Histogram analysis of apparent diffusion coefficient for the assessment of local aggressiveness of cervical cancer

Improved effects of the b-value for 2000 sec/mm2 DWI on an accurate qualitative and quantitative assessment of rectal cancer

BACKGROUND AND STUDY OBJECTIVES

A higher b-value Diffusion-weighted imaging (DWI) would improve the contrast between cancerous and noncancerous tissue. Apparent diffusion coefficient (ADC)-histogram analysis is a method that can provide statistical data and quantitative information on tumor heterogeneity. This study aimed to compare two high b-values (1000 and 2000 sec/mm2) DWI in tumor detection and diagnostic performance in identifying early-stage tumor rectal cancer.

PATIENTS AND METHODS

This blinded and blinded retrospective study involved 56 patients with rectal cancer and 45 patients. Two radiologists evaluated the qualitative detection parameters and quantitative parameters of the ADC evaluated histogram and compared them between two DWI sequences (b-value for 1000 sec/mm2 and 2000 sec/mm2). The characteristic curves were used to assess diagnostic administration for the ADC histogram in discriminating early-stage tumors.

RESULTS

The b-value for 2000 sec/mm2 DWI significantly improved AUCs, sensitivity, specificity, and precision and decreased false-positive rate for detection compared to the b-value for 1000 sec/mm2 (p < 0.05). The mean and fifth percentile ADC value for stage I using the b-value for 1000 sec/mm2 DWI was significantly higher than stage ≥ II (p = 0.036II and 0.016 respectively), as the well as fifth, 10th, mean ADC of the fifth, 10th, and 25th ADC percentile at b-value for 2000 sec/mm2 (p = 0.031, 0.014, 0.035 and 0.025 respectively). The AUCs of the fifth percentile ADC at b-value for 2000 sec/mm2 DWI in both readers in differentiating the stage Ⅰ tumor were the highest (0.732 and 0.751).

CONCLUSION

The b-value for 2000 sec/mm2 DWI could improve the accurate detection of rectal cancer. The fifth percentile ADC at b-value for 2000 sec/mm2 sec/mm2 DWI was more useful for discriminating early stage than the b-value for 1000 sec/mm2 DWI.

Whole-tumor histogram analysis of apparent diffusion coefficient maps with machine learning algorithms for predicting histologic grade of sinonasal squamous cell carcinoma: a preliminary study

PURPOSE

Accurate histologic grade assessment is helpful for clinical decision making and prognostic assessment of sinonasal squamous cell carcinoma (SNSCC). This research aimed to explore whether whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps with machine learning algorithms can predict histologic grade of SNSCC.

METHODS

One hundred and forty-seven patients with pathologically diagnosed SNSCC formed this retrospective study. Sixty-six patients were low-grade (grade I/II) and eighty-one patients were high-grade (grade III). Eighteen histogram features were obtained from quantitative ADC maps. Additionally, the mean ADC value and clinical features were analyzed for comparison with histogram features. Machine learning algorithms were applied to build the best diagnostic model for predicting histological grade. The receiver operating characteristic (ROC) curve was used to evaluate the performance of each model prediction, and the area under the ROC curve (AUC) were analyzed.

RESULTS

The histogram model based on three features (10th Percentile, Mean, and 90th Percentile) with support vector machine (SVM) classifier demonstrated excellent diagnostic performance, with an AUC of 0.947 on the testing dataset. The AUC of the histogram model was similar to that of the mean ADC value model (0.947 vs 0.957; P = 0.7029). The poor diagnostic performance of the clinical model (AUC = 0.692) was improved by the combined model incorporating histogram features or mean ADC value (P < 0.05).

CONCLUSION

ADC histogram analysis improved the projection of SNSCC histologic grade, compared with clinical model. The complex histogram model had comparable but not better performance than mean ADC value model.

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.

