

MATERIALS AND METHODS

A total of 143 patients with cervical cancer confirmed by surgical pathology were analyzed retrospectively and 125 patients were enrolled in primary lesions study, 83 patients and 134 lymph nodes were enrolled in lymph nodes study. Patients and lymph nodes were randomly divided into training group and test group at a ratio of 2: 1. The IVIM-DWI parameters and 3D texture features of primary lesions and lymph nodes of all patients were measured. The least absolute shrinkage and selection operator algorithm, spearman's correlation analysis, independent two-sample t-test and Mann-Whitney U-test were used to select texture parameters. Multivariate Logistic regression analysis and receiver operating characteristic curves were used to model and evaluate diagnostic performances.

RESULTS

In primary lesions study, model 1 was constructed by combining f value, original_shape_Sphericity and original_firstorder_Mean of primary lesions. In lymph nodes study, model 2 was constructed by combining short diameter, circular enhancement and rough margin of lymph nodes. Model 3 was constructed by combining ADC, f value and original_glszm_Small Area Emphasis of lymph nodes. The areas under curve of model 1, 2 and 3 in training group and test group were 0.882, 0.798, 0.907 and 0.862, 0.771, 0.937 respectively.

CONCLUSION

Models based on IVIM-DWI and texture parameters of primary lesions and lymph nodes both performed well in diagnosing pelvic lymph node metastasis of cervical cancer and were superior to morphological features of lymph nodes. Especially, parameters of lymph nodes showed higher diagnostic efficiency and clinical significance.

Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers

The objective of this review was to summarize published radiomics studies dealing with infradiaphragmatic cancers, blood malignancies, melanoma, and musculoskeletal cancers, and assess their quality. PubMed database was searched from January 1990 to February 2022 for articles performing radiomics on PET imaging of at least 1 specified tumor type. Exclusion criteria includd: non-oncological studies; supradiaphragmatic tumors; reviews, comments, cases reports; phantom or animal studies; technical articles without a clinically oriented question; studies including <30 patients in the training cohort. The review database contained PMID, first author, year of publication, cancer type, number of patients, study design, independent validation cohort and objective. This database was completed twice by the same person; discrepant results were resolved by a third reading of the articles. A total of 162 studies met inclusion criteria; 61 (37.7%) studies included >100 patients, 13 (8.0%) were prospective and 61 (37.7%) used an independent validation set. The most represented cancers were esophagus, lymphoma, and cervical cancer (n = 24, n = 24 and n = 19 articles, respectively). Most studies focused on 18F-FDG, and prognostic and response to treatment objectives. Although radiomics and artificial intelligence are technically challenging, new contributions and guidelines help improving research quality over the years and pave the way toward personalized medicine.

Radiomics model of 18F-FDG PET/CT imaging for predicting disease-free survival of early-stage uterine cervical squamous cancer

BACKGROUND

To explore an effective predictive model based on PET/CT radiomics for the prognosis of early-stage uterine cervical squamous cancer.

METHODS

Preoperative PET/CT data were collected from 201 uterine cervical squamous cancer patients with stage IB-IIA disease (FIGO 2009) who underwent radical surgery between 2010 and 2015. The tumor regions were manually segmented, and 1318 radiomic features were extracted. First, model-based univariate analysis was performed to exclude features with small correlations. Then, the redundant features were further removed by feature collinearity. Finally, the random survival forest (RSF) was used to assess feature importance for multivariate analysis. The prognostic models were established based on RSF, and their predictive performances were measured by the C-index and the time-dependent cumulative/dynamics AUC (C/D AUC).

RESULTS

In total, 6 radiomic features (5 for CT and 1 for PET) and 6 clinicopathologic features were selected. The radiomic, clinicopathologic and combination prognostic models yielded C-indexes of 0.9338, 0.9019 and 0.9527, and the mean values of the C/D AUC (mC/D AUC) were 0.9146, 0.8645 and 0.9199, respectively.

CONCLUSIONS

PET/CT radiomics could achieve approval power in predicting DFS in early-stage uterine cervical squamous cancer.

Development of High-Resolution Dedicated PET-Based Radiomics Machine Learning Model to Predict Axillary Lymph Node Status in Early-Stage Breast Cancer

Simple Summary

Accurate clinical axillary evaluation plays an important role in the diagnosis of and treatment planning for breast cancer (BC). This study aimed to develop a machine learning model integrating dedicated breast PET and clinical characteristics for prediction of axillary lymph node status in cT1-2N0-1M0 BC non-invasively. The performance of this integrating model in identifying pN0 and pN1 with the AUC was 0.94. We achieved an NPV of 96.88% in the cN0 and PPV of 92.73% in the cN1 subgroup. The higher true positive and true negative rate could delineate clinical subtypes and apply more precise treatment for patients with early-stage BC.

Abstract

Purpose of the Report: Accurate clinical axillary evaluation plays an important role in the diagnosis and treatment planning for early-stage breast cancer (BC). This study aimed to develop a scalable, non-invasive and robust machine learning model for predicting of the pathological node status using dedicated-PET integrating the clinical characteristics in early-stage BC. Materials and Methods: A total of 420 BC patients confirmed by postoperative pathology were retrospectively analyzed. 18F-fluorodeoxyglucose (¹⁸F-FDG) Mammi-PET, ultrasound, physical examination, Lymph-PET, and clinical characteristics were analyzed. The least absolute shrinkage and selection operator (LASSO) regression analysis were used in developing prediction models. The characteristic curve (ROC) of the area under receiver-operator (AUC) and DeLong test were used to evaluate and compare the performance of the models. The clinical utility of the models was determined via decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. Results: A total of 290 patients were enrolled in this study. The AUC of the integrated model diagnosed performance was 0.94 (95% confidence interval (CI), 0.91–0.97) in the training set (n = 203) and 0.93 (95% CI, 0.88–0.99) in the validation set (n = 87) (both p < 0.05). In clinical N0 subgroup, the negative predictive value reached 96.88%, and in clinical N1 subgroup, the positive predictive value reached 92.73%. Conclusions: The use of a machine learning integrated model can greatly improve the true positive and true negative rate of identifying clinical axillary lymph node status in early-stage BC.

