Relationship between imaging biomarkers of stage I cervical cancer and poor-prognosis histologic features: quantitative histogram analysis of diffusion-weighted MR images

Does volumetric measurement of ADC values achieve higher diagnostic performance in bladder cancer MRI?

BACKGROUND

Apparent diffusion coefficient (ADC) value is an important part of bladder cancer magnetic resonance imaging (MRI) assessment and can predict the aggressive and invasive potentials. There is growing interest in whole tumor volume measurements.

PURPOSE

To investigate if the volumetric ADC measurement method will significantly exceed the diagnostic performance of the selected region of interest (ROI) method in everyday practice.

MATERIAL AND METHODS

A prospective evaluation was carried out of 50 patients with bladder cancer by two radiologists. The mean and the minimum ADC values were measured using both methods. The inter-reader agreement was determined by the intraclass correlation coefficient. The ADC values were compared between different grades, states of muscle invasion, and lympho-vascular invasion (LVI); then, validity was evaluated using receiver operating characteristic (ROC) curves. Areas under the curve (AUC) were then compared for the level of statistical significance.

RESULTS

The inter-observer agreement was excellent for the ADC values using both methods. The volumetric measurement provides higher mean and lower minimum ADC values with statistically significant differences (P <0.00001). The highest diagnostic accuracy for differentiating tumor grade and predicting muscle invasion was for the minimum ADC by a selected ROI. However, the differences between the achieved AUCs were of no statistical significance. None of the ADC values predicted LVI with statistical significance.

CONCLUSION

The selected ROI and volumetric measurement methods of mean and minimum ADC in bladder cancer yield different values, still having comparable diagnostic performance with accurate ROI sampling. The minimum ADC value by ROI is preferred in everyday clinical practice.

Whole-tumor histogram analysis of apparent diffusion coefficients for predicting lymphovascular space invasion in stage IB-IIA cervical cancer

Objectives

To investigate the value of apparent diffusion coefficient (ADC) histogram analysis based on whole tumor volume for the preoperative prediction of lymphovascular space invasion (LVSI) in patients with stage IB-IIA cervical cancer.

Methods

Fifty consecutive patients with stage IB-IIA cervical cancer were stratified into LVSI-positive (n = 24) and LVSI-negative (n = 26) groups according to the postoperative pathology. All patients underwent pelvic 3.0T diffusion-weighted imaging with b-values of 50 and 800 s/mm² preoperatively. Whole-tumor ADC histogram analysis was performed. Differences in the clinical characteristics, conventional magnetic resonance imaging (MRI) features, and ADC histogram parameters between the two groups were analyzed. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of ADC histogram parameters in predicting LVSI.

Results

ADC_max, ADC_range, ADC₉₀, ADC₉₅, and ADC₉₉ were significantly lower in the LVSI-positive group than in the LVSI-negative group (all P-values < 0.05), whereas no significant differences were reported for the remaining ADC parameters, clinical characteristics, and conventional MRI features between the groups (all P-values > 0.05). For predicting LVSI in stage IB-IIA cervical cancer, a cutoff ADC_max of 1.75×10^-3 mm²/s achieved the largest area under ROC curve (A_z) of 0.750, followed by a cutoff ADC_range of 1.36×10^-3 mm²/s and ADC₉₉ of 1.75×10^-3 mm²/s (A_z = 0.748 and 0.729, respectively), and the cutoff ADC₉₀ and ADC₉₅ achieved an A_z of <0.70.

Conclusion

Whole-tumor ADC histogram analysis has potential value for preoperative prediction of LVSI in patients with stage IB-IIA cervical cancer. ADC_max, ADC_range, and ADC₉₉ are promising prediction parameters.

Prediction of prognostic factors in breast cancer: A noninvasive method utilizing histogram parameters derived from Adc maps

Objective

The aim of this study is to investigate the relationship between histogram parameters and prognostic factors of breast cancer and to reveal the diagnostic performance of histogram parameters in predicting prognostic factors status.

Materials and methods

Ninety-two patients with a confirmed histopathological diagnosis of breast cancer were included in the study. Magnetic resonance imaging (MRI) was performed using a 1.5T scanner and two different b values were used for diffusion-weighted imaging (DWI) (b values: 0 s/mm², b: 800 s/mm²). For 3D histogram analysis, regions of interest (ROI) were drawn each slice of the lesion on apparent diffusion coefficient (ADC) maps. The following data were derived from the histogram analysis data: percentiles, skewness, kurtosis, and entropy. The relationship between prognostic factors and histogram analysis data was investigated using the Kolmogorov-Smirnov test, Shapiro-Wilk test, skewness-kurtosis test, independent t-test, Mann-Whitney U test, and Kruskal-Wallis test. Receiver operator characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the histogram parameters.

Results

ADC_max, kurtosis, and entropy parameters were statistically significantly correlated with tumor diameter (p = 0.002, p = 0.008, and p = 0.001, respectively). There was a significant difference in ADC_90% and ADC_max values, depending on estrogen receptor (ER) and progesterone receptor (PR) status. These values were lower in ER- and PR-positive than ER- and PR-negative patients (p = 0.02 and p = 0.001 vs. p = 0.018, p = 0.008). All ADC percentage values were lower in patients with a positive Ki-67 proliferation index as compared with those with a negative Ki-67 proliferation index (all p = 0.001). The entropy value was high in high-grade lesions and lesions with axillary involvement (p = 0.039 and p = 0.048, respectively). The highest area under the curve (AUC) for ER and PR status was calculated for the ADC_90% value with ROC curve analysis. The highest AUC for Ki-67 proliferation index was found for the ADC_50%.

Conclusion

Histogram analysis parameters derived from of ADC maps of whole lesions can reflect histopathological features of the tumors. Based on our study, it was concluded that histogram analysis parameters were related to the prognostic factors of the tumor.

Imaging biomarkers for clinical applications in neuro-oncology: current status and future perspectives

Biomarker discovery and development are popular for detecting the subtle diseases. However, biomarkers are needed to be validated and approved, and even fewer are ever used clinically. Imaging biomarkers have a crucial role in the treatment of cancer patients because they provide objective information on tumor biology, the tumor's habitat, and the tumor's signature in the environment. Tumor changes in response to an intervention complement molecular and genomic translational diagnosis as well as quantitative information. Neuro-oncology has become more prominent in diagnostics and targeted therapies. The classification of tumors has been actively updated, and drug discovery, and delivery in nanoimmunotherapies are advancing in the field of target therapy research. It is important that biomarkers and diagnostic implements be developed and used to assess the prognosis or late effects of long-term survivors. An improved realization of cancer biology has transformed its management with an increasing emphasis on a personalized approach in precision medicine. In the first part, we discuss the biomarker categories in relation to the courses of a disease and specific clinical contexts, including that patients and specimens should both directly reflect the target population and intended use. In the second part, we present the CT perfusion approach that provides quantitative and qualitative data that has been successfully applied to the clinical diagnosis, treatment and application. Furthermore, the novel and promising multiparametric MR imageing approach will provide deeper insights regarding the tumor microenvironment in the immune response. Additionally, we briefly remark new tactics based on MRI and PET for converging on imaging biomarkers combined with applications of bioinformatics in artificial intelligence. In the third part, we briefly address new approaches based on theranostics in precision medicine. These sophisticated techniques merge achievable standardizations into an applicatory apparatus for primarily a diagnostic implementation and tracking radioactive drugs to identify and to deliver therapies in an individualized medicine paradigm. In this article, we describe the critical principles for imaging biomarker characterization and discuss the current status of CT, MRI and PET in finiding imaging biomarkers of early disease.

T1 mapping and extracellular volume fraction measurement to evaluate the poor-prognosis factors in patients with cervical squamous cell carcinoma

PURPOSE

To evaluate the clinical feasibility of T₁ mapping and extracellular volume fraction (ECV) measurement in assessing prognostic factors in patients with cervical squamous cell carcinoma (CSCC).

MATERIALS AND METHODS

A total of 117 CSCC patients and 59 healthy volunteers underwent T₁ mapping and diffusion-weighted imaging (DWI) on a 3 T system. Native T₁ , contrast-enhanced T₁ , ECV, and apparent diffusion coefficient (ADC) were calculated and compared based on surgico-pathologically verified deep stromal infiltration, parametrial invasion (PMI), lymphovascular space invasion (LVSI), lymph node metastasis, stage, histologic grade, and the Ki-67 labeling index (LI).

RESULTS

Native T₁ , contrast-enhanced T₁ , ECV, and ADC values were significantly different between CSCC and the normal cervix (all p < 0.05). No significant differences were observed in any parameters of CSCC when the tumors were grouped by stromal infiltration or lymph node status, respectively (all p > 0.05). In subgroups of the tumor stage and PMI, native T₁ was significantly higher for advanced-stage (p = 0.032) and PMI-positive CSCC (p = 0.001). In subgroups of the grade and Ki-67 LI, contrast-enhanced T₁ was significantly higher for high-grade (p = 0.012) and Ki-67 LI ≥ 50% tumors (p = 0.027). ECV was significantly higher in LVSI-positive CSCC than in LVSI-negative CSCC (p < 0.001). ADC values showed a significant difference for the grade (p < 0.001) but none for the other subgroups.

CONCLUSION

Both T₁ mapping and DWI could stratify the CSCC histologic grade. In addition, T₁ mapping and ECV measurement might provide more quantitative metrics for noninvasively predicting poor prognostic factors and aiding in preoperative risk assessment in CSCC patients.

Whole-Lesion Apparent Diffusion Coefficient Histogram Analysis for Assessing Normal-Sized Lymph Node Metastasis in Cervical Cancer: Comparison Between Readout-Segmented and Single-Shot Echo-Planar Diffusion-Weighted Imaging

OBJECTIVE

To compare the value of whole-lesion apparent diffusion coefficient (ADC) histogram analysis derived from readout-segmented echo-planar imaging (RS-EPI) and single-shot echo-planar imaging (SS-EPI) diffusion-weighted imaging (DWI) in evaluating normal-sized lymph node metastasis (LNM) in cervical cancer.

METHODS

Seventy-six pathologically confirmed cervical cancer patients (stages IB and IIA) were enrolled, including 61 patients with non-LNM (group A) and 15 patients with normal-sized LNM (group B). The recorded tumor volume on T2-weighted imaging was the reference against which both DWIs were evaluated. Each ADC histogram parameter (including ADCmax, ADC90, ADCmedian, ADCmean, ADC10, ADCmin, ADCskewness, ADCkurtosis, and ADCentropy) was compared between SS-EPI and RS-EPI and between the 2 groups.