A feasibility study of reduced full-of-view synthetic high-b-value diffusion-weighted imaging in uterine tumors

OBJECTIVES

This study aimed to evaluate the feasibility of reduced full-of-view synthetic high-b value diffusion-weighted images (rFOV-syDWIs) in the clinical application of cervical cancer based on image quality and diagnostic efficacy.

METHODS

We retrospectively evaluated the data of 35 patients with cervical cancer and 35 healthy volunteers from May to November 2021. All patients and volunteers underwent rFOV-DWI scans, including a 13b-protocol: b = 0, 25, 50, 75, 100, 150, 200, 400, 600, 800, 1000, 1200, and 1500 s/mm² and a 5b-protocol: b = 0, 100, 400, 800,1500 s/mm². rFOV-syDWIs with b values of 1200 (rFOV-syDWI_b=1200) and 1500 (rFOV-syDWI_b=1500) were generated from two different multiple-b-value image datasets using a mono-exponential fitting algorithm. According to homoscedasticity and normality assessed by the Levene's test and Shapiro-Wilk test, the inter-modality differences of quantitative measurements were, respectively, examined by Wilcoxon signed-rank test or paired t test and the inter-group differences of ADC values were examined by independent t test or Mann-Whitney U test.

RESULTS

A higher inter-reader agreement between SNRs and CNRs was found in 13b-protocol and 5b-protocol rFOV-syDWI_b=1200/1500 compared to 13b-protocol rFOV-sDWI_b=1200/1500 (p < 0.05). AUC of 5b-protocol syADC_{mean,b=1200/1500} and syADC_{minimum,b=1200/1500} was equal or higher than that of 13b-protocol sADC_{mean,b=1200/1500} and sADC_{minimum,b=1200/1500}.

CONCLUSIONS

rFOV-syDWIs provide better lesion clarity and higher image quality than rFOV-sDWIs. 5b-protocol rFOV-syDWIs shorten scan time, and synthetic ADCs offer reliable diagnosis value as scanned 13b-protocol DWIs.

Prognosis prediction of uterine cervical cancer using changes in the histogram and texture features of apparent diffusion coefficient during definitive chemoradiotherapy

OBJECTIVES

We investigated prospectively whether, in cervical cancer (CC) treated with concurrent chemoradiotherapy (CCRT), the Apparent diffusion coefficient (ADC) histogram and texture parameters and their change rates during treatment could predict prognosis.

METHODS

Fifty-seven CC patients treated with CCRT at our institution were included. They underwent MRI scans up to four times during the treatment course (1st, before treatment [n = 41], 2nd, at the start of image-guided brachytherapy (IGBT) [n = 41], 3rd, in the middle of IGBT [n = 27], 4th, after treatment [n = 53]). The entire tumor was manually set as the volume of interest (VOI) manually in the axial images of the ADC map by two radiologists. A total of 107 image features (morphology features 14, histogram features 18, texture features 75) were extracted from the VOI. The recurrence prediction values of the features and their change rates were evaluated by Receiver operating characteristics (ROC) analysis. The presence or absence of local and distant recurrence within two years was set as an outcome. The intraclass correlation coefficient (ICC) was also calculated.

RESULTS

The change rates in kurtosis between the 1st and 3rd, and 1st and 2nd MRIs, and the change rate in grey level co-occurrence matrix_cluster shade between the 2nd and 3rd MRIs showed particularly high predictive powers (area under the ROC curve = 0.785, 0.759, and 0.750, respectively), which exceeded the predictive abilities of the parameters obtained from pre- or post-treatment MRI only. The change rate in kurtosis between the 1st and 2nd MRIs had good reliability (ICC = 0.765).

CONCLUSIONS

The change rate in ADC kurtosis between the 1st and 2nd MRIs was the most reliable parameter, enabling us to predict prognosis early in the treatment course.

Can the combination of DWI and T2WI radiomics improve the diagnostic efficiency of cervical squamous cell carcinoma?