The Role of Histogram-Based Textural Analysis of 18F-FDG PET/CT in Evaluating Tumor Heterogeneity and Predicting the Prognosis of Invasive Lung Adenocarcinoma

Objectives:

This study aimed to investigate the contributory role of histogram-based textural features (HBTFs) extracted from ¹⁸fluorinefluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) in tumoral heterogeneity (TH) evaluation and invasive lung adenocarcinoma (ILA) prognosis prediction.

Methods:

This retrospective study analyzed the data of 72 patients with ILA who underwent ¹⁸F-FDG PET/CT followed by surgical resection. The maximum standardized uptake value (SUV_max), metabolic tumor volume, and total lesion glycolysis values were calculated for each tumor. Additionally, HBTFs were extracted from ¹⁸F-FDG PET/CT images using the software program. ILA was classified into the following five histopathological subtypes according to the predominant pattern: Lepidic adenocarcinoma (LA), acinar adenocarcinoma, papillary adenocarcinoma, solid adenocarcinoma (SA), and micropapillary adenocarcinoma (MA). Differences between ¹⁸F-FDG PET/CT parameters and histopathological subtypes were evaluated using non-parametric tests. The study endpoints include overall survival (OS) and progression-free survival (PFS). The prognostic values of clinicopathological factors and ¹⁸F-FDG PET/CT parameters were evaluated using the Cox regression analyses.

Results:

The median SUV_max and entropy values were significantly higher in SA-MA, whereas lower in LA. The median energy-uniformity value of the LA was significantly higher than the others. Among all parameters, only skewness and kurtosis were significantly associated with lymph node involvement status. The median values for follow-up time, PFS, and OS were 31.26, 16.07, and 20.87 months, respectively. The univariate Cox regression analysis showed that lymph node involvement was the only significant predictor for PFS. The multivariate Cox regression analysis revealed that higher SUV_max (≥11.69) and advanced stage (IIB-IIIA) were significantly associated with poorer OS [hazard ratio (HR): 3.580, p=0.024 and HR: 7.608, p=0.007, respectively].

Conclusion:

HBTFs were tightly associated with clinicopathological factors causing TH. Among the ¹⁸F-FDG PET/CT parameters, only skewness and kurtosis were associated with lymph node involvement, whereas SUV_max was the only independent predictor of OS. TH measurement with HBTFs may contribute to conventional metabolic parameters in guiding precision medicine for ILA.

Prediction of out-of-field recurrence after chemoradiotherapy for cervical cancer using a combination model of clinical parameters and magnetic resonance imaging radiomics: a multi-institutional study of the Japanese Radiation Oncology Study Group

We retrospectively assessed whether magnetic resonance imaging (MRI) radiomics combined with clinical parameters can improve the predictability of out-of-field recurrence (OFR) of cervical cancer after chemoradiotherapy. The data set was collected from 204 patients with stage IIB (FIGO: International Federation of Gynecology and Obstetrics 2008) cervical cancer who underwent chemoradiotherapy at 14 Japanese institutes. Of these, 180 patients were finally included for analysis. OFR-free survival was calculated using the Kaplan-Meier method, and the statistical significance of clinicopathological parameters for the OFR-free survival was evaluated using the log-rank test and Cox proportional-hazards model. Prediction of OFR from the analysis of diffusion-weighted images (DWI) and T2-weighted images of pretreatment MRI was done using the least absolute shrinkage and selection operator (LASSO) model for engineering image feature extraction. The accuracy of prediction was evaluated by 5-fold cross-validation of the receiver operating characteristic (ROC) analysis. Para-aortic lymph node metastasis (p = 0.003) was a significant prognostic factor in univariate and multivariate analyses. ROC analysis showed an area under the curve (AUC) of 0.709 in predicting OFR using the pretreatment status of para-aortic lymph node metastasis, 0.667 using the LASSO model for DWIs and 0.602 using T2 weighted images. The AUC improved to 0.734 upon combining the pretreatment status of para-aortic lymph node metastasis with that from the LASSO model for DWIs. Combining MRI radiomics with clinical parameters improved the accuracy of predicting OFR after chemoradiotherapy for locally advanced cervical cancer.

The Influence of Different Ultrasonic Machines on Radiomics Models in Prediction Lymph Node Metastasis for Patients with Cervical Cancer

Objective To investigate the effects of different ultrasonic machines on the performance of radiomics models using ultrasound (US) images in the prediction of lymph node metastasis (LNM) for patients with cervical cancer (CC) preoperatively. Methods A total of 536 CC patients with confirmed histological characteristics and lymph node status after radical hysterectomy and pelvic lymphadenectomy were enrolled. Radiomics features were extracted and selected with US images acquired with ATL HDI5000, Voluson E8, MyLab classC, ACUSON S2000, and HI VISION Preirus to build radiomics models for LNM prediction using support vector machine (SVM) and logistic regression, respectively. Results There were 148 patients (training vs validation: 102:46) scanned in machine HDI5000, 75 patients (53:22) in machine Voluson E8, 100 patients (69:31) in machine MyLab classC, 110 patients (76:34) in machine ACUSON S2000, and 103 patients (73:30) in machine HI VISION Preirus, respectively. Few radiomics features were reproducible among different machines. The area under the curves (AUCs) ranged from 0.75 to 0.86, 0.73 to 0.86 in the training cohorts, and from 0.71 to 0.82, 0.70 to 0.80 in the validation cohorts for SVM and logistic regression models, respectively. The highest difference in AUCs for different machines reaches 17.8% and 15.5% in the training and validation cohorts, respectively. Conclusions The performance of radiomics model is dependent on the type of scanner. The problem of scanner dependency on radiomics features should be considered, and their effects should be minimized in future studies for US images.