RESULTS

There was no significant difference in tumor volume between the 2 DWIs and T2-weighted imaging (both P > 0.05). Higher ADCmax and ADCentropy but lower ADC10, ADCmin and ADCskewness were found in SS-EPI than those in RS-EPI (all P < 0.05). For SS-EPI, lower ADC90 and higher ADCkurtosis were found in group B than those in group A (both P < 0.05). For RS-EPI, lower ADC90 and higher ADCkurtosis and ADCentropy were found in group B than those in group A (all P < 0.05). Readout-segmented echo-planar imaging ADCkurtosis showed the highest area under the curve of 0.792 in the differentiation of the 2 groups (sensitivity, 80%; specificity, 73.77%).

CONCLUSIONS

Compared with SS-EPI, the ADC histogram parameters derived from RS-EPI were more accurate, and ADCkurtosis held great potential in differentiating normal-sized LNM in cervical cancer.

Multiparametric MRI-based radiomics analysis: differentiation of subtypes of cervical cancer in the early stage

BACKGROUND

There are significant differences in outcomes for different histological subtypes of cervical cancer (CC). Yet, it is difficult to distinguish CC subtypes using non-invasive methods.

PURPOSE

To investigate whether multiparametric magnetic resonance imaging (MRI)-based radiomics analysis can differentiate CC subtypes and explore tumor heterogeneity.

MATERIAL AND METHODS

This study retrospectively analyzed 96 patients with CC (squamous cell carcinoma [SCC] = 50, adenocarcinoma [AC] = 46) who underwent pelvic MRI before surgery. Radiomics features were extracted from the tumor volumes on five sequences (sagittal T2-weighted imaging [T2SAG], transverse T2-weighted imaging [T2TRA], sagittal contrast-enhanced T1-weighted imaging [CESAG], transverse contrast-enhanced T1-weighted imaging [CETRA], and apparent diffusion coefficient [ADC]). Clustering and logistic regression were used to examine the distinguishing capabilities of radiomics features extracted from five different MR sequences.

RESULTS

Among the 105 extracted radiomics features, there were 51, 38, 37, and 2 features that showed intergroup differences for T2SAG, T2TRA, ADC, and CESAG, respectively (all P < 0.05). AC had greater textural heterogeneity than SCC (P < 0.05). Upon unsupervised clustering of significantly different features, T2SAG achieved the highest accuracy (0.844; sensitivity = 0.920; specificity = 0.761). The largest area under the curve (AUC) for classification ability was 0.86 for T2SAG. Hence, the radiomics model from five combined MR sequences (AUC = 0.89; accuracy = 0.81; sensitivity = 0.67; specificity = 0.94) exhibited better differentiation ability than any MR sequence alone.

CONCLUSION

Multiparametric MRI-based radiomics models may be a promising method to differentiate AC and SCC. AC showed more heterogeneous features than SCC.

Volumetric apparent diffusion coefficient (ADC) histogram metrics as imaging biomarkers for pretreatment predicting response to fertility-sparing treatment in patients with endometrial cancer

OBJECTIVES

To investigate the feasibility of volumetric apparent diffusion coefficient (ADC) histogram analysis for prediction of fertility-sparing treatment (FST) response in patients with endometrial cancer (EC).

METHODS

Pretreatment data of 54 EC patients with FST were retrospectively analyzed. Treatment response at each follow-up was pathologically evaluated. The associations of ADC histogram metrics (volume, minADC, maxADC, meanADC; 10th, 25th, 50th, 75th and 90th ADC percentiles; skewness; kurtosis) and baseline clinical characteristics with complete response (CR) at the second and third follow-ups, two-consecutive CR, and recurrence at the final follow-up were evaluated by uni- and multivariable logistic regression analysis. Receiver operating characteristic (ROC) curve analysis was used for diagnostic performance evaluation.

RESULTS

Compared with non-CR patients, CR patients had significantly higher minADC and 10th and 25th ADC percentiles at the second follow-up (P = 0.008, 0.039, and 0.034, respectively) and higher minADC, older age, lower HE4 level, and higher overweight rate at the third follow-up (P = 0.001, 0.040, 0.021, and 0.004, respectively). Patients with two-consecutive CR had a significantly higher minADC than those without (P = 0.018). There was no association between ADC metrics or clinical characteristics and recurrence (all P > 0.05). MinADC yielded the largest AUC in predicting CR (0.688 and 0.735 at the second and third follow-up, respectively) and the presence of two-consecutive CR (0.753). When combined with patient age and HE4 level, the prediction of CR could be further improved at the third follow-up, with an AUC of 0.786.

CONCLUSION

Pretreatment minADC could be a potential imaging biomarker for predicting FST response. Clinical characteristics may have incremental value to minADC in predicting CR.

Added-value of texture analysis of ADC in predicting the survival of patients with 2018 FIGO stage IIICr cervical cancer treated by concurrent chemoradiotherapy

PURPOSE

To investigate the value of texture analysis of ADC in predicting the survival of patients with 2018 International Federation of Gynecology and Obstetrics (FIGO) stage IIICr cervical squamous cell cancer (CSCC) treated with concurrent chemoradiotherapy (CCRT).

METHODS

A total of 91 patients with stage IIICr CSCC treated by CCRT between January 2014 and December 2018 were retrospectivelyenrolled in this study. Clinical variables and 21 first-order texture features extracted from ADC maps were collected. Univariate and multivariate Cox hazard regression analyses were performed to evaluate these parameters in predicting progression-free survival (PFS) and overall survival (OS). The independent variables were combined to build a prediction model and compared with the 2018 FIGO staging system. Survival curves were generated using the Kaplan-Meier method, and the log-rank test was used for comparison.

RESULTS

Mean Absolute Deviation (MAD), T stage, and the number of lymph node metastasis (LNM) were independently associated with PFS, while MAD, energy, T stage, number of LNM, and tumor grade were independently associated with OS. The C-index values of the combined models for PFS and OS, which were respectively 0.750 and 0.832, were significantly higher compared to 2018 FIGO staging system values of 0.629 and 0.630, respectively (P < 0.05).

CONCLUSIONS

The texture analysis of the ADC maps could be used along with clinical prognostic biomarkers to predict PFS and OS in patients with stage IIICr CSCC treated by CCRT.

Prediction of overall survival in patients with pancreatic ductal adenocarcinoma: histogram analysis of ADC value and correlation with pathological intratumoral necrosis

BACKGROUND

To evaluate the utility of histogram analysis (HA) of apparent diffusion coefficient (ADC) values to predict the overall survival (OS) in patients with pancreatic ductal adenocarcinoma (PDAC) and to correlate with pathologically evaluated massive intratumoral necrosis (MITN).

MATERIALS AND METHODS

Thirty-nine patients were included in this retrospective study with surgically resected PDAC who underwent preoperative magnetic resonance imaging. Twelve patients received neoadjuvant chemotherapy. HA on the ADC maps were performed to obtain the tumor HA parameters. Using Cox proportional regression analysis adjusted for age, time-dependent receiver-operating-characteristic (ROC) curve analysis, and Kaplan-Meier estimation, we evaluated the association between HA parameters and OS. The association between prognostic factors and pathologically confirmed MITN was assessed by logistic regression analysis.

RESULTS

The median OS was 19.9 months. The kurtosis (P < 0.001), entropy (P = 0.013), and energy (P = 0.04) were significantly associated with OS. The kurtosis had the highest area under the ROC curve (AUC) for predicting 3-year survival (AUC 0.824) among these three parameters. Between the kurtosis and MITN, the logistic regression model revealed a positive correlation (P = 0.045). Lower survival rates occurred in patients with high kurtosis (cutoff value > 2.45) than those with low kurtosis (≤ 2.45) (P < 0.001: 1-year survival rate, 75.2% versus 100%: 3-year survival rate, 14.7% versus 100%).

CONCLUSIONS

HA derived kurtosis obtained from tumor ADC maps might be a potential imaging biomarker for predicting the presence of MITN and OS in patients with PDAC.

Multi-Parametric Magnetic Resonance Imaging-Based Radiomics Analysis of Cervical Cancer for Preoperative Prediction of Lymphovascular Space Invasion

Background

Detection of lymphovascular space invasion (LVSI) in early cervical cancer (CC) is challenging. To date, no standard clinical markers or screening tests have been used to detect LVSI preoperatively. Therefore, non-invasive risk stratification tools are highly desirable.

Objective

To train and validate a multi-parametric magnetic resonance imaging (mpMRI)-based radiomics model to detect LVSI in patients with CC and investigate its potential as a complementary tool to enhance the efficiency of risk assessment strategies.

Materials and Methods

The model was developed from the tumor volume of interest (VOI) of 125 patients with CC. A total of 1037 radiomics features obtained from conventional magnetic resonance imaging (MRI), including a small field-of-view (sFOV) high-resolution (HR)-T2-weighted MRI (T2WI), apparent diffusion coefficient (ADC), T2WI, fat-suppressed (FS)-T2WI, as well as axial and sagittal contrast-enhanced T1-weighted MRI (T1c). We conducted a radiomics-based characterization of each tumor region using pretreatment image data. Feature selection was performed using the least absolute shrinkage and selection operator method on the training set. The predictive performance was compared with single variates (clinical data and single-layer radiomics signatures) analyzed using a receiver operating characteristic (ROC) curve. Three-fold cross-validation performed 20 times was used to evaluate the accuracy of the trained classifiers and the stability of the selected features. The models were validated by using a validation set.

Results

Feature selection extracted the six most important features (3 from sFOV HR-T2WI, 1 T2WI, 1 FS-T2WI, and 1 T1c) for model construction. The mpMRI-combined radiomics model (area under the curve [AUC]: 0.940) reached a significantly higher performance (better than the clinical parameters [AUC: 0.730]), including any single-layer model using sFOV HR-T2WI (AUC: 0.840), T2WI (AUC: 0.770), FS-T2WI (AUC: 0.710), ADC maps (AUC: 0.650), sagittal, and axial T1c values (AUC: 0.710, 0.680) in the validation set.

Conclusion

Biomarkers using multi-parametric radiomics features derived from preoperative MR images could predict LVSI in patients with CC.

MRI Radiomic Features: A Potential Biomarker for Progression-Free Survival Prediction of Patients With Locally Advanced Cervical Cancer Undergoing Surgery

Objectives

To investigate the prognostic role of radiomic features based on pretreatment MRI in predicting progression-free survival (PFS) of locally advanced cervical cancer (LACC).