BACKGROUND

To investigate the value of MRI multi-sequence imaging model in differentiation of cervical squamous cell carcinoma (CSCC).

METHODS

A total of 104 CSCC patients confirmed with pathology were retrospectively enrolled. All patients underwent conventional MRI examination before treatment. The lesions were segmented using ITK-SNAP software manually and radiomics features were extracted by Artificial Intelligence Kit (AK) software. 396 tumor texture features were obtained and then the mRMR and Lasso algorithms were used to reduce the feature dimension. Three models including T2WI model, DWI model and Joint model (combined TWI and DWI) were constructed in training group and evaluated in validation group. and the receiver operator characteristics and calibration curve were used to evaluate the model performance.

RESULTS

The Joint model and T2WI model both showed a better diagnostic efficacy than single DWI model in differentiation of CSCC in training group (Joint model: AUC = 0.841; T2WI model: AUC = 0.804; DWI model: AUC = 0.732) and validation group (Joint model: AUC = 0.822; T2WI model: AUC = 0.791; DWI model: AUC = 0.724). But there was no statistical difference between Joint model and T2WI model by Delong test(P > 0.05).

CONCLUSIONS

The study suggested that the conventional T2WI sequence may be more suitable for prognosis evaluation of CSCC, which can provide a potential tool to facilitate the differential diagnosis of low-differentiation and high-differentiation CSCC.

MRI-based Whole-Tumor Radiomics to Classify the Types of Pediatric Posterior Fossa Brain Tumor

BACKGROUND

Differential diagnosis between medulloblastoma (MB), ependymoma (EP) and astrocytoma (PA) is important due to differing medical treatment strategies and predicted survival. The aim of this study was to investigate non-invasive MRI-based radiomic analysis of whole tumors to classify the histologic tumor types of pediatric posterior fossa brain tumor and improve the accuracy of discrimination, using a random forest classifier.

METHODS

MRI images of 99 patients, with 59 MBs, 13 EPs and 27 PAs histologically confirmed by surgery and pathology before treatment, were included in this retrospective study. Registration was performed between the three sequences, and high- throughput features were extracted from manually segmented tumors on MR images of each case. The forest-based feature selection method was adopted to select the top ten significant features. Finally, the results were compared and analyzed according to the classification.

RESULTS

The top ten contributions according to the classifier of wavelet features all came from the ADC sequence. The random forest classifier achieved 100% accuracy on the training data and validated the best accuracy (0.938): sensitivity = 1.000, 0.948 and 0.808, specificity = 0.952, 0.926 and 1.000 for EP, MB and PA, respectively.

CONCLUSION

A random forest classifier based on the ADC sequence of the whole tumor provides more quantitative information than TIWI and T2WI in differentiating pediatric posterior fossa brain tumors. In particular, the histogram percentile value showed great superiority, which added diagnostic value in pediatric neuro-oncology.

The Utility of ADC First-Order Histogram Features for the Prediction of Metachronous Metastases in Rectal Cancer: A Preliminary Study

Simple Summary

Metachronous metastases are the main factors affecting survival in rectal cancer, and 15–25% of patients will develop them at a 5-year follow-up. Early identification of patients with higher risk of developing distant metachronous metastases would help to improve therapeutic protocols and could allow for a more accurate, personalized management. Apparent diffusion coefficient (ADC) represents an MRI quantitative biomarker, which can assess the diffusion characteristics of tissues, depending on the microscopic mobility of water, showing information related to tissue cellularity. First-order histogram-based features statistics describe the frequency distribution of intensity values within a region of interest, revealing microstructural alterations. In our study, we demonstrated that whole-tumor ADC first-order features may provide useful information for the assessment of rectal cancer prognosis, regarding the occurrence of metachronous metastases.