PET/MRI and PET/CT Radiomics in Primary Cervical Cancer: A Pilot Study on the Correlation of Pelvic PET, MRI, and CT Derived Image Features

PURPOSE

To evaluate the correlation of radiomic features in pelvic [2-deoxy-2-18F]fluoro-D-glucose positron emission tomography/magnetic resonance imaging and computed tomography ([¹⁸F]FDG PET/MRI and [¹⁸F]FDG PET/CT) in patients with primary cervical cancer (CCa).

PROCEDURES

Nineteen patients with histologically confirmed primary squamous cell carcinoma of the cervix underwent same-day [¹⁸F]FDG PET/MRI and PET/CT. Two nuclear medicine physicians performed a consensus reading in random order. Free-hand regions of interest covering the primary cervical tumors were drawn on PET, contrast-enhanced pelvic CT, and pelvic MR (T2 weighted and ADC) images. Several basic imaging features, standard uptake values (SUV_mean, SUV_max, and SUV_peak), total lesion glycolysis (TLG), metabolic tumor volume (MTV), and more advanced texture analysis features were calculated. Pearson's correlation test was used to assess the correlation between each pair of features. Features were compared between local and metastatic tumors, and their role in predicting metastasis was evaluated by receiver operating characteristic curves.

RESULTS

For a total of 101 extracted features, 1104/5050 pairs of features showed a significant correlation (ρ ≥ 0.70, p < 0.05). There was a strong correlation between 190/484 PET pairs of features from PET/MRI and PET/CT, 91/418 pairs of CT and PET from PET/CT, 79/418 pairs of T2 and PET from PET/MRI, and 50/418 pairs of ADC and PET from PET/MRI. Significant difference was seen between eight features in local and metastatic tumors including MTV, TLG, and entropy on PET from PET/CT; MTV and TLG on PET from PET/MRI; compactness and entropy on T2; and entropy on ADC images.

CONCLUSIONS

We demonstrated strong correlation of many extracted radiomic features between PET/MRI and PET/CT. Eight radiomic features calculated on PET/CT and PET/MRI were significantly different between local and metastatic CCa. This study paves the way for future studies to evaluate the diagnostic and predictive potential of radiomics that could guide clinicians toward personalized patients care.

Prediction of lymphovascular space invasion using a combination of tenascin-C, cox-2, and PET/CT radiomics in patients with early-stage cervical squamous cell carcinoma

BACKGROUND

Lymphovascular space invasion is an independent prognostic factor in early-stage cervical cancer. However, there is a lack of non-invasive methods to detect lymphovascular space invasion. Some researchers found that Tenascin-C and Cyclooxygenase-2 was correlated with lymphovascular space invasion. Radiomics has been studied as an emerging tool for distinguishing tumor pathology stage, evaluating treatment response, and predicting prognosis. This study aimed to establish a machine learning model that combines radiomics based on PET imaging with tenascin-C (TNC) and cyclooxygenase-2 (COX-2) for predicting lymphovascular space invasion (LVSI) in patients with early-stage cervical cancer.

METHODS

One hundred and twelve patients with early-stage cervical squamous cell carcinoma who underwent PET/CT examination were retrospectively analyzed. Four hundred one radiomics features based on PET/CT images were extracted and integrated into radiomics score (Rad-score). Immunohistochemical analysis was performed to evaluate TNC and COX-2 expression. Mann-Whitney U test was used to distinguish differences in the Rad-score, TNC, and COX-2 between LVSI and non-LVSI groups. The correlations of characteristics were tested by Spearman analysis. Machine learning models including radiomics model, protein model and combined model were established by logistic regression algorithm and evaluated by ROC curve. Pairwise comparisons of ROC curves were tested by DeLong test.

RESULTS

The Rad-score of patients with LVSI was significantly higher than those without. A significant correlation was shown between LVSI and Rad-score (r = 0.631, p < 0.001). TNC was correlated to both the Rad-score (r = 0.244, p = 0.024) and COX-2 (r = 0.227, p = 0.036). The radiomics model had the best predictive performance among all models in training and external dataset (AUCs: 0.914, 0.806, respectively, p < 0.001). However, in testing dataset, the combined model had better efficiency for predicting LVSI than other models (AUCs: 0.801 vs. 0.756 and 0.801 vs. 0.631, respectively).

CONCLUSION

The machine learning model of the combination of PET radiomics with COX-2 and TNC provides a new tool for detecting LVSI in patients with early-stage cervical cancer. In the future, multicentric studies on larger sample of patients will be used to test the model.

TRIAL REGISTRATION

This is a retrospective study and there is no experimental intervention on human participants. The Ethics Committee has confirmed that retrospectively registered is not required.

Self-Regulation of Cerebral Metabolism and Its Neuroprotective Effect After Hypoxic-Ischemic Injury: Evidence From 1H-MRS

¹H-MRS technology can be used to non-invasively detect the content of cerebral metabolites, to assess the severity of hypoxic-ischemic (HI) injury, and to predict the recovery of compromised neurological function. However, changes to the cerebral self-regulation process after HI are still unclear. This study investigated the changes in cerebral metabolites and the potential relationship with the number of neurons and neural stem/progenitor cells (NSPC) using ¹H-MRS, and finally clarifies the self-regulation of cerebral metabolism and neuroprotection after HI injury. Newborn Yorkshire pigs (28 males, 1.0–1.5 kg) aged 3–5 days were used for the HI model in this study. The pigs were randomly divided into the HI group (n = 24) and the control group (n = 4), then the experimental group was subdivided according to different recovery time after HI into the following groups: 0–2 h (n = 4), 2–6 h (n = 4), 6–12 h (n = 4), 12–24 h (n = 4), 24–48 h (n = 4), and 48–72 h (n = 4). Following the HI timepoints, ¹H-MRS scans were performed and processed using LCModel software, and brain tissue was immunohistochemically stained for Nestin and NeuN. Immunofluorescence staining of creatine phosphokinase-BB (CK-BB), N-acetylaspartylglutamate synthetase (NAAGS), glutamate carboxypeptidase II (GCP-II), glutamate-cysteine ligase catalytic subunit (GCLC), glutathione synthase (GS), and excitatory amino acid carrier 1 (EAAC1) was then performed. The ¹H-MRS results showed that cerebral N-acetylaspartylglutamate (NAAG), glutathione (GSH), and creatine (Cr) content reached their peaks at 12–24 h, which was consistent with the recovery time of hippocampal NSPCs and neurons, indicating a potential neuroprotective effect of NAAG, GSH, and Cr after HI injury.