Methods

All 181 women with histologically confirmed LACC were randomly divided into the training cohort (n = 126) and the validation cohort (n = 55). For each patient, we extracted radiomic features from whole tumors on sagittal T2WI and axial DWI. The least absolute shrinkage and selection operator (LASSO) algorithm combined with the Cox survival analysis was applied to select features and construct a radiomic score (Rad-score) model. The cutoff value of the Rad-score was used to divide the patients into high- and low-risk groups by the X-tile. Kaplan–Meier analysis and log-rank test were used to assess the prognostic value of the Rad-score. In addition, we totally developed three models, the clinical model, the Rad-score, and the combined nomogram.

Results

The Rad-score demonstrated good performance in stratifying patients into high- and low-risk groups of progression in the training (HR = 3.279, 95% CI: 2.865–3.693, p < 0.0001) and validation cohorts (HR = 2.247, 95% CI: 1.735–2.759, p < 0.0001). Otherwise, the combined nomogram, integrating the Rad-score and patient’s age, hemoglobin, white blood cell, and lymph vascular space invasion, demonstrated prominent discrimination, yielding an AUC of 0.879 (95% CI, 0.811–0.947) in the training cohort and 0.820 (95% CI, 0.668–0.971) in the validation cohort. The Delong test verified that the combined nomogram showed better performance in estimating PFS than the clinical model and Rad-score in the training cohort (p = 0.038, p = 0.043).

Conclusion

The radiomics nomogram performed well in individualized PFS estimation for the patients with LACC, which might guide individual treatment decisions.

Whole-body diffusion-weighted MRI of normal lymph nodes: prospective apparent diffusion coefficient histogram and nodal distribution analysis in a healthy cohort

BACKGROUND

Whole body DWI (WB-DWI) enables the identification of lymph nodes for disease evaluation. However, quantitative data of benign lymph nodes across the body are lacking to allow meaningful comparison of diseased states. We evaluated apparent diffusion coefficient (ADC) histogram parameters of all visible lymph nodes in healthy volunteers on WB-DWI and compared differences in nodal ADC values between anatomical regions.

METHODS

WB-DWI was performed on a 1.5 T MR system in 20 healthy volunteers (7 female, 13 male, mean age 35 years). The b900 images were evaluated by two radiologists and all visible nodes from the neck to groin areas were segmented and individual nodal median ADC recorded. All segmented nodes in a patient were summated to generate the total nodal volume. Descriptors of the global ADC histogram, derived from individual node median ADCs, including mean, median, skewness and kurtosis were obtained for the global volume and each nodal region per patient. ADC values between nodal regions were compared using one-way ANOVA with Bonferroni post hoc tests and a p-value ≤0.05 was deemed statistically significant.

RESULTS

One thousand sixty-seven lymph nodes were analyzed. The global mean and median ADC of all lymph nodes were 1.12 ± 0.27 (10^- 3 mm²/s) and 1.09 (10^- 3 mm²/s). The average median ADC skewness was 0.25 ± 0.02 and average median ADC kurtosis was 0.34 ± 0.04. The ADC values of intrathoracic, portal and retroperitoneal nodes were significantly higher (1.53 × 10^- 3, 1.75 × 10^- 3 and 1.58 × 10^- 3 mm²/s respectively) than in other regions. Intrathoracic, portal and mesenteric nodes were relatively uncommon, accounting for only 3% of the total nodes segmented.

CONCLUSIONS

The global mean and median ADC of all lymph nodes were 1.12 ± 0.27 (10^- 3 mm²/s) and 1.09 (10^- 3 mm²/s). Intrathoracic, portal and retroperitoneal nodes display significantly higher ADCs. Normal intrathoracic, portal and mesenteric nodes are infrequently visualized on WB-DWI of healthy individuals.

TRIAL REGISTRATION

Royal Marsden Hospital committee for clinical research registration number 09/H0801/86, 19.10.2009.

Using quantitative MRI to study brain responses to immune challenge with interferon-α

Inflammatory processes in the Central Nervous System (CNS) have been proposed to mediate the association between peripheral inflammation and the development of psychiatric disorders, but we currently lack sensitive measures of CNS inflammation for human studies in vivo. Here we used quantitative MRI (qMRI) to explore the in vivo central response to a peripheral immune challenge in healthy humans, and we assessed whether changes in quantitative relaxometry MRI parameters were associated with changes in peripheral inflammation. Quantitative relaxation times (T1 & T2) and Proton Density (PD) were measured in n ​= ​6 healthy males (mean age ​= ​30.5 ​± ​6.8 years) in two MRI assessments collected before and 24 ​hours after a subcutaneous injection of the proinflammatory cytokine and immune activator, interferon-alpha (IFN-α). Serum levels of immune markers and markers of blood-brain barrier integrity (S100B) were also measured before and after the injection. Region of interest and histogram-based analyses (optimized for the small sample size) showed a statistically significant increase of both T1 (t₍₅₎ ​= ​3.78, p ​= 0.013) and PD (t₍₅₎ ​= ​2.91, p ​= ​0.033) parameters in the bilateral hippocampus after IFN-α administration. T1 peak values in bilateral hippocampus were positively correlated with serum Tumour Necrosis Factor-alpha levels at 24 ​h after the injection, when this cytokine peaked (Spearman's rho ​= ​0.67, p ​= ​0.018) and negatively correlated with S100B levels (Spearman's rho ​= ​-0.826, p ​= ​0.001). Our data suggest that peripheral administration of IFN-α produces acute changes in brain qMRI which might indicate a brain immune response. This is supported by the association of such changes with low-grade peripheral inflammation.

Application of first-order feature analysis of DWI-ADC in rare malignant mesenchymal tumours of the maxillofacial region

BACKGROUND

To research the first-order features of apparent diffusion coefficient (ADC) values on diffusion-weighted magnetic resonance imaging (DWI) in maxillofacial malignant mesenchymal tumours.

METHODS

The clinical data of 12 patients with rare malignant mesenchymal tumours of the maxillofacial region (6 cases of sarcoma and 6 cases of lymphoma) treated in the hospital from May 2018 to June 2020 and were confirmed by postoperative pathology were retrospectively analyzed. The patients were all examined by 1.5T magnetic resonance imaging. PyRadiomics were used to extract radiomics imaging first-order features. Group differences in quantitative variables were examined using independent-samples t-tests.

RESULTS

The voxels number of ADC_mean and ADC_median of sarcoma tissues were 44.9124 and 44.2064, respectively, significantly higher than those in lymphoma tissues (ADC_mean (- 68.8379) and ADC_median (- 74.0045)), the difference considered statistically significant, so do the ADC_kurt and ADC_skew.

CONCLUSIONS

The statistical difference of ADC_mean and ADC_median is significant, it is consistent with the outcome of the manual measurement of the ADC mean value of the most significant cross-section of twelve cases of lymphoma. Development of tumour volume based on the ADC parameter map of DWI demonstrates that the first-order ADC radiomics features analysis can provide new imaging markers for the differentiation of maxillofacial sarcoma and lymphoma. Therefore, first-order ADC features of ADC_kurt combined ADC_skew may improve the diagnosis level.

Diagnostic Accuracy of Magnetic Resonance Imaging for International Federation of Gynecology and Obstetrics 2018 IB to IIB Cervical Cancer Staging: Comparison Among Magnetic Resonance Sequences and Pathologies

OBJECTIVE

This study aimed to investigate the most accurate magnetic resonance (MR) sequence for tumor detection, maximal tumor diameter, and parametrial invasion compared with histopathologic diagnoses.

METHODS

Fifty-one patients with International Federation of Gynecology and Obstetrics 2018 IB1 to IIB cervical cancer underwent preoperative MR imaging and surgical resection. Two radiologists independently evaluated the tumor detection, parametrial invasion, and tumor size in each of T2-weighted image, diffusion-weighted image, and contrast-enhanced T1-weighted image. Results obtained for squamous cell carcinoma (SCC) and adenocarcinoma were also compared.

RESULTS

Neither the tumor detection rate nor parametrial invasion was found to be significantly different among sequences. Tumor size assessment using MR imaging with pathology showed good correlation: r = 0.63-0.72. The adenocarcinoma size tended to be more underestimated than SCC in comparison with the pathologic specimen.

CONCLUSIONS

Cervical cancer staging by MR images showed no significant difference among T2-weighted image, diffusion-weighted image, and contrast-enhanced T1-weighted image. Adenocarcinoma was prone to be measured as smaller than the pathologic specimen compared with SCC.

Volumetric ADC histogram analysis for preoperative evaluation of LVSI status in stage I endometrioid adenocarcinoma

OBJECTIVES

To investigate the value of volumetric ADC histogram metrics for evaluating lymphovascular space invasion (LVSI) status in stage I endometrioid adenocarcinoma (EAC).

METHODS

Preoperative MRI of 227 patients with stage I EAC were retrospectively analyzed. ADC histogram data were derived from the whole tumor with ROIs drawn on all slices of DWI scans (b = 0, 1000 s/mm²). The Student t-test was performed to compare ADC histogram metrics (minADC, maxADC, and meanADC; 10th, 25th, 50th, 75th, and 90th percentiles of ADC; skewness; and kurtosis) between the LVSI-positive and LVSI-negative groups, as well as between stage Ia and Ib EACs. ROC curve analysis was carried out to evaluate the diagnostic performance of ADC histogram metrics in predicting LVSI status in EAC.

RESULTS

The minADC and meanADC and 10th, 25th, 50th, 75th, and 90th percentiles of ADC were significantly lower in LVSI-positive EACs compared with those in the LVSI-negative groups for stage I, Ia, and Ib EACs (all p < 0.05). MeanADC ≤ 0.857 × 10^-3 mm²/s, meanADC ≤ 0.854 × 10^-3 mm²/s, and the 90th percentile of ADC ≤ 1.06 × 10^-3 mm²/s yielded the largest AUC of 0.844, 0.844, and 0.849 for evaluating LVSI positivity in stage I, Ia, and Ib tumors, respectively, with sensitivity of 75.4%, 75.0%, and 76.2%; specificity of 80.0%, 83.1%, and 82.1%; and accuracy of 79.3%, 81.5%, and 79.6%, respectively.

CONCLUSION

Volumetric ADC histogram metrics might be helpful for the preoperative evaluation of LVSI status and personalized clinical management in patients with stage I EAC.