Abstract

This study aims the ability of first-order histogram-based features, derived from ADC maps, to predict the occurrence of metachronous metastases (MM) in rectal cancer. A total of 52 patients with pathologically confirmed rectal adenocarcinoma were retrospectively enrolled and divided into two groups: patients who developed metachronous metastases (n = 15) and patients without metachronous metastases (n = 37). We extracted 17 first-order (FO) histogram-based features from the pretreatment ADC maps. Student’s t-test and Mann–Whitney U test were used for the association between each FO feature and presence of MM. Statistically significant features were combined into a model, using the binary regression logistic method. The receiver operating curve analysis was used to determine the diagnostic performance of the individual parameters and combined model. There were significant differences in ADC 90th percentile, interquartile range, entropy, uniformity, variance, mean absolute deviation, and robust mean absolute deviation in patients with MM, as compared to those without MM (p values between 0.002–0.01). The best diagnostic was achieved by the 90th percentile and uniformity, yielding an AUC of 0.74 [95% CI: 0.60–0.8]). The combined model reached an AUC of 0.8 [95% CI: 0.66–0.90]. Our observations point out that ADC first-order features may be useful for predicting metachronous metastases in rectal cancer.

Magnetic resonance imaging-based texture analysis for the prediction of postoperative clinical outcome in uterine cervical cancer

OBJECTIVES

Magnetic resonance imaging (MRI)-based texture analysis (MRTA) is a novel image analysis tool that offers objective information about the spatial arrangement of MRI signal intensity. We aimed to investigate the value of MRTA in predicting the postoperative clinical outcome of patients with uterine cervical cancer.

METHODS

This retrospective study included 115 patients with surgically proven cervical cancer who underwent preoperative pelvic 3T-MRI, and MRTA was performed on T2-weighted images (T2), apparent diffusion coefficient (ADC) maps, and contrast-enhanced T1-weighted images (CE-T1). Filtration histogram-based texture analysis was used to generate six first-order statistical parameters [mean intensity, standard deviation (SD), mean of positive pixels (MPP), entropy, skewness, and kurtosis] at five spatial scaling factors (SSFs, 2-6 mm) as well as from unfiltered images. Cox proportional hazard models and time-dependent receiver operating characteristic analyses were used to evaluate the associations between parameters and recurrence-free survival (RFS).

RESULTS

During a median follow-up of 36 months, tumor recurrence was found in 26 patients (22.6%). Multivariate analysis demonstrated that CE-T1 MPP and T2 kurtosis at SSF3-5, CE-T1 MPP at SSF6, and CE-T1 SD at unfiltered images were independent predictors of RFS (p < 0.05). Regarding the 2-year RFS for CE-T1 MPP and T2 kurtosis at SSF5, and CE-T1 MPP at SSF6, patients with > optimal cutoff values demonstrated significantly worse survival than those with ≤ optimal cutoff values (p < 0.05).

CONCLUSION

Preoperative MRTA may be useful for predicting postoperative outcome in patients with cervical cancer.

Imaging to assist fertility-sparing surgery

Cytological screening and human papilloma virus testing has led to diagnosis of cervical cancer in young women at an earlier stage. Defining the full extent of the disease within the cervix with imaging aids the decision on feasibility of fertility-sparing surgical options, such as extended cone biopsy or trachelectomy. High spatial resolution images with maximal contrast between tumour and surrounding background are achieved with T2-weighted and diffusion-weighted (DW) magnetic resonance imaging (MRI) obtained using an endovaginal receiver coil. Tumour size and volume demonstrated in this way correlates between observers and with histology and differences between MRI and histology estimates of normal endocervical canal length are not significant. For planning fertility-sparing surgery, this imaging technique facilitates the best oncological outcome while minimising subsequent obstetric risks. Parametrial invasion may be assessed on large field of view T2-weighted MRI. The fat content of the parametrium limits the utility of DW imaging in this context, because fat typically shows diffusion restriction. The use of contrast-enhanced MRI for assessing the parametrium does not provide additional benefits to the T2-weighted images and the need for an extrinsic contrast agent merely adds additional complexity and cost. For nodal assessment, 18fluorodeoxyglucose positron emission tomography-computerised tomography (18FDG PET-CT) remains the gold standard.