Relationship between tumor heterogeneity and volume in cervical cancer: Evidence from integrated fluorodeoxyglucose 18 PET/MR texture analysis

OBJECTIVE

The aim of this study was to evaluate the effect of cervical cancer volume on PET/magnetic resonance (MR) texture heterogeneity.

MATERIALS AND METHODS

We retrospectively analyzed the PET/MR images of 138 patients with pathologically diagnosed cervical squamous cell carcinoma, including 50 patients undergoing surgery and 88 patients receiving concurrent chemoradiotherapy. Fluorodeoxyglucose 18 (18FDG)-PET/MR examination were performed for each patient before treatment, and the PET and MR texture analysis were undertaken. The texture features of the tumor based on gray-level co-occurrence matrices were extracted, and the correlation between tumor texture features and volume parameters was analyzed using Spearman's rank correlation coefficient. Finally, the variation trend of tumor texture heterogeneity was analyzed as tumor volumes increased.

RESULTS

PET texture features were highly correlated with metabolic tumor volume (MTV), including entropy-log2, entropy-log10, energy, homogeneity, dissimilarity, contrast, correlation, and the correlation coefficients (rs) were 0.955, 0.955, -0.897, 0.883, -0.881, -0.876, and 0.847 (P < 0.001), respectively. In the range of smaller MTV, the texture heterogeneity of energy, entropy-log2, and entropy-log10 increases with an increase in tumor volume, whereas the texture heterogeneity of homogeneity, dissimilarity, contrast, and correlation decreases with an increase in tumor volume. Only homogeneity, contrast, correlation, and dissimilarity had high correlation with tumor volume on MRI. The correlation coefficients (rs) were 0.76, -0.737, 0.644, and -0.739 (P < 0.001), respectively. The texture heterogeneity of MRI features that are highly correlated with tumor volume decreases with increasing tumor volume.

CONCLUSION

In the small tumor volume range, the heterogeneity variation trend of PET texture features is inconsistent as the tumor volume increases, but the variation trend of MRI texture heterogeneity is consistent, and MRI texture heterogeneity decreases as tumor volume increases. These results suggest that MRI is a better imaging modality when compared with PET in determining tumor texture heterogeneity in the small tumor volume range.

Imager-4D: New Software for Viewing Dynamic PET Scans and Extracting Radiomic Parameters from PET Data

Extensive research is currently being conducted into dynamic positron emission tomography (PET) acquisitions (including dynamic whole-body imaging) as well as extraction of radiomic features from imaging modalities. We describe a new PET viewing software known as Imager-4D that provides a facile means of viewing and analyzing dynamic PET data and obtaining associated quantitative metrics including radiomic parameters. The Imager-4D was programmed in the Java language utilizing the FX extensions. It is executable on any system for which a Java w/FX compliant virtual machine is available. The software incorporates the ability to view and analyze dynamic data acquired with different types of dynamic protocols. For image display, the program maintains a built-in library of 62 different lookup tables with monochromatic and full-color distributions. The Imager-4D provides multiple display layouts and can display fused images. Multiple methods of volume-of-interest (VOI) selection are available. Dynamic analysis features, such as image summation and full Patlak analysis, are also available. The user interface includes window width and level, blending, and zoom functionality. VOI sizes are adjustable and data from VOIs can either be displayed numerically or graphically within the software or exported. An example case of a 50-year-old woman with metastatic colorectal cancer and thyroiditis is included and demonstrates the steps for a user to obtain standard PET parameters, dynamic data, and radiomic features using selected VOIs. The Imager-4D represents a novel PET viewer that allows the user to view dynamic PET data, to derive dynamic and radiomic parameters from that data, and to combine dynamic data with radiomics ("dynomics"). The Imager-4D is available as a free download. This software has the potential to speed the adoption of advanced analysis of dynamic PET data into routine clinical use.

A COVID-19 risk score combining chest CT radiomics and clinical characteristics to differentiate COVID-19 pneumonia from other viral pneumonias

With the continued transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) throughout the world, identification of highly suspected COVID-19 patients remains an urgent priority. In this study, we developed and validated COVID-19 risk scores to identify patients with COVID-19. In this study, for patient-wise analysis, three signatures, including the risk score using radiomic features only, the risk score using clinical factors only, and the risk score combining radiomic features and clinical variables, show an excellent performance in differentiating COVID-19 from other viral-induced pneumonias in the validation set. For lesion-wise analysis, the risk score using three radiomic features only also achieved an excellent AUC value. In contrast, the performance of 130 radiologists based on the chest CT images alone without the clinical characteristics included was moderate as compared to the risk scores developed. The risk scores depicting the correlation of CT radiomics and clinical factors with COVID-19 could be used to accurately identify patients with COVID-19, which would have clinically translatable diagnostic and therapeutic implications from a precision medicine perspective.

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.

PET-CT radiomics by integrating primary tumor and peritumoral areas predicts E-cadherin expression and correlates with pelvic lymph node metastasis in early-stage cervical cancer

OBJECTIVES

To explore the role of radiomics in integrating primary tumor and peritumoral areas based on PET-CT scans for predicting E-cadherin (E-cad) expression in early-stage cervical cancer (ESCC) and its correlation with pelvic lymph node metastasis (PLNM).