KEY POINTS

• Volumetric ADC histogram analysis helps evaluate LVSI status preoperatively. • LVSI-positive EAC is associated with a reduction in multiple volumetric ADC histogram metrics. • MeanADC and the 90th percentile of ADC were shown to be best in evaluating LVSI- positivity in stage Ia and Ib EACs, respectively.

Overview of fertility sparing treatments for cervical cancer

Until the late 1980s, the mainstay of treatment for cervical cancer has been either hysterectomy or radiotherapy. From the mid to late 1990s, surgical treatments have been focussed more on sparing fertility by preserving the corpus of the womb with trachelectomy or even conserving part of the cervical stroma with a cone biopsy. In carefully selected cases, less radical treatment that preserves the uterus has been considered safe. However, these approaches can be associated with specific operative and obstetric complications such as stitch ulceration, cervical stenosis, late miscarriage, and premature labour. Most guidelines agree that the management of such patients should be centralised in a unit with specialist gynaecological oncology, radiology, and histopathology services supported by specialist cancer nurses.

The role of volumetric ADC histogram analysis in preoperatively evaluating the tumour subtype and grade of endometrial cancer

PURPOSE

To assess the value of volumetric ADC histogram metrics in evaluating the histological subtype and grade of endometrial cancer.

METHOD

Preoperative MRI datasets of 317 patients with endometrial cancer were used to obtain volumetric ADC histogram metrics (tumour volume; minADC, maxADC and meanADC; 10th, 25th, 50th, 75th and 90th percentiles of ADC; skewness; and kurtosis). The Mann-Whitney test or Student's t-test was used to compare the difference in ADC histogram metrics between endometrioid adenocarcinomas (EACs) and serous endometrial cancers (SECs) and between different tumour grades (G1, G2, G3). The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to evaluate the performance of ADC histogram metrics or combined models in predicting the tumour subtype and grade.

RESULTS

SECs showed a significantly larger tumour volume (P < 0.001) and lower meanADC, 50th, 75th and 90th percentiles of ADC than EACs (all P < 0.05). MinADC, maxADC, meanADC, 10th, 25th, 50th, 75th, 90th percentiles of ADC were significantly higher in G1 than in G2 and G3 EACs (all P < 0.05), while were not significantly different between G2 and G3 EACs (all P > 0.05). A tumour volume ≥ 7.752 cm³ allowed for the prediction of SECs, with an AUC of 0.765 (0.714-0.810). A meanADC ≥ 0.892 × 10^-3  mm²/s enabled to discriminate G1 from G2 and G3 EACs, with an AUC of 0.818 (0.769-0.861).

CONCLUSION

Volumetric ADC histogram analysis is helpful for non-invasive preoperatively predicting the subtype of endometrial cancer and differentiating G1 from G2 and G3 EACs.

Association between IVIM parameters and treatment response in locally advanced squamous cell cervical cancer treated by chemoradiotherapy

OBJECTIVE

To examine the associations of intravoxel incoherent motion (IVIM) parameters with treatment response in cervical cancer following concurrent chemoradiotherapy (CCRT).

MATERIALS AND METHODS

Forty-five patients, median age of 58 years (range: 28-82), with pre-CCRT and post-CCRT MRI, were retrospectively analysed. The IVIM parameters pure diffusion coefficient (D) and perfusion fraction (f) were estimated using the full b-value distribution (BVD) as well as an optimised subsample BVD. Dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC) were used to measure observer repeatability in tumour delineation at both time points. Treatment response was determined by the response evaluation criteria in solid tumour (RECIST) 1.1 between MRI examinations. Mann-Whitney U tests were used to test for significant differences in IVIM parameters between treatment response groups.

RESULTS

Pre-CCRT tumour delineation repeatability was good (DSC = 0.81) while post-CCRT delineation repeatability was moderate (DSC = 0.67). Values of D and f had good repeatability at both time points (ICC > 0.80). Pre-CCRT f estimated using the full BVD and optimised subsample BVD were found to be significantly higher in patients with partial response compared to those with stable disease or disease progression (p = 0.01 and 95% CI = -0.02-0.00 for both cases).

CONCLUSION

Pre-CCRT f was associated with treatment response in cervical cancer with good observer repeatability. Similar discriminative ability was also observed in estimated pre-CCRT f from an optimised subsample BVD.

KEY POINTS

• Pre-treatment tumour delineation and IVIM parameters had good observer repeatability. • Post-treatment tumour delineation was worse than at pre-treatment, but IVIM parameters retained good ICC. • Pre-treatment perfusion fraction estimated from all b-values and an optimised subsample of b-values were associated with treatment response.

Value of conventional magnetic resonance imaging texture analysis in the differential diagnosis of benign and borderline/malignant phyllodes tumors of the breast

BACKGROUND

The purpose of this study was to determine the potential value of magnetic resonance imaging (MRI) texture analysis (TA) in differentiating between benign and borderline/malignant phyllodes tumors of the breast.

METHODS

The preoperative MRI data of 25 patients with benign phyllodes tumors (BPTs) and 19 patients with borderline/malignant phyllodes tumors (BMPTs) were retrospectively analyzed. A gray-level histogram and gray-level cooccurrence matrix (GLCM) were used for TA with fat-suppressed T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) images, and 2- and 7-min postcontrast T1W images on dynamic contrast-enhanced MRI (DCE-T1WI_2min and DCE-T1WI_7min) between BPTs and BMPTs. Independent sample t-test and Mann-Whitney U test were performed for intergroup comparison. A regression model was established by using binary logistic regression analysis, and receiver operating characteristic (ROC) curve analysis was carried out to evaluate diagnostic efficiency.

RESULTS

For ADC images, the texture parameters angular second moment (ASM), correlation, contrast, entropy and the minimum gray values of ADC images (ADC_Minimum) showed significant differences between the BPT group and BMPT group (all p<0.05). The parameter entropy of FS-T2WI and the maximum gray values and kurtosis of the tumor solid region of DCE-T1WI_7min also showed significant differences between these two groups. Except for ADC_Minimum, angular second moment of FS-T2WI (FS-T2WI_ASM), and the maximum gray values of DCE-T1WI_7min (DCE-T1WI_7min-Maximum) of the tumor solid region, the AUC values of other positive texture parameters mentioned above were greater than 0.75. Binary logistic regression analysis demonstrated that the contrast of ADC images (ADC_Contrast) and entropy of FS-T2WI (FS-T2WI_Entropy) could be considered independent texture variables for the differential diagnosis of BPTs and BMPTs. Combined, the AUC of these parameters was 0.891 (95% CI: 0.793-0.988), with a sensitivity of 84.2% and a specificity of up to 89.0%.

CONCLUSION

Texture analysis could be helpful in improving the diagnostic efficacy of conventional MR images in differentiating BPTs and BMPTs.

Volumetric histograms-based analysis of apparent diffusion coefficients and standard uptake values for the assessment of pediatric sarcoma at staging: preliminary results of a PET/MRI study

PURPOSE

To assess the relationship between apparent diffusion coefficients (ADC) and standard uptake values (SUV) of pediatric sarcomas at staging by using volumetric histograms analyses.

METHODS

Children with histologically proven sarcoma, referring to our tertiary center for a whole-body ¹⁸F-FDG PET/MRI for staging and including diffusion weighted imaging in the MRI protocol were investigated. Firstly, turbo inversion recovery magnitude (TIRM) and PET images were resliced and resampled according to the ADC maps. Regions of interests were drawn along tumor margins on TIRM images and then copied on PET and ADC datasets. Pixel-based SUVs and ADCs were collected from the entire volume of each lesion. Mean, median, skewness, and kurtosis of SUVs and ADCs values were computed, and the Pearson correlation coefficient was then applied (for the entire population and for histological subgroups with more than five patients).

RESULTS

Thirteen patients met the inclusion criteria (six females; mean age 8.31 ± 6.03 years). Histology revealed nine rhabdomyosarcomas, three Ewing sarcomas, and one chondroblastic osteosarcoma. A significant negative correlation between ADCs' and SUVs' mean (r_mean = - 0.501, P < 0.001), median (r_median = - 0.519, P < 0,001), and skewness (r_skewness = - 0.550, P < 0.001) emerged for the entire population and for rhabdomyosarcomas (r_mean = - 0.541, P = 0.001, r_median = - 0.597, P < 0.001, r_skewness = - 0.568, P < 0.001), whereas a significant positive correlation was found for kurtosis (r_kurtosis = 0.346, P < 0.001, and r_kurtosis = 0.348, P < 0.001 for the entire population and for rhabdomyosarcomas, respectively).

CONCLUSION

Our preliminary results demonstrate that, using volumetric histograms, simultaneously collected SUVs and ADCs are dependent biomarkers in pediatric FDG-avid sarcomas. Further studies, on a larger population, are necessary to confirm this evidence and assess its clinical implications.

Influence of menstrual status and pathological type on the apparent diffusion coefficient in cervical cancer: a primary study

BACKGROUND

Apparent diffusion coefficient (ADC) value is an important quantitative parameter in the research of cervical cancer, affected by some factors.

PURPOSE

To investigate the effect of pathological type and menstrual status on the ADC value of cervical cancer.

MATERIAL AND METHODS

A total of 352 individuals with pathologically confirmed cervical cancer between January 2015 to December 2017 were retrospectively enrolled in this study, including 317 cases with squamous cell carcinomas (SCC) and 35 cases with adenocarcinomas (AC); 177 patients were non-menopausal and 175 were menopausal. All patients underwent a routine 3.0-T magnetic resonance imaging (MRI) scan and diffusion-weighted imaging (DWI) examination using b-values of 0, 800, and 1000 s/mm². Three parameters including mean ADC (ADC_mean), maximum ADC (ADC_max), and minimum ADC (ADC_min) of cervical cancer lesions were measured and retrospectively analyzed. Independent samples t-test was used to compare the difference of ADC values in different menstrual status and pathological types.

RESULTS

In all menopausal and non-menopausal patients, the ADC_mean and ADC_min values of SCC were lower than those of AC (P<0.05), the ADC_max of two pathological types showed no statistical difference (P > 0.05). In menopausal patients, the ADC_mean, ADC_max, and ADC_min values of SCC were not statistically different compared with those of AC (P > 0.05). The ADC_mean, ADC_max, and ADC_min values of different pathological types cervical cancers in non-menopausal patients were all higher than those in menopausal patients (P<0.05).

CONCLUSION

The ADC values of the cervical cancers were different in different pathological types and were also affected by menstrual status.