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.

Whole-Lesion Apparent Diffusion Coefficient Histogram Analysis: Significance for Discriminating Lung Cancer from Pulmonary Abscess and Mycobacterial Infection

Simple Summary

Diffusion-weighted magnetic resonance imaging (DWI) can differentiate malignant from benign pulmonary nodules and masses. However, it is difficult to differentiate pulmonary abscesses and mycobacterium infections (PAMIs) from lung cancers because PAMIs show restricted diffusion in DWI. The purpose of this study was to establish the role of ADC histogram for differentiating lung cancer from PAMI. There were 41 lung cancers and 19 PAMIs. Parameters more than 60% of AUC were ADC, maximal ADC, mean ADC, median ADC, most frequency ADC, kurtosis of ADC, and volume of lesion. There were significant differences between lung cancer and PAMI in ADC, mean ADC, median ADC, and most frequency ADC. ADC histogram has the potential to be a valuable tool to differentiate PAMI from lung cancer.

Abstract

Diffusion-weighted magnetic resonance imaging (DWI) can differentiate malignant from benign pulmonary nodules. However, it is difficult to differentiate pulmonary abscesses and mycobacterial infections (PAMIs) from lung cancers because PAMIs show restricted diffusion in DWI. The study purpose is to establish the role of ADC histogram for differentiating lung cancer from PAMI. There were 41 lung cancers (25 adenocarcinomas, 16 squamous cell carcinomas), and 19 PAMIs (9 pulmonary abscesses, 10 mycobacterial infections). Parameters more than 60% of the area under the ROC curve (AUC) were ADC, maximal ADC, mean ADC, median ADC, most frequency ADC, kurtosis of ADC, and volume of lesion. There were significant differences between lung cancer and PAMI in ADC, mean ADC, median ADC, and most frequency ADC. The ADC (1.19 ± 0.29 × 10⁻³ mm²/s) of lung cancer obtained from a single slice was significantly lower than that (1.44 ± 0.54) of PAMI (p = 0.0262). In contrast, mean, median, or most frequency ADC of lung cancer which was obtained in the ADC histogram was significantly higher than the value of each parameter of PAMI. ADC histogram could discriminate PAMIs from lung cancers by showing that AUCs of several parameters were more than 60%, and that several parameters of ADC of PAMI were significantly lower than those of lung cancer. ADC histogram has the potential to be a valuable tool to differentiate PAMI from lung cancer.

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.

An analysis of Ki-67 expression in stage 1 invasive ductal breast carcinoma using apparent diffusion coefficient histograms

Background

To investigate the value of apparent diffusion coefficient (ADC) histograms in differentiating Ki-67 expression in T1 stage invasive ductal breast carcinoma (IDC).

Methods

The records of 111 patients with pathologically confirmed T1 stage IDC who underwent magnetic resonance imaging prior to surgery were retrospectively reviewed. The expression of Ki-67 in tumor tissue samples from the patients was assessed using immunohistochemical (IHC) staining, with a cut-off value of 25% for high Ki-67 labeling index (LI). ADC images of the maximum lay of tumors were selected, and the region of interest (ROI) of each lay was delineated using the MaZda software and analyzed by histogram. The correlations between the histogram characteristic parameters and the Ki-67 LI were investigated. Additionally, the histogram characteristic parameters of the high Ki-67 group (n=54) and the low Ki-67 group (n=57) were statistically analyzed to determine the characteristic parameters with significant difference. Receiver operator characteristic (ROC) analyses were further performed for the significant parameters.