METHODS

Ninety-seven ESCC patients who had undergone PET-CT scans were retrospectively analyzed. The ROI of primary tumors, peritumoral areas, and plus tumors were semi-automatically segmented on PET-CT images. A total of 1188 radiomics features were extracted, selected, and eventually integrated into radiomics score (rad-score). The rad-score difference between patients with E-cad expression of high and low was analyzed using Mann-Whitney tests. Characteristic correlation was tested using a Spearman analysis. Four models were established using logistic regression algorithms and evaluated using ROC and calibration curves. A DeLong test was used to perform pairwise comparisons of AUCs.

RESULTS

The rad-score of patients with low E-cad expression was higher than that of patients with high E-cad expression in both training and testing cohorts (p < 0.001 and p = 0.027, respectively). A significant correlation was observed between the rad-score and E-cad (p < 0.001). PLNM correlated slightly with rad-score and E-cad values (p = 0.01 and p < 0.001, respectively). The ROC curve and calibration curve of the rad-score model performed best in both training and testing cohorts (AUC = 0.915, 0.844, p < 0.001, respectively).

CONCLUSIONS

The radiomics of integrating primary tumor and peritumoral areas based on PET-CT showed correlations with PLNM. It was also able to predict E-cad expression in ESCC patients, allowing for evaluation of those patients' prognosis and more individualized medical treatment.

KEY POINTS

• By integrating the primary tumor and peritumoral area based on PET-CT, radiomics was feasible. • The rad-score was associated with E-cad expression and PLNM in patients with ESCC. • Radiomics that integrated the primary tumor and peritumoral areas based on PET-CT could predict E-cad expression in patients with ESCC.

A novel 2-deoxy-2-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT)-based nomogram to predict lymph node metastasis in early stage uterine cervical squamous cell cancer

Background

We aimed to establish an effective 2-deoxy-2-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) based nomogram for pelvic lymph node (PLN) metastasis prediction in early-stage uterine cervical squamous cell cancer.

Methods

A predictive model was developed in a cohort that consisted of 351 patients with stage IB-IIA [International Federation of Gynecology and Obstetrics (FIGO) 2009] uterine cervical squamous cell cancer. All patients underwent a preoperative PET/CT scan and subsequent radical surgery between 2010 and 2017, with 241 and 110 patients allotted into training and external validation cohorts. The chi-square (χ²) test and the logistic regression analysis were used to compare the clinical and PET/CT parameters with PLN metastasis. A nomogram was developed and validated by internal and external validation.

Results

In the training cohort, 82 (34.0%) patients had positive PLNs identified in the preoperative PET/CT scan. Among them, 46 (56.1%) were pathologically confirmed. There were 30 (18.9%) PET/CT scan-negative patients found to have PLN metastasis. The χ² test and logistic regression showed that only the squamous cell carcinoma antigen (SCCA) level (P=0.039) and maximum standardized uptake value (SUVmax) of PLN (nSUVmax, P=0.001) were independent predictors for PLN metastasis. A predictive nomogram based on these 2 parameters was developed with a C-index [95% confidence interval (CI)] of 0.854 (0.772-0.937) on internal validation and 0.836 (0.723-0.948) on the external validation. Compared to nSUVmax alone, our nomogram showed elevated sensitivity (70.5%, 73.1% vs. 60.5%), specificity (94.4%, 86.4% vs. 78.2%), and positive predictive value (PPV) (93.9%, 86.4% vs. 56.1%) in both the training and validation cohorts.

Conclusions

We successfully developed a noninvasive and convenient nomogram for preoperative identification of PLN metastasis in early-stage squamous cell cervical cancer.

Can computed tomography-based radiomics potentially discriminate between anterior mediastinal cysts and type B1 and B2 thymomas?

Background

Anterior mediastinal cysts (AMC) are often misdiagnosed as thymomas and undergo surgical resection, which caused unnecessary treatment and medical resource waste. The purpose of this study is to explore potential possibility of computed tomography (CT)-based radiomics for the diagnosis of AMC and type B1 and B2 thymomas.

Methods

A group of 188 patients with pathologically confirmed AMC (106 cases misdiagnosed as thymomas in CT) and thymomas (82 cases) and underwent routine chest CT from January 2010 to December 2018 were retrospectively analyzed. The lesions were manually delineated using ITK-SNAP software, and radiomics features were performed using the artificial intelligence kit (AK) software. A total of 180 tumour texture features were extracted from enhanced CT and unenhanced CT, respectively. The general test, correlation analysis, and LASSO were used to features selection and then the radiomics signature (radscore) was obtained. The combined model including radscore and independent clinical factors was developed. The model performances were evaluated on discrimination, calibration curve.

Results

Two radscore models were constructed from the unenhanced and enhanced phases based on the selected four and three features, respectively. The AUC, sensitivity, and specificity of the enhanced radscore model were 0.928, 89.3%, and 83.8% in the training dataset and 0.899, 84.6%, and 87.5% in the test dataset (higher than the unenhanced radscore model). The combined model of enhanced CT including radiomics features and independent clinical factors yielded an AUC, sensitivity and specificity of 0.941, 82.1%, and 94.6% in the training dataset and 0.938, 92.3%, and 87.5% in the test dataset (higher than the unenhanced combined model and enhanced radscore model).

Conclusions

The study suggested the possibility that the combined model in enhanced CT provided a potential tool to facilitate the differential diagnosis of AMC and type B1 and B2 thymomas.

Pathological angiogenesis and inflammation in tissues

The role of angiogenesis in the growth of organs and tumors is widely recognized. Vascular-organ interaction is a key mechanism and a concept that enables an understanding of all biological phenomena and normal physiology that is essential for human survival under pathological conditions. Recently, vascular endothelial cells have been classified as a type of innate immune cells that are dependent on the pathological situations. Moreover, inflammatory cytokines and signaling regulators activated upon exposure to infection or various stresses play crucial roles in the pathological function of parenchymal cells, peripheral immune cells, stromal cells, and cancer cells in tissues. Therefore, vascular-organ interactions as a vascular microenvironment or tissue microenvironment under physiological and pathological conditions are gaining popularity as an interesting research topic. Here, we review vascular contribution as a major factor in microenvironment homeostasis in the pathogenesis of normal as well as cancerous tissues. Furthermore, we suggest that the normalization strategy of pathological angiogenesis could be a promising therapeutic target for various diseases, including cancer.