Heterogeneity analysis of MRI T2 maps for measurement of early tumor response to radiotherapy

External beam radiotherapy (XRT) is a widely used cancer treatment, yet responses vary dramatically among patients. These differences are not accounted for in clinical practice, partly due to a lack of sensitive early response biomarkers. We hypothesize that quantitative magnetic resonance imaging (MRI) measures reflecting tumor heterogeneity can provide a sensitive and robust biomarker of early XRT response. MRI T2 mapping was performed every 72 hours following 10 Gy dose XRT in two models of pancreatic cancer propagated in the hind limb of mice. Interquartile range (IQR) of tumor T2 was presented as a potential biomarker of radiotherapy response compared with tumor growth kinetics, and biological validation was performed through quantitative histology analysis. Quantification of tumor T2 IQR showed sensitivity for detection of XRT-induced tumor changes 72 hours after treatment, outperforming T2-weighted and diffusion-weighted MRI, with very good robustness. Histological comparison revealed that T2 IQR provides a measure of spatial heterogeneity in tumor cell density, related to radiation-induced necrosis. Early IQR changes were found to correlate to subsequent tumor volume changes, indicating promise for treatment response prediction. Our preclinical findings indicate that spatial heterogeneity analysis of T2 MRI can provide a translatable method for early radiotherapy response assessment. We propose that the method may in future be applied for personalization of radiotherapy through adaptive treatment paradigms.

Apparent diffusion coefficient magnetic resonance imaging (ADC-MRI) in the axillary breast cancer lymph node metastasis detection: a narrative review

The presence of axillary lymph nodes metastases in breast cancer is the most significant prognostic factor, with a great impact on morbidity, disease-related survival and management of oncological therapies; for this reason, adequate imaging evaluation is strictly necessary. Physical examination is not enough sensitive to assess breast cancer nodal status; axillary ultrasonography (US) is commonly used to detect suspected or occult nodal metastasis, providing exclusively morphological evaluation, with low sensitivity and positive predictive value. Currently, sentinel lymph node biopsy (SLNB) and/or axillary dissection are the milestone for the diagnostic assessment of axillary lymph node metastases, although its related morbidity. The impact of magnetic resonance imaging (MRI) in the detection of nodal metastases has been widely investigated, as it continues to represent the most promising imaging modality in the breast cancer management. In particular, diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) values represent additional reliable non-contrast sequences, able to improve the diagnostic accuracy of breast cancer MRI evaluation. Several studies largely demonstrated the usefulness of implementing DWI/ADC MRI in the characterization of breast lesions. Herein, in the light of our clinical experience, we perform a review of the literature regarding the diagnostic performance and accuracy of ADC value as potential pre-operative tool to define metastatic involvement of nodal structures in breast cancer patients. For the purpose of this review, PubMed, Web of Science, and SCOPUS electronic databases were searched with different combinations of "axillary lymph node", "breast cancer", "MRI/ADC", "breast MRI" keywords. All original articles, reviews and metanalyses were included.

Impact of different scanners and acquisition parameters on robustness of MR radiomics features based on women's cervix

MR Radiomics based on cervical lesions from one single scanner has achieved promising results. However, it is a challenge to achieve clinical translation. Considering multi-scanners and non-uniform scanning parameters from different centers in a real-world medical scenario, we should first identify the influence of such conditions on the robustness of MR radiomics features (RFs) based on the female cervix. In this study, 9 healthy female volunteers were enrolled and 3 kiwis were selected as references. Each of them underwent T2 weighted imaging in three different 3.0-T MR scanners with uniform acquisition parameters, and in one MR scanner with various scanning parameters. A total of 396 RFs were extracted from their images with and without decile intensity normalization. The RFs’ reproducibility was evaluated by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). Representative features were selected using the hierarchical cluster analysis and their discrimination abilities were estimated by ROC analysis through retrospective comparison with the junctional zone and the outer muscular layer of healthy cervix in patients (n = 58) with leiomyoma. This study showed that only a few RFs were robust across different MR scanners and acquisition parameters based on females’ cervix, which might be improved by decile intensity normalization method.

Association between MRI histogram features and treatment response in locally advanced cervical cancer treated by chemoradiotherapy

OBJECTIVE

To examine the associations of histogram features of T2-weighted (T2W) images and apparent diffusion coefficient (ADC) with treatment response in locally advanced cervical cancer (LACC) following concurrent chemoradiotherapy (CCRT).

MATERIALS AND METHODS

Fifty-eight patients who underwent a 4-week CCRT regimen with MRI prior to treatment (pre-CCRT) and after treatment (post-CCRT) were retrospectively analysed. Histogram features were calculated from volumes of interest (VOIs) from one radiologist on T2W images and ADC maps. VOIs from two radiologists were used to assess observer repeatability in delineation and feature values at both time-points with the Dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC). Treatment response was defined as a 90% reduction in tumour volume. Paired Mann-Whitney U tests were used to determine if features changed significantly between examinations. Two-sample Mann-Whitney U tests were used to identify features that were significantly different between response groups. Receiver operating characteristic (ROC) analysis was done on significantly different MRI features between treatment response groups.

RESULTS

Pre-CCRT delineation and feature repeatability were generally good (DSC > 0.700; ICC > 0.750). Post-CCRT repeatability was low (DSC < 0.700; ICC < 0.750), but ADC mean and percentiles retained good ICC scores. All features, except for T2W_Kurtosis, significantly changed between examinations. Post-CCRT ADC₅₀ was the only feature that demonstrated both good observer variability and significant differences between treatment response groups (p = 0.036) and had an AUC of 0.701 with a cut-off of 1.357 × 10^-6 mm²/s.

CONCLUSION

ADC and T2W histogram features could be used to track changes in LACC tumours undergoing CCRT. Post-CCRT ADC₅₀ was associated with treatment response with good observer repeatability.

KEY POINTS

• Pre-treatment tumour delineation and histogram feature values had good observer repeatability, while these were less repeatable at post-treatment. • MRI histogram analysis could be used to track changes in the tumour as it undergoes concurrent chemoradiotherapy. • Post-treatment median ADC was associated with treatment response and had good repeatability.

Whole solid tumor volume histogram parameters for predicting the recurrence in patients with epithelial ovarian carcinoma: a feasibility study on quantitative DCE-MRI

BACKGROUND

Preoperative prediction of the recurrence of epithelial ovarian carcinoma (EOC) can guide the clinical treatment and improve the prognosis. However, there are still no reliable predictive biomarkers.

PURPOSE

To evaluate whether whole solid tumor volume histogram parameters measured from quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict the recurrence in patients with EOC.

MATERIAL AND METHODS

We followed up 56 patients with surgical and histopathologically diagnosed EOC who underwent quantitative DCE-MRI scans. The differences of the histogram parameters between patients with and without recurrence were compared. Mann-Whitney U test, Pearson's Chi-squared test, or Fisher's exact test, and receiver operating characteristic (ROC) curves were used for statistical analysis.

RESULTS

All histogram parameters of K^trans, k_ep, and v_e were not significantly different between EOC patients with and without recurrence (P>0.05). For 30 patients with high-grade serous ovarian carcinoma (HGSOC), the histogram parameters of K^trans (mean and 5th, 10th, 25th, 50th, 75th percentiles) and k_ep (mean and 50th percentile) in 12 patients with recurrence were significantly lower than those in 18 patients without recurrence (all P<0.05). ROC curves showed that the 5th percentile of K^trans had the largest area under the curve (AUC) of 0.792 for predicting the recurrence in patients with HGSOC. When the threshold value was ≤0.0263/min, the sensitivity, specificity, and accuracy were 100%, 66.7%, and 80%, respectively.

CONCLUSION

Instead of predicting the recurrence of EOC, whole solid tumor volume quantitative DCE-MRI histogram parameters could predict the recurrence of HGSOC and may be potential biomarkers for the prediction of HGSOC recurrence.

Tumor grade in soft-tissue sarcoma: Prediction with magnetic resonance imaging texture analysis

To determine the value of 3T magnetic resonance imaging (MRI) texture analysis in differentiating high- from low-grade soft-tissue sarcoma.Forty-two patients with soft-tissue sarcomas who underwent 3T MRI were analyzed. Qualitative and texture analysis were performed on T1-, T2- and fat-suppressed contrast-enhanced (CE) T1-weighted images. Various features of qualitative and texture analysis were compared between high- and low-grade sarcoma. Areas under the receiver operating characteristic curves (AUC) were calculated for texture features. Multivariate logistic regression analysis was used to analyze the value of qualitative and texture analysis.There were 11 low- and 31 high-grade sarcomas. Among qualitative features, signal intensity on T1-weighted images, tumor margin on T2-weighted images, tumor margin on fat-suppressed CE T1-weighted images and peritumoral enhancement were significantly different between high- and low-grade sarcomas. Among texture features, T2 mean, T1 SD, CE T1 skewness, CE T1 mean, CE T1 difference variance and CE T1 contrast were significantly different between high- and low-grade sarcomas. The AUCs of the above texture features were > 0.7: T2 mean, .710 (95% confidence interval [CI] .543-.876); CE T1 mean, .768 (.590-.947); T1 SD, .730 (.554-.906); CE T1 skewness, .751 (.586-.916); CE T1 difference variance, .721 (.536-.907); and CE T1 contrast, .727 (.530-.924). The multivariate logistic regression model of both qualitative and texture features had numerically higher AUC than those of only qualitative or texture features.Texture analysis at 3T MRI may provide additional diagnostic value to the qualitative MRI imaging features for the differentiation of high- and low-grade sarcomas.

Diffusion-weighted magnetic resonance imaging of primary cervical cancer in the detection of sub-centimetre metastatic lymph nodes

BACKGROUND

Magnetic resonance imaging (MRI) has limited accuracy in detecting pelvic lymph node (PLN) metastasis. This study aimed to examine the use of intravoxel incoherent motion (IVIM) in classifying pelvic lymph node (PLN) involvement in cervical cancer patients.

METHODS

Fifty cervical cancer patients with pre-treatment magnetic resonance imaging (MRI) were examined for PLN involvement by one subspecialist and one non-subspecialist radiologist. PLN status was confirmed by positron emission tomography or histology. The tumours were then segmented by both radiologists. Kruskal-Wallis tests were used to test for differences between diffusion tumour volume (DTV), apparent diffusion coefficient (ADC), pure diffusion coefficient (D), and perfusion fraction (f) in patients with no malignant PLN involvement, those with sub-centimetre and size-significant PLN metastases. These parameters were then considered as classifiers for PLN involvement, and were compared with the accuracies of radiologists.