Results

The mean value, and the 1st, 10th, 50th, 90th, and 99th percentiles were found to be negatively correlated with the expression of Ki-67 (all P values <0.001), with a correlation coefficient of -0.624, -0.749, -0.717, -0.621, -0.500, and -0.410, respectively. In the high Ki-67 group, the mean value, and the 1st, 10th, 50th, 90th, and 99th percentiles extracted by the histogram were significantly lower (all P values <0.05) than that of the low Ki-67 group, with areas under the ROC curves ranging from 0.717-0.856. However, the variance, skewness, and kurtosis did not differ between the two groups (all P values >0.05).

Conclusions

Histogram-derived parameters for ADC images can serve as a reliable tool in the prediction of Ki-67 proliferation status in patients with T1 stage IDC. Among the significant ADC histogram values, the 1st and 10th percentiles showed the best predictive values.

Combination Assessment of Diffusion-Weighted Imaging and T2-Weighted Imaging Is Acceptable for the Differential Diagnosis of Lung Cancer from Benign Pulmonary Nodules and Masses

Simple Summary

The purpose of this study is to determine whether the combination assessment of DWI and T2WI improves the diagnostic ability for differential diagnosis of lung cancer from benign pulmonary nodules and masses (BPNMs). As using the OCV (1.470 × 10⁻³ mm²/s) for ADC, the sensitivity was 83.9% (220/262), the specificity 63.4% (33/52), and the accuracy 80.6% (253/314). As using the OCV (2.45) for T2 CR, the sensitivity was 89.7% (235/262), the specificity 61.5% (32/52), and the accuracy 85.0% (267/314). In 212 PNMs which were judged to be malignant by both DWI and T2WI, 203 PNMs (95.8%) were lung cancers. In 33 PNMs which were judged to be benign by both DWI and T2WI, 23 PNMs (69.7%) were BPNMs. The combined assessment of DWI and T2WI could judge PNMs more precisely and would be acceptable for differential diagnosis of PNMs.

Abstract

The purpose of this study is to determine whether the combination assessment of DWI and T2-weighted imaging (T2WI) improves the diagnostic ability for differential diagnosis of lung cancer from benign pulmonary nodules and masses (BPNMs). The optimal cut-off value (OCV) for differential diagnosis was set at 1.470 × 10⁻³ mm²/s for apparent diffusion coefficient (ADC), and at 2.45 for T2 contrast ratio (T2 CR). The ADC (1.24 ± 0.29 × 10⁻³ mm²/s) of lung cancer was significantly lower than that (1.69 ± 0.58 × 10⁻³ mm²/s) of BPNM. The T2 CR (2.01 ± 0.52) of lung cancer was significantly lower than that (2.74 ± 1.02) of BPNM. As using the OCV for ADC, the sensitivity was 83.9% (220/262), the specificity 63.4% (33/52), and the accuracy 80.6% (253/314). As using the OCV for T2 CR, the sensitivity was 89.7% (235/262), the specificity 61.5% (32/52), and the accuracy 85.0% (267/314). In 212 PNMs which were judged to be malignant by both DWI and T2WI, 203 PNMs (95.8%) were lung cancers. In 33 PNMs which were judged to be benign by both DWI and T2WI, 23 PNMs (69.7%) were BPNMs. The combined assessment of DWI and T2WI could judge PNMs more precisely and would be acceptable for differential diagnosis of PNMs.

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.

Whole-tumor texture model based on diffusion kurtosis imaging for assessing cervical cancer: a preliminary study

OBJECTIVES

To evaluate the diagnostic potential of diffusion kurtosis imaging (DKI) functional maps with whole-tumor texture analysis in differentiating cervical cancer (CC) subtype and grade.

METHODS

Seventy-six patients with CC were enrolled. First-order texture features of the whole tumor were extracted from DKI and DWI functional maps, including apparent kurtosis coefficient averaged over all directions (MK), kurtosis along the axial direction (Ka), kurtosis along the radial direction (Kr), mean diffusivity (MD), fractional anisotropy (FA), and ADC maps, respectively. The Mann-Whitney U test and ROC curve were used to select the most representative texture features. Models based on each individual and combined functional maps were established using multivariate logistic regression analysis. Conventional parameters-the average values of ADC and DKI parameters derived from the conventional ROI method-were also evaluated.