Feasibility of MRI-based radiomics features for predicting lymph node metastases and VEGF expression in cervical cancer

PURPOSE

To investigate the predictive value of MRI-based radiomics features for lymph node metastasis (LNM) and vascular endothelial growth factor (VEGF) expression in patients with cervical cancer.

METHOD

A total of 163 patients with cervical cancer were enrolled in this study. A total of 134 patients were included for LNM differentiation, and 118 were included for VEGF expression discrimination. The patients were randomly assigned to the training group or test group at a ratio of 2:1. Radiomics features were extracted from T1WI enhanced and T2WI MRI scans of each patient, and tumor stage was also documented according to the International Federation of Gynecology and Obstetrics (FIGO) guidelines. The least absolute shrinkage and selection operator algorithm was used for feature selection. The results of 5-fold cross validation were applied to select the best classification models. The performances of the constructed models were further evaluated with the test group.

RESULTS

Sixteen radiomics features and the FIGO stage were selected to construct the LNM discrimination model. The LNM prediction model achieved the best diagnostic performance, with areas under the receiver operating curve (AUCs) of 0.95 and 0.88 in the training group and test group, respectively. Nine radiomics characteristics were screened to build the VEGF prediction model, with AUCs of 0.82 and 0.70 in the training group and test group, respectively. Decision curve analysis confirmed their clinical usefulness.

CONCLUSIONS

The presented radiomics prediction models demonstrated potential to noninvasively differentiate LNM and VEGF expression in cervical cancer.

Radiomics Analysis of Multiparametric MRI for the Preoperative Prediction of Lymph Node Metastasis in Cervical Cancer

Objective: To develop and validate a radiomics predictive model based on multiparameter MR imaging features and clinical features to predict lymph node metastasis (LNM) in patients with cervical cancer.

Material and Methods: A total of 168 consecutive patients with cervical cancer from two centers were enrolled in our retrospective study. A total of 3,930 imaging features were extracted from T2-weighted (T2W), ADC, and contrast-enhanced T1-weighted (cT1W) images for each patient. Four-step procedures, mainly minimum redundancy maximum relevance (MRMR) and least absolute shrinkage and selection operator (LASSO) regression, were applied for feature selection and radiomics signature building in the training set from center I (n = 115). Combining clinical risk factors, a radiomics nomogram was then constructed. The models were then validated in the external validation set comprising 53 patients from center II. The predictive performance was determined by its calibration, discrimination, and clinical usefulness.

Results: The radiomics signature derived from the combination of T2W, ADC, and cT1W images, composed of six LN-status-related features, was significantly associated with LNM and showed better predictive performance than signatures derived from either of them alone in both sets. Encouragingly, the radiomics signature also showed good discrimination in the MRI-reported LN-negative subgroup, with AUC of 0.825 (95% CI: 0.732–0.919). The radiomics nomogram that incorporated radiomics signature and MRI-reported LN status also showed good calibration and discrimination in both sets, with AUCs of 0.865 (95% CI: 0.794–0.936) and 0.861 (95% CI: 0.733–0.990), respectively. Decision curve analysis confirmed its clinical usefulness.

Conclusion: The proposed MRI-based radiomics nomogram has good performance for predicting LN metastasis in cervical cancer and may be useful for improving clinical decision making.

Machine learning-based analysis of [18F]DCFPyL PET radiomics for risk stratification in primary prostate cancer

PURPOSE

Quantitative prostate-specific membrane antigen (PSMA) PET analysis may provide for non-invasive and objective risk stratification of primary prostate cancer (PCa) patients. We determined the ability of machine learning-based analysis of quantitative [¹⁸F]DCFPyL PET metrics to predict metastatic disease or high-risk pathological tumor features.

METHODS

In a prospective cohort study, 76 patients with intermediate- to high-risk PCa scheduled for robot-assisted radical prostatectomy with extended pelvic lymph node dissection underwent pre-operative [¹⁸F]DCFPyL PET-CT. Primary tumors were delineated using 50-70% peak isocontour thresholds on images with and without partial-volume correction (PVC). Four hundred and eighty standardized radiomic features were extracted per tumor. Random forest models were trained to predict lymph node involvement (LNI), presence of any metastasis, Gleason score ≥ 8, and presence of extracapsular extension (ECE). For comparison, models were also trained using standard PET features (SUVs, volume, total PSMA uptake). Model performance was validated using 50 times repeated 5-fold cross-validation yielding the mean receiver-operator characteristic curve AUC.

RESULTS

The radiomics-based machine learning models predicted LNI (AUC 0.86 ± 0.15, p < 0.01), nodal or distant metastasis (AUC 0.86 ± 0.14, p < 0.01), Gleason score (0.81 ± 0.16, p < 0.01), and ECE (0.76 ± 0.12, p < 0.01). The highest AUCs reached using standard PET metrics were lower than those of radiomics-based models. For LNI and metastasis prediction, PVC and a higher delineation threshold improved model stability. Machine learning pre-processing methods had a minor impact on model performance.

CONCLUSION

Machine learning-based analysis of quantitative [¹⁸F]DCFPyL PET metrics can predict LNI and high-risk pathological tumor features in primary PCa patients. These findings indicate that PSMA expression detected on PET is related to both primary tumor histopathology and metastatic tendency. Multicenter external validation is needed to determine the benefits of using radiomics versus standard PET metrics in clinical practice.

Integrating tumor and nodal radiomics to predict lymph node metastasis in gastric cancer

BACKGROUND

To develop and validate a radiomics method via integrating tumor and lymph node radiomics for the preoperative prediction of lymph node (LN) status in gastric cancer (GC).