RESULTS

Twenty-one patients had PLN involvement of which 10 had sub-centimetre metastatic PLNs. DTV increased (p = 0.013) while ADC (p = 0.015), and f (p = 0.006) decreased as the nodal status progressed from no malignant involvement to sub-centimetre and then size-significant PLN metastases. In determining PLN involvement, a classification model (DTV + f) had similar accuracies (80%) as the non-subspecialist (76%; p = 0.73) and subspecialist (90%; p = 0.31). However, in identifying patients with sub-centimetre PLN metastasis, the model had higher accuracy (90%) than the non-subspecialist (30%; p = 0.01) but had similar accuracy with the subspecialist (90%, p = 1.00). Interobserver variability in tumour delineation did not significantly affect the performance of the classification model.

CONCLUSION

IVIM is useful in determining PLN involvement but the added value decreases with reader experience.

Impact of different b-value combinations on radiomics features of apparent diffusion coefficient in cervical cancer

Background

The impact of variable b-value combinations on apparent diffusion coefficient (ADC)-based radiomics features has not been fully addressed in literature.

Purpose

To investigate the correlation between radiomics features extracted from ADC maps and various b-value combinations in cervical cancer.

Material and Methods

Diffusion-weighted images (b-values: 0, 600, 800, and 1000 s/mm2) of 20 patients with cervical cancer were included. Tumors were identified with the largest transversal cross-section and manually segmented by radiologist. For each b-value combination, 92 radiomics features were extracted and coefficient of variance (CV) was used to evaluate the robustness of radiomics features with different b-value combinations. Features with CV > 5% were normalized by the mean feature variation across the group.

Results

Out of a total of 92 radiomics features, 18 were classified as robust features with CV ≤5%. Among the rest (CV > 5%), 11, 23, and 40 features demonstrated 5%< CV ≤10%, 10%< CV ≤20%, and CV > 20%, respectively. A subset of features in each category (CV > 5%) showed strong correlation with the b-value combination variation, including 44% (7/16) features in gray level co-occurrence matrix, 62% (8/13) features in gray level dependence matrix, 64% (9/14) features in first order, 50% (8/16) features in gray level run length matrix, 57% (8/14) features in gray level size matrix, and 20% (1/5) features in neighborhood gray-tone difference matrix.

Conclusions

Variations in b-value combinations demonstrated impact on radiomics features extracted from ADC maps for cervical cancer. The radiomics features with CV <5% can be considered as robust features and are recommended to be used in multicenter radiomics studies.

The histogram analysis of apparent diffusion coefficient in differential diagnosis of parotid tumor

OBJECTIVES

Use apparent diffusion coefficient (ADC) histogram to investigate whether the parameters of ADC histogram can distinguish between benign and malignant tumors and further differentiate the tumor subgroups.

METHODS AND MATERIALS

This study retrospectively enrolls 161 patients with parotid gland tumors. Histogram parameters including mean, inhomogeneity, skewness, kurtosis and 10th, 25th, 50th, 75th, 90th percentiles are derived from ADC mono-exponential model. Mann-Whitney U test is used to compare the differences between benign and malignant groups. Kruskal-Wallis test with post-hoc Dunn-Bonferroni method is used for subgroup classification, then receiver operating characteristic curve analysis is performed in mean ADC value to obtain the appropriate cutoff values.

RESULTS

Except for kurtosis and 90th percentile, there are significant differences in all other ADC parameters between benign and malignant groups. In subgroup classification of benign tumors, there are significant differences in all ADC parameters between pleomorphic adenoma and Warthin's tumor (area under curve 0.988; sensitivity 93.8%; specificity 94.7%; all ps < 0.05). Pleomorphic adenoma has high value in mean than basal cell adenoma (area under curve 0.819; sensitivity 76.9%; specificity 76.9%; p < 0.05). Basal cell adenoma has high values in mean (area under curve 0.897; sensitivity 92.3%; specificity 78.9%; all ps < 0.05) and 10th, 25th, 50th percentiles than Warthin's tumor. In subgroup classification of malignant tumors, low-risk parotid carcinomas have higher values than hematolymphoid tumors in mean (area under curve 0.912; sensitivity 84.6%; specificity 100%, all ps < 0.05) and 10th, 25th percentiles.

CONCLUSION

ADC histogram parameters, especially mean and 10th, 25th percentiles, can potentially be an effective indicator for identifying and classifying parotid tumors.

Radiomic features of cervical cancer on T2-and diffusion-weighted MRI: Prognostic value in low-volume tumors suitable for trachelectomy

BACKGROUND

Textural features extracted from MRI potentially provide prognostic information additional to volume for influencing surgical management of cervical cancer.

PURPOSE

To identify textural features that differ between cervical tumors above and below the volume threshold of eligibility for trachelectomy and determine their value in predicting recurrence in patients with low-volume tumors.

METHODS

Of 378 patients with Stage1-2 cervical cancer imaged prospectively (3T, endovaginal coil), 125 had well-defined, histologically-confirmed squamous or adenocarcinomas with >100 voxels (>0.07 cm3) suitable for radiomic analysis. Regions-of-interest outlined the whole tumor on T2-W images and apparent diffusion coefficient (ADC) maps. Textural features based on grey-level co-occurrence matrices were compared (Mann-Whitney test with Bonferroni correction) between tumors greater (n = 46) or less (n = 79) than 4.19 cm3. Clustering eliminated correlated variables. Significantly different features were used to predict recurrence (regression modelling) in surgically-treated patients with low-volume tumors and compared with a model using clinico-pathological features.

RESULTS

Textural features (Dissimilarity, Energy, ClusterProminence, ClusterShade, InverseVariance, Autocorrelation) in 6 of 10 clusters from T2-W and ADC data differed between high-volume (mean ± SD 15.3 ± 11.7 cm3) and low-volume (mean ± SD 1.3 ± 1.2 cm3) tumors. (p < 0.02). In low-volume tumors, predicting recurrence was indicated by: Dissimilarity, Energy (ADC-radiomics, AUC = 0.864); Dissimilarity, ClusterProminence, InverseVariance (T2-W-radiomics, AUC = 0.808); Volume, Depth of Invasion, LymphoVascular Space Invasion (clinico-pathological features, AUC = 0.794). Combining ADC-radiomic (but not T2-radiomic) and clinico-pathological features improved prediction of recurrence compared to the clinico-pathological model (AUC = 0.916, p = 0.006). Findings were supported by bootstrap re-sampling (n = 1000).

CONCLUSION

Textural features from ADC maps and T2-W images differ between high- and low-volume tumors and potentially predict recurrence in low-volume tumors.

Whole lesion histogram analysis of apparent diffusion coefficients on MRI predicts disease-free survival in locally advanced squamous cell cervical cancer after radical chemo-radiotherapy

Background

The aim was to investigate the prognostic value of MR apparent diffusion coefficients (ADC) using histogram analysis (HA) in predicting disease-free survival (DFS) of cervical cancer after chemo-radiation therapy.

Methods

We retrospectively analyzed 103 women with pathologically proven squamous cell uterine cancer who received chemo-radiation therapy between 2009 and 2013. All patients were followed up for more than 2 years. Pre-treatment MR images were retrieved and imported for HA using an in-house developed software program based on 3D Slicer. Regions of interest of whole tumors were drawn manually on DWI with reference to T2WI. HA features (mean, max, min, 50, 10, 90%, kurtosis, and skewness) were extracted from apparent diffusion coefficient (ADC) maps and compared between the recurrence and non-recurrence groups after the 2-year follow-up. Univariate and multivariate Cox regression analysis was used to correlate ADC HA features and relevant clinical variables (age, grade, maximal diameter of tumor, FIGO stage, SCC-Ag) with DFS.

Results

One hundred three patients with stage IB-IV cervical cancers were followed up for 2.0–94.6 months (median 48.9 months). Twenty patients developed recurrence within 2 years. In the recurrence group, the min (P = 0.001) and 10% (P = 0.048) ADC values were significantly lower than those of the non-recurrence group. Univariate and multivariate Cox regression analysis revealed that ADC_min (P = 0.006, HR = 0.110) was significantly correlated with DFS.

Conclusion

Pre-treatment volumetric ADC_min in histogram analysis is an independent factor that is correlated with DFS in cervical cancer patients treated with chemo-radiation therapy.

Exploratory Study of Apparent Diffusion Coefficient Histogram Metrics in Assessing Pancreatic Malignancy

PURPOSE

To evaluate whole-lesion 3D-histogram apparent diffusion coefficient (ADC) metrics for assessment of pancreatic malignancy.

METHODS

Forty-two pancreatic malignancies (36 pancreatic adenocarcinoma [PDAC], 6 pancreatic neuroendocrine [PanNET]) underwent abdominal magnetic resonance imaging (MRI) with diffusion-weighted imaging before endoscopic ultrasound biopsy or surgical resection. Two radiologists independently placed 3D volumes of interest to derive whole-lesion histogram ADC metrics. Mann-Whitney tests and receiver operating characteristic analyses were used to assess metrics' diagnostic performance for lesion histology, T-stage, N-stage, and grade.

RESULTS

Whole-lesion ADC histogram metrics lower in PDACs than PanNETs for both readers (P ≤ .026) were mean ADC (area under the curve [AUC] = 0.787-0.792), mean of the bottom 10th percentile (mean_0-10) (AUC = 0.787-0.880), mean of the 10th-25th percentile (mean_10-25) (AUC = 0.884-0.917) and mean of the 25th-50th percentile (mean_25-50) (AUC = 0.829-0.829). For mean_10-25 (metric with highest AUC for identifying PDAC), for reader 1 a threshold > 0.94 × 10^-3 mm²/s achieved sensitivity 94% and specificity 83%, and for reader 2 a threshold > 0.82 achieved sensitivity 97% and specificity 67%. Metrics lower in nodal status ≥ N₁ than N₀ for both readers (P ≤ .043) were mean_0-10 (AUC = 0.789-0.822) and mean_10-25 (AUC = 0.800-0.822). For mean_10-25 (metric with highest AUC for identifying N₀), for reader 1 a threshold <1.17 achieved sensitivity 87% and specificity 67%, and for reader 2 a threshold <1.04 achieved sensitivity 87% and specificity 83%. No metric was associated with T-stage (P > .195) or grade (P > .215).

CONCLUSION

Volumetric ADC histogram metrics may serve as non-invasive biomarkers of pancreatic malignancy. Mean_10-25 outperformed standard mean for lesion histology and nodal status, supporting the role of histogram analysis.