RESULTS

The combined model based on Ka, Kr, MD, and FA maps yielded the best diagnostic performance in discrimination of cervical squamous cell cancer (SCC) and cervical adenocarcinoma (CAC) with the highest AUC (0.932). Among individual functional map derived models, Kr map-derived model showed the best performance when differentiating tumor subtypes (AUC = 0.828). MK_90th percentile was useful for distinguishing high-grade and low-grade in SCC tumors with an AUC of 0.701. The average values of MD, FA, and ADC were significantly different between SCC and CAC, but no conventional parameters were useful for tumor grading.

CONCLUSIONS

The whole-tumor texture analysis applied to DKI functional maps can be used for differential diagnosis of cervical cancer subtypes and grading SCC.

KEY POINTS

• The whole-tumor texture analysis applied to DKI functional maps allows accurate differential diagnosis of CC subtype and grade. • The combined model derived from multiple functional maps performs significantly better than the single models when differentiating tumor subtypes. • MK_90th percentile was useful for distinguishing poorly and well-/moderately differentiated SCC tumors with an AUC of 0.701.

Multi-Slice Radiomic Analysis of Apparent Diffusion Coefficient Metrics Improves Evaluation of Brain Alterations in Neonates With Congenital Heart Diseases

Apparent diffusion coefficients (ADC) can provide phenotypic information of brain lesions, which can aid the diagnosis of brain alterations in neonates with congenital heart diseases (CHDs). However, the corresponding clinical significance of quantitative descriptors of brain tissue remains to be elucidated. By using ADC metrics and texture features, this study aimed to investigate the diagnostic value of single-slice and multi-slice measurements for assessing brain alterations in neonates with CHDs. ADC images were acquired from 60 neonates with echocardiographically confirmed non-cyanotic CHDs and 22 healthy controls (HCs) treated at Children's Hospital of Nanjing Medical University from 2012 to 2016. ADC metrics and texture features for both single and multiple slices of the whole brain were extracted and analyzed to the gestational age. The diagnostic performance of ADC metrics for CHDs was evaluated by using analysis of covariance and receiver operating characteristic. For both the CHD and HC groups, ADC metrics were inversely correlated with the gestational age in single and multi-slice measurements (P < 0.05). Histogram metrics were significant for identifying CHDs (P < 0.05), while textural features were insignificant. Multi-slice ADC (P < 0.01) exhibited greater diagnostic performance for CHDs than single-slice ADC (P < 0.05). These findings indicate that radiomic analysis based on ADC metrics can objectively provide more quantitative information regarding brain development in neonates with CHDs. ADC metrics for the whole brain may be more clinically significant in identifying atypical brain development in these patients. Of note, these results suggest that multi-slice ADC can achieve better diagnostic performance for CHD than single-slice.

The Role of Multiparametric Magnetic Resonance Imaging in the Study of Primary Tumor and Pelvic Lymph Node Metastasis in Stage IB1-IIA1 Cervical Cancer

OBJECTIVE

The aim of this study was to investigate the value of multiparametric magnetic resonance imaging (MRI) in demonstrating the metastatic potential of primary tumor and differentiating metastatic lymph nodes (MLNs) from nonmetastatic lymph nodes (non-MLNs) in stage IB1-IIA1 cervical cancer.

METHODS

Fifty-seven stage IB1-IIA1 subjects were included. The apparent diffusion coefficient (ADC) values and dynamic contrast-enhanced MRI (DCE-MRI) parameters of primary tumors and lymph nodes and the conventional imaging features of the lymph nodes were measured and analyzed. Mann-Whitney test and χ test were used to analyze statistically significant parameters, logistic regression was used for multivariate analysis, and receiver operating characteristic analysis was used to compare the diagnostic performance of the MLNs.