MATERIALS AND METHODS

We retrospectively collected 170 contrast-enhanced abdominal CT images from GC patients. Five times repeated random hold-out experiment was employed. Tumor and nodal radiomics features were extracted from each individual tumor and LN respectively, and then multi-step feature selection was performed. The optimal tumor and nodal features were selected using Pearson correlation analysis and sequential forward floating selection (SFFS) algorithm. After feature fusion, the SFFS algorithm was used to develop radiomics signatures. The performance of the radiomics signatures developed based on logistic regression classifier was further analyzed and compared using the area under the receiver operating characteristic curve (AUC).

RESULTS

The AUC values, reported as mean ± standard deviation, were 0.9319 ± 0.0129 and 0.8546 ± 0.0261 for the training and validation cohorts respectively. The radiomic signatures could predict LN status, especially in T2-stage, diffuse-type and moderately/well differentiated GC. After integrating clinicopathologic information, the radiomic-clinicopathologic model (training cohort, 0.9432 ± 0.0129; validation cohort, 0.8764 ± 0.0322) showed a better discrimination capability than other radiomics models and clinicopathologic model. The radiomic-clinicopathologic model also showed superior performance to the gastroenterologist' decision in all experiments, and outperformed the radiologist in some experiments.

CONCLUSION

Our proposed method presented good predictive performance and great potential for predicting LNM in GC. As a noninvasive preoperative prediction tool, it can be helpful for guiding the prognosis and treatment decision-making in GC patients.

Noninvasive prediction of lymph node status for patients with early-stage cervical cancer based on radiomics features from ultrasound images

OBJECTIVE

To investigate the feasibility of a noninvasive detection of lymph node metastasis (LNM) for early-stage cervical cancer (ECC) patients with radiomics methods based on the textural features from ultrasound images.

METHODS

One hundred seventy-two ECC patients between January 2014 and September 2018 with pathologically confirmed lymph node status (LNS) and preoperative ultrasound images were retrospectively reviewed. Regions of interest (ROIs) were delineated by a senior radiologist in the ultrasound images. LIFEx was applied to extract textural features for radiomics study. Least absolute shrinkage and selection operator (LASSO) regression was applied for dimension reduction and for selection of key features. A multivariable logistic regression analysis was adopted to build the radiomics signature. The Mann-Whitney U test was applied to investigate the correlation between radiomics and LNS for both training and validation cohorts. Receiver operating characteristic (ROC) curves were applied to evaluate the accuracy of the radiomics prediction models.

RESULTS

A total of 152 radiomics features were extracted from ultrasound images, in which 6 features were significantly associated with LNS (p < 0.05). The radiomics signatures demonstrated a good discrimination between patients with LNM and non-LNM groups. The best radiomics performance model achieved an area under the curve (AUC) of 0.79 (95% confidence interval (CI), 0.71-0.88) in the training cohort and 0.77 (95% CI, 0.65-0.88) in the validation cohort.

CONCLUSIONS

The feasibility of radiomics features from ultrasound images for the prediction of LNM in ECC was investigated. This noninvasive prediction method may be used to facilitate preoperative identification of LNS in patients with ECC.

KEY POINTS

• Few studied had investigated the feasibility of radiomics based on ultrasound images for cervical cancer, even though it is the most common practice for gynecological cancer diagnosis and treatment. • The radiomics signatures based on ultrasound images demonstrated a good discrimination between patients with and without lymph node metastasis with an area under the curve (AUC) of 0.79 and 0.77 in the training and validation cohorts, respectively. • The radiomics model based on preoperative ultrasound images has the potential ability to predict lymph node status noninvasively in patients with early-state cervical cancer, so as to reduce the impact of invasive examination and to optimize the treatment choices.

Value of integrated PET-IVIM MR in assessing metastases in hypermetabolic pelvic lymph nodes in cervical cancer: a multi-parameter study

PURPOSE

To evaluate the value of integrated multi-parameter positron emission tomography-intravoxel incoherent motion magnetic resonance (PET-IVIM MR) imaging for pelvic lymph nodes with high FDG uptake in cervical cancer, and to determine the best combination of parameters.

METHODS

A total of 38 patients with 59 lymph nodes with high FDG uptake were included. The imaging parameters of the lymph nodes were calculated by PET-IVIM MR, and the differences between lymph nodes diagnosed by postoperative pathology as metastasis versus non-metastasis were compared. We used the receiver operating characteristic (ROC) curve and logistic regression to construct a combination prediction model to filter low value and similar parameters, in order to search the optimal combination of PET/MR parameters for predicting pathologically confirmed metastatic lymph nodes. The correlation between diffusion parameters and metabolic parameters was analyzed by Spearman's rank correlation.

RESULTS

The maximum standardized uptake value (SUV_max), mean standardized uptake value (SUV_mean), total metabolic tumor volume (MTV), total lesion glycolysis (TLG), apparent diffusion coefficient (ADC), diffusion-related coefficient (D), and perfusion-related parameter (F) showed significant differences between the metastatic and non-metastatic groups (p < 0.05). The combination of MTV, SUV_max, and D had the strongest predictive value (area under the ROC 0.983, p < 0.05). SUV_max, SUV_mean, and TLG weakly correlated with F (R = - 0.306, - 0.290, and - 0.310; p < 0.05).

CONCLUSIONS

The combination of MTV, SUV_max, and D may have a better diagnostic performance than PET- or IVIM-derived parameters either in combination or individually. No strong correlation exists between diffusion parameters and metabolic parameters.

KEY POINTS

• Integrated PET-IVIM MR may assist to characterize lymph node status. • The combination of MTV, SUV_max, and D may have a better diagnostic performance than PET- or IVIM-derived parameters either in combination or individually for the assessment of pelvic lymph nodes with high FDG uptake. • No strong correlation exists between diffusion parameters and metabolic parameters in pelvic lymph nodes with high FDG uptake.

Radiogenomic analysis of vascular endothelial growth factor in patients with diffuse gliomas

OBJECTIVE

To predict vascular endothelial growth factor (VEGF) expression in patients with diffuse gliomas using radiomic analysis.