Whole Lesion Histogram Analysis Derived From Morphological MRI Sequences Might be Able to Predict EGFR- and Her2-Expression in Cervical Cancer

RATIONALE AND OBJECTIVES

Histogram analysis is an imaging analysis in which a whole tumor can be assessed, and every voxel of a radiological image is issued into a histogram. Thereby, statistically information about tumor can be obtained. The purpose of the study was to analyze possible relationships between histogram parameters derived from conventional MRI sequences and several histopathological features in cervical squamous cell carcinomas.

METHODS

A total of 18 female patients (age range 32-79 years) with squamous cell cervical carcinoma were retrospectively enrolled into the study. In all cases, pelvic MRI with a clinically protocol was performed. Histogram analysis was performed as a whole lesion measurement, calculating several percentils, minimum, mean, median, mode, maximum, kurtosis, skewness, and entropy. Histopathological parameters included expression of epidermal-growth factor (EGFR), vascular endothelial growth factor, hypoxia-inducible factor 1-alpha, Her2, and Histone 3. Spearman's correlation coefficient was used to analyze associations between investigated parameters.

RESULTS

Several pre- and postcontrast derived T1-weighted parameters correlated inversely with EGFR expression. For precontrast T1-weighted images, the strongest correlation was found for p90 (ρ = -0.77, p = 0.004). For postcontrast T1-weighted images, the strongest correlation was observed for minimum (ρ = -0.64, p = 0.021). Several parameters derived from T2-weighted images were statistically significant different between Her2-positive and Her2 negative tumors. Skewness had the best p-value ( p = 0.004).

CONCLUSIONS

Histogram analysis parameters of T1-weighted and T2-weighted images reflect HER2 status and EGFR expression in cervical cancer. Histogram parameters cannot predict cell count, proliferation index, or angiogenesis related histopathological features.

Radiomics in Glioblastoma: Current Status and Challenges Facing Clinical Implementation

Radiomics analysis has had remarkable progress along with advances in medical imaging, most notability in central nervous system malignancies. Radiomics refers to the extraction of a large number of quantitative features that describe the intensity, texture and geometrical characteristics attributed to the tumor radiographic data. These features have been used to build predictive models for diagnosis, prognosis, and therapeutic response. Such models are being combined with clinical, biological, genetics and proteomic features to enhance reproducibility. Broadly, the four steps necessary for radiomic analysis are: (1) image acquisition, (2) segmentation or labeling, (3) feature extraction, and (4) statistical analysis. Major methodological challenges remain prior to clinical implementation. Essential steps include: adoption of an optimized standard imaging process, establishing a common criterion for performing segmentation, fully automated extraction of radiomic features without redundancy, and robust statistical modeling validated in the prospective setting. This review walks through these steps in detail, as it pertains to high grade gliomas. The impact on precision medicine will be discussed, as well as the challenges facing clinical implementation of radiomic in the current management of glioblastoma.

Repeatability analysis of ADC histogram metrics of the uterus

BACKGROUND

Recently, histogram analysis based on voxel-wise apparent diffusion coefficient (ADC) value distribution has been increasingly performed. However, few studies have been reported regarding its repeatability.

PURPOSE

To evaluate the repeatability of ADC histogram metrics of the uterus in clinical magnetic resonance imaging (MRI).

MATERIAL AND METHODS

Thirty-three female patients who underwent pelvic MRI including diffusion-weighted imaging (DWI) were prospectively included after providing informed consent. Two sequential DWI acquisitions with identical parameters and position were obtained. Regions of interest (ROIs) for histologically confirmed uterine lesions (five cervical and three endometrial cancers, and one endometrial hyperplasia) and normal appearing tissues (21 endometrium and 33 myometrium) were assigned on the first DWI dataset and then pasted onto the second DWI dataset. ADC histogram metrics within the ROIs were calculated and repeatability was evaluated by calculating within-subject coefficient of variance (%) (wCV (%)) and Bland-Altman plot (%).

RESULTS

ADC 10%, 25%, median, 75%, 90%, maximum, mean, and entropy showed high repeatability (wCV (%) < 7, 95% limit of agreement in Bland-Altman plot (%) < ±20), followed by ADC minimum (wCV (%) = 8.12, 95% limit of agreement in Bland-Altman plot (%) < ±30). However, ADC skewness and kurtosis showed very low repeatability in all evaluations.

CONCLUSION

ADC histogram metrics like ADC 10%, 25%, median, 75%, 90%, maximum, mean, and entropy are robust biomarkers and could be applicable to clinical use. However, ADC skewness and kurtosis lack robustness. Radiologists should keep these characteristics and limitations in mind when interpreting quantitative DWI.

Whole-tumour histogram analysis of pharmacokinetic parameters from dynamic contrast-enhanced MRI in resectable oesophageal squamous cell carcinoma can predict T-stage and regional lymph node metastasis

OBJECTIVE

To identify whether whole-tumour histogram analysis of pharmacokinetic parameters from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) could predict T-stage and regional lymph node metastasis (LNM) of resectable oesophageal squamous cell carcinoma (SCC).

MATERIALS AND METHODS

Forty-two consecutive patients with confirmed oesophageal SCC underwent thoracic DCE-MRI. Histogram metrics (median, mean, standard deviation [SD], skewness, kurtosis and entropy) of whole-tumour pharmacokinetic parameters including endothelial transfer constant (Ktrans), reflux rate (Kep) and fractional extravascular extracellular space volume (Ve) were generated by the Omni-Kinetics software. Histogram datasets were interpreted using the Mann-Whitney U test and receiver operating characteristic (ROC) statistical analyses.

RESULTS

The Mann-Whitney U tests revealed that the median, mean and SD of Ktrans, the SD and entropy of Kep, and the median, mean and entropy of Ve of T1-2 stage oesophageal SCC were lower when compared with T3 stage (all Ps < 0.05); and the ROC analysis showed that the entropy of Ve could reliably distinguish T1-2 stage from T3 stage with an area under ROC (AUC) of 0.773. The Mann-Whitney U tests illustrated that the entropy of Ktrans, and the median, mean, SD and entropy of Kep were higher while the skewness of Kep was lower in tumours with LNM than without LNM (all Ps < 0.05); and the ROC analysis demonstrated that the SD of Kep could best identify tumours with LNM with an AUC of 0.702.

CONCLUSION

Whole-tumour histogram analysis of pharmacokinetic parameters of oesophageal SCC on DCE-MRI could be used to predict T-stage and regional LNM.

Skewness of apparent diffusion coefficient (ADC) histogram helps predict the invasive potential of intraductal papillary neoplasms of the bile ducts (IPNBs)

OBJECTIVE

This retrospective study was to explore the value of whole lesion apparent diffusion coefficient (ADC) histogram in distinguishing invasive and noninvasive intraductal papillary neoplasms of the bile ducts (IPNBs).

METHOD AND MATERIALS

Fifty-two patients of IPNB underwent MRI at 1.5T with diffusion-weighted imaging (DWI, b = 500 s/mm²) before surgical resections. ADC histogram metrics were generated by using the software MR OncoTreat. The mean, standard deviation, median, skewness, kurtosis as well as the 10th, 25th, 75th, and 90th percentiles were compared between pathologically defined invasive (n = 35) and noninvasive (n = 17) IPNBs. Such conventional imaging characters as lesion location, bile duct wall dilation, and mural nodularity were also assessed. Multivariate regression analysis as well as receiver operating characteristics (ROC) analysis were then conducted to determine the predictive factors and to evaluate potential diagnostic performances.

RESULTS

The inter-operator reliability was good to excellent (ICC: 0.693-979). Mean median, kurtosis, and the 10th, 25th, 75th, 90th percentiles were all greater in noninvasive group than invasive ones (P: 0.00-002). Skewness was lower in noninvasive group than invasive ones (- 1.0 ± 0.6 vs. - 0.3 ± 0.6, P = 0.00). After multivariate regression, skewness (AUC = 0.822, 95%CI 0.70-0.91) and mural nodularity (accuracy = 0.808) were the only two independent factors in predicting invasive IPNBs. The diagnostic performance improved (AUC = 0.867, 95%CI 0.742-0.946) when combining skewness and mural nodularity, however, the difference did not reach statistical significance (P = 0.16).

CONCLUSION

The ADC histogram has capability of distinguishing invasive and noninvasive IPNBs, in which skewness was an independent predictive factor.

A histogram analysis of diffusion and perfusion features of cervical cancer based on intravoxel incoherent motion magnetic resonance imaging

OBJECTIVE

To evaluate the diagnostic potential based on histogram analysis of IVIM parameters between uterine cervical cancers (CC) - normal myometrium (Myo) versus CC - gluteus maximus muscle (GM) and to study the feasibility of histogram analysis of IVIM parameters to differentiate the early from locally advanced stage CCs.

METHODS

64 patients with pathologically confirmed CC were enrolled. Histogram indices mean, median, 25th, and ð 75th percentile of apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) value of entire tumor were statistically analyzed and compared between CC - GM versus CC - Myo, as well as between early and locally advanced stage CCs. A multivariate analysis was performed to identify indices that could best distinguish early from locally advanced stage CC. Receiver operating characteristic curves (ROC) were used to evaluate the diagnostic efficiency of every histogram parameter.

RESULTS

All the tested histogram indices significantly differed between the patients with CC - GM vs. CC - Myo, nonetheless, CC - GM yielded higher range area under the curve (AUC) value of 0.8-0.99 vs. 0.6-0.99. The additional significant difference was found among all the tested histogram indices of D*, mean, median, and 75th percentile of f, mean and 75th percentile of ADC, and 75th percentile of D discriminating early from locally advanced CCs. ROC curves indicated that the 75th percentile of D* value 28.17 × 10^-3 mm²/s could best differentiate early from locally advanced stage CCs, with AUC of 0.776. In the multivariate analysis, ROC indicated the 50th percentile of D* and f was the most significant with AUCs of 0.856.

CONCLUSIONS

The histogram analysis of IVIM parameters depicted that gluteus maximus served better reference tissue in comparison to myometrium. The histogram index 75th percentile of ADC, D, D*, and f may serve a diagnostic biomarker to differentiate the early from locally advanced stage CCs.

Repeatability of diffusion-weighted MRI of the prostate using whole lesion ADC values, skew and histogram analysis

PURPOSE

To investigate the repeatability of diffusion-weighted imaging parameter including ADC-derived histogram values in prostate cancer.