RESULTS

Nineteen subjects had lymph node metastasis. A total of 94 lymph nodes were evaluated, including 30 MLNs and 64 non-MLNs. There were no significant difference in ADC and DCE-MRI parameters between metastatic and nonmetastatic primary tumors. The heterogeneous signal was more commonly seen in MLNs than in non-MLNs (P = 0.001). The values of ADCmean, ADCmin, and ADCmax of MLNs were lower than those of non-MLNs (P < 0.001). The values of short-axis diameter, K, Kep, and Ve of MLNs were higher than those of non-MLNs (P < 0.05). Compared with individual MRI parameters, the combined evaluation of short-axis diameter, ADCmean, and K showed the highest area under the curve of 0.930.

CONCLUSIONS

Diffusion-weighted imaging and DCE-MRI could not demonstrate the metastatic potential of primary tumor in stage IB1-IIA1 cervical cancer. Compared with individual MRI parameters, the combination of multiparametric MRI could improve the diagnostic performance of lymph node metastasis.

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.

Histological grades of rectal cancer: whole-volume histogram analysis of apparent diffusion coefficient based on reduced field-of-view diffusion-weighted imaging

Background

To explore the role of whole-lesion histogram analysis of apparent diffusion coefficient (ADC) derived from reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) technique in discriminating histological grades of rectal carcinoma.

Methods

Altogether, 49 patients with rectal cancer were enrolled in this retrospective study. All patients received preoperative 3.0 T MR scan. Histogram parameters from rFOV DWI were calculated and correlated with histological differentiation of rectal cancer. The parameters were compared between different histological grades of rectal cancer by independent Student's t-test or Man-Whitney U-test. The Spearman correlation test analyzed correlations between histological grade and histogram parameters. The diagnostic performance of individual parameters for distinguishing poorly from well-/moderately differentiated tumors was assessed by receiver operating characteristic curve (ROC) analysis.

Results

There were significant differences for ADCmean, 25^th, 50^th, 75^th, 90^th, 95^th percentiles, skewness, and kurtosis of rFOV DWI sequence between well-, moderately, and poorly differentiated rectal cancers (P<0.05). Significant correlations were noted between histological grades and the above histogram parameters (r=0.679, 0.540, 0.701, 0.730, 0.669, 0.574, -0.730, and -0.760 respectively, P<0.001). Among the individual histogram parameter, kurtosis achieved the highest AUC of 0.882 with an optimal cutoff value of 1.934 in distinguishing poorly from well-/moderately differentiated rectal cancers. The combination of ADCmean, 75^th percentile, and kurtosis yielded the highest AUC of 0.927 with a sensitivity of 88.00% and a sensitivity of 91.7% using logistic regression.

Conclusions

Quantitative whole-lesion ADC histogram analysis based on the rFOV DWI technique could help differentiate histological grades of rectal cancer. The combination of ADCmean, 75^th percentile, and kurtosis may be the best choice.

Rectal Cancer Invasiveness: Whole-Lesion Diffusion-Weighted Imaging (DWI) Histogram Analysis by Comparison of Reduced Field-of-View and Conventional DWI Techniques

To explore the role of whole-lesion histogram analysis of apparent diffusion coefficient (ADC) for discriminating between T stages of rectal carcinoma by comparison of reduced field-of-view (FOV) and conventional DWI techniques. 102 patients with rectal cancer were enrolled in this retrospective study. All patients received preoperative MR scan at 3 T, including reduced and full FOV DWI sequences. Histogram parameters from two DWI methods were calculated and correlated with histological T stage of rectal cancer. The diagnostic performance of individual parameter for differentiating stage pT1-2 and pT3-4 tumors from both DWI techniques was assessed by receiver operating characteristic curve analysis. There were significant differences for the parameters of ADCmean, 50th, 75th, 90th, 95