MATERIALS AND METHODS

Preoperative magnetic resonance images were retrospectively obtained from 239 patients with diffuse gliomas (World Health Organization grades II-IV). The patients were randomly assigned to a training group (n = 160) or a validation group (n = 79) at a 2:1 ratio. For each patient, a total of 431 radiomic features were extracted. The minimum redundancy maximum relevance (mRMR) algorithm was used for feature selection. A machine-learning model for predicting VEGF status was then developed using the selected features and a support vector machine classifier. The predictive performance of the model was evaluated in both groups using receiver operating characteristic curve analysis, and correlations between selected features were assessed.

RESULTS

Nine radiomic features were selected to generate a VEGF-associated radiomic signature of diffuse gliomas based on the mRMR algorithm. This radiomic signature consisted of two first-order statistics or related wavelet features (Entropy and Minimum) and seven textural features or related wavelet features (including Cluster Tendency and Long Run Low Gray Level Emphasis). The predictive efficiencies measured by the area under the curve were 74.1% in the training group and 70.2% in the validation group. The overall correlations between the 9 radiomic features were low in both groups.

CONCLUSIONS

Radiomic analysis facilitated efficient prediction of VEGF status in diffuse gliomas, suggesting that using tumor-derived radiomic features for predicting genomic information is feasible.

nm23, TOP2A and VEGF expression: Potential prognostic biologic factors in peripheral T-cell lymphoma, not otherwise specified

Peripheral T-cell lymphoma not otherwise specified (PTCL-NOS) is an aggressive lymphoma associated with a poor outcome. To date, the factor consistently associated with prognosis is the International Prognostic Index (IPI) score; however, it is considered that the IPI score cannot be beneficial for guiding potential targeted therapies. New scoring systems have recently been developed. The aim of the present study was to observe the expression of NME/NM23 nucleoside diphosphate kinase 1 (nm23), nuclear DNA topoisomerase 2-α (TOP2A), multiple myeloma oncogene-1 (MUM-1) and vascular endothelial growth factor (VEGF), and evaluate their prognostic value in PTCL-NOS. A retrospective analysis of 124 cases of PTCL-NOS showed that 70/122 (57.4%) cases were positive for nm23, 71/122 (58.2%) for TOP2A, 30/119 (25.2%) for MUM-1 and 64/122 (52.5%) for VEGF. Of note, 50/122 cases concurrently expressed nm23, TOP2A and VEGF. The univariate analysis results revealed that the nm23 (P=0.012), TOP2A (P=0.002) and VEGF (P=0.008) expression had a negative prognostic effect in patients with PTCL-NOS, while the MUM-1 expression did not have a significant prognostic value (P=0.918). In addition, the concurrent expression of nm23, TOP2A and VEGF was significantly associated with a worse prognosis (P=0.002). However, in multivariate Cox regression analysis, the concurrent expression of nm23, TOP2A and VEGF tended to predict a worse prognosis, however the P-value was borderline (hazard ratio, 1.495; 95% confidence interval, 0.993-2.250; P=0.054). It is speculated that there may be an association among the expression of nm23, TOP2A and VEGF, and that their expression may serve as a promising prognostic factor for PTCL-NOS.

AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics

INTRODUCTION

The quantitative imaging features (radiomics) that can be obtained from the different modalities of current-generation hybrid imaging can give complementary information with regard to the tumour environment, as they measure different morphologic and functional imaging properties. These multi-parametric image descriptors can be combined with artificial intelligence applications into predictive models. It is now the time for hybrid PET/CT and PET/MRI to take the advantage offered by radiomics to assess the added clinical benefit of using multi-parametric models for the personalized diagnosis and prognosis of different disease phenotypes.

OBJECTIVE

The aim of the paper is to provide an overview of current challenges and available solutions to translate radiomics into hybrid PET-CT and PET-MRI imaging for a smart and truly multi-parametric decision model.

Combinative evaluation of primary tumor and lymph nodes in predicting pelvic lymphatic metastasis in early-stage cervical cancer: A multiparametric PET-CT study

PURPOSE

The purpose of this study is to investigate the value of combining tumor and pelvic lymph node (PLN) characteristics on PET-CT in predicting PLN metastasis of patients with early-stage cervical cancer, specifically to further reduce the false-negative cases of diagnosis.

METHODS

The [18F] FDG PET-CT imaging data of 394 patients who were newly diagnosed with cervical cancer (FIGO stage, Ia-IIa) were retrospectively studied. We measured size, total lesion glycolysis (TLG) of tumor, metabolic tumor volume (MTV), maximum and mean standardized uptake value (SUVmax, SUVmean) of tumor and lymph node (LN). Diagnostic efficiency was evaluated using receiver operator characteristic (ROC) curve. We also investigated additional CT diagnosis information in PET-negative cases.

RESULTS

Our results indicated both lymph node and tumor parameters were independent risk factors for lymphatic metastasis in early-stage cervical cancer. The diagnosis based on above meaningful parameters, we name it 'combination diagnosis', offered significantly higher predictive value than that based on SUV measurement alone, which the values of AUC were 0.842 and 0.784 respectively (P < 0.05). In PET-negative cases, we also found that tumor TLG, suspicious LN in lymphatic drainage pathway, long/short axis of LN ≤ 2, heterogeneity of LN significantly associated with PLN metastasis. ROC analysis showed combination diagnosis of all these parameters above produced an AUC value of 0.859 (P < 0.05, 95% CI, 0.811-0.899), which was significantly higher than either using tumor TLG alone (AUC = 0.622, Z = 3.919, P < 0.05) or indices derived from CT alone (AUC = 0.727, 0.668, 0.695. Z = 3.620, 5.356, 3.696, P < 0.05).

CONCLUSIONS

We proposed a combination diagnosis method that can better predict PLN metastasis for patients with early-stage cervical cancer. In PET-negative cases, combination diagnosis of TLG of tumor and CT indicators also produced improved prediction by reducing false-negative cases of diagnosis. This combination diagnosis approach has significant implications in cervical cancer patient management and treatment planning.