METHODS

10 patients with prostate cancer were prospectively recruited to a retest cohort. 3 T diffusion-weighted MRI of the prostate was acquired consecutively with patient getting off the scanner between studies. Prostatectomy-histopathology defined tumour regions-of-interest were outlined on ADC maps and diffusion-weighted metrics including histograms were calculated. The coefficient of reproducibility (CoR) and Bland-Altman plots were used to assess repeatability.

RESULTS

10th centile, 90th centile, and median ADC showed good repeatability with mean difference ranging from -0.005 to -0.025 × 103 mm2s-1, and CoR ranging from 0.271-0.294 × 103 mm2s-1 of scan 1 mean). Two measures of heterogeneity and simplified texture, IQR and mean local range, had only moderate repeatability. IQR had a mean difference of -0.032 × 103 mm2s-1 between scans with CoR 0.181 × 103 mm2s-1 (56% of scan 1 mean). Mean local range had a mean difference -0.008 × 103 mm2s-1 between scans (37% of scan 1 mean). Bland-Altman plots showed good repeatability for test and re-test analysis for median, percentile and mean range values. All ADC values had good reliability regardless of whether the tumour border was included in quantitative analysis. ADC histogram skew had poor repeatability, CoR 0.78 × 103 mm2s-1 (373% of scan 1 mean).

CONCLUSION

10th and 90th centile ADC demonstrated sufficient repeatability for clinical use. However, more advanced measures of heterogeneity such as histogram skew, IQR, or mean local range may be limited by their repeatability.

Associations between whole tumor histogram analysis parameters derived from ADC maps and expression of EGFR, VEGF, Hif 1-alpha, Her-2 and Histone 3 in uterine cervical cancer

OBJECTIVE

Diffusion weighted imaging (DWI) can be quantified by apparent diffusion coefficient (ADC) and can predict tissue microstructure. The aim of the present study was to analyze possible associations between ADC histogram based parameters with different histopathological parameters in cervical squamous cell carcinoma.

MATERIALS AND METHODS

18 female patients (age range 32-79 years) with squamous cell cervical carcinoma were retrospectively enrolled. In all cases, pelvic MRI was performed with a DWI (b-values 0 and 1000 s/mm²). Histogram analysis was performed as a whole lesion measurement. Histopathological parameters included expression of EGFR, VEGF, Hif1-alpha, Her2 and Histone 3. Spearman's correlation coefficient was used to analyze associations between investigated parameters.

RESULTS

Analyze of the investigated ADC histogram parameters showed a good interreader variability, ranging from 0.705 for entropy to 0.959 for ADCmedian. EGFR expression correlated statistically significant with several histogram parameters. The highest correlation was observed for p75 (p = -0.562, P = 0.015). There were several correlations with histone 3, the highest with p25 (p = -0.610, P = 0.007). None of the ADC related parameters correlated statistically significant with expression of VEGF, Hif1-alpha and Her2.

CONCLUSION

Histogram analysis showed a good interreader agreement. ADC histogram parameters might be able to reflect expression of EGFR and histone 3 in cervical squamous cell carcinomas, but not expression of VEGF, Hif1-alpha and Her2.

Radiomics Analysis of Multiparametric MRI Evaluates the Pathological Features of Cervical Squamous Cell Carcinoma

BACKGROUND

Robust parameters to evaluate pathological aggressiveness are needed to provide individualized therapy for cervical cancer patients.

PURPOSE

To investigate the radiomics analysis of multiparametric MRI to evaluate tumor grade, lymphovascular space invasion (LVSI), and lymph node (LN) metastasis of cervical squamous cell carcinoma (CSCC).

STUDY TYPE

Retrospective.

SUBJECTS

Fifty-six patients with histopathologically confirmed CSCC.

FIELD STRENGTH/SEQUENCE

3T, axial T₂ and T₂ with fat suppression (FS), diffusion-weighted imaging (DWI) (multi-b values), axial dynamic contrast enhanced (DCE) MRI (8 sec temporal resolution).

ASSESSMENT

Regions of interest were drawn around the tumor on each axial slice and fused to generate the whole tumor volume. Sixty-six radiomics features were derived from each image sequence, including axial T₂ and T₂ FS, ADC maps, and K_trans , V_e , and V_p maps from DCE MRI.

STATISTICAL TESTS

A univariate analysis was performed to assess each parameter's association with tumor grade and the presence of lymphovascular space invasion (LVSI) and lymph node (LN) metastasis. A principal component analysis was employed for dimension reduction and to generate new discriminative valuables. Using logistic regression, a discriminative model of each parameter was built and a receiver operating characteristic curve (ROC) was generated.

RESULTS

The area under the ROC curve (AUC) of anatomical, diffusion, and permeability parameters in discriminating the presence of LVSI ranged from 0.659 to 0.814, with V_e showing the best discriminative value. The AUC in discriminating the presence of LN metastasis and distinguishing tumor grade ranged from 0.747 to 0.850, 0.668 to 0.757, with ADC and V_e showing the best discriminative value, respectively.

DATA CONCLUSION

Functional maps exhibit better discriminative values than anatomical images for discriminating the pathological features of CSCC, with ADC maps showing the best discrimination performance for LN metastasis and V_e maps showing the best discriminative value for LVSI and tumor grade.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1141-1148.

Use of Apparent Diffusion Coefficient Histogram in Differentiating Between Medulloblastoma and Pilocytic Astrocytoma in Children

Background

This research aimed to investigate the value of apparent diffusion coefficient (ADC) histogram in differentiating between medulloblastoma and pilocytic astrocytoma in children.

Material/Methods

Thirty-three children with posterior cranial fossa tumor confirmed by operation and pathology participated in this retrospective study, including 18 children with medulloblastoma and 15 children with pilocytic astrocytoma. ADC images of the maximum lay of tumors were selected, and the region of interest was delineated by Mazda software and analyzed by histogram. Histogram characteristic parameters of the 2 tumors were statistically analyzed to determine the significantly different characteristic parameters between the 2 tumor types.

Results

There were significant differences in the mean value, variance, skewness, kurtosis, and 1^th, 10^th, 50^th, and 90^th percentiles of 9 characteristic parameters extracted by histogram (P<0.05). The corresponding receiver operating characteristic (ROC) curves were drawn, in which the mean value and 50^th percentile were best identified. When the maximum area under the ROC curve was 1 and the optimal threshold was 137.7 and 125.5, the specificity and sensitivity were both 100%.

Conclusions

ADC histograms can be used to differentiate between medulloblastoma and pilocytic astrocytoma in children and provide reliable and objective evidence for the differentiation.

Prognostic model based on magnetic resonance imaging, whole-tumour apparent diffusion coefficient values and HPV genotyping for stage IB-IV cervical cancer patients following chemoradiotherapy

OBJECTIVES

To develop and validate a prognostic model of integrating whole-tumour apparent diffusion coefficient (ADC) from pretreatment diffusion-weighted (DW) magnetic resonance (MR) imaging with human papillomavirus (HPV) genotyping in predicting the overall survival (OS) and disease-free survival (DFS) for women with stage IB-IV cervical cancer following concurrent chemoradiotherapy (CCRT).

METHODS

We retrospectively analysed three prospectively collected cohorts comprising 300 patients with stage IB-IV cervical cancer treated with CCRT in 2007-2014 and filtered 134 female patients who underwent MR imaging at 3.0 T for final analysis (age, 24-92 years; median, 54 years). Univariate and multivariate Cox regression analyses were used to evaluate the whole-tumour ADC histogram parameters, HPV genotyping and relevant clinical variables in predicting OS and DFS. The dataset was randomly split into training (n = 88) and testing (n = 46) datasets for construction and independent bootstrap validation of the models.

RESULTS

The median follow-up time for surviving patients was 69 months (range, 9-126 months). Non-squamous cell type, ADC₁₀ <0.77 × 10^-3 mm²/s, T3-4, M1 stage and high-risk HPV status were selected to generate a model, in which the OS and DFS for the low, intermediate and high-risk groups were significantly stratified (p < 0.0001). The prognostic model improved the prediction significantly compared with the International Federation of Gynaecology and Obstetrics (FIGO) stage for both the training and independent testing datasets (p < 0.0001).

CONCLUSIONS

The prognostic model based on integrated clinical and imaging data could be a useful clinical biomarker to predict OS and DFS in patients with stage IB-IV cervical cancer treated with CCRT.

KEY POINTS

• ADC ₁₀ is the best prognostic factor among ADC parameters in cervical cancer treated with CCRT • A novel prognostic model was built based on histology, ADC ₁₀ , T and M stage and HPV status • The prognostic model outperforms FIGO stage in the survival prediction.

Spatial relationship of 2-deoxy-2-[18F]-fluoro-D-glucose positron emission tomography and magnetic resonance diffusion imaging metrics in cervical cancer

Background

This study investigated the spatial relationship of 2-deoxy-2-[¹⁸F]-fluoro-D-glucose positron emission tomography ([¹⁸F]FDG-PET) standardized uptake values (SUVs) and apparent diffusion coefficients (ADCs) derived from magnetic resonance (MR) diffusion imaging on a voxel level using simultaneously acquired PET/MR data.

We performed an institutional retrospective analysis of patients with newly diagnosed cervical cancer who received a pre-treatment simultaneously acquired [¹⁸F]FDG-PET/MR. Voxel SUV and ADC values, and global tumor metrics including maximum SUV (SUV_max), mean ADC (ADC_mean), and mean tumor-to-muscle ADC ratio (ADC_T/M) were compared. The impacts of histology, grade, and tumor volume on the voxel SUV to ADC relationship were also evaluated. The potential prognostic value of the voxel SUV/ADC relationship was evaluated in an exploratory analysis using Kaplan-Meier/log-rank and univariate Cox analysis.

Results

Seventeen patients with PET/MR scans were identified. There was a significant inverse correlation between SUV_max and ADC_mean, and SUV_max and ADC_T/M. In the voxelwise analysis, squamous cell carcinomas (SCCAs) and poorly differentiated tumors showed a consistent significant inverse correlation between voxel SUV and ADC values; adenocarcinomas (AdenoCAs) and well/moderately differentiated tumors did not. The strength of the voxel SUV/ADC correlation varied with metabolic tumor volume (MTV). On log-rank analysis, the correlation between voxel SUV/ADC values was prognostic of disease-free survival (DFS).

Conclusions

In this hypothesis-generating study, a consistent inverse correlation between voxel SUV and ADC values was seen in SCCAs and poorly differentiated tumors. On univariate statistical analysis, correlation between voxel SUV and ADC values was prognostic for DFS.

Electronic supplementary material

The online version of this article (10.1186/s13550-018-0403-7) contains supplementary material, which is available to authorized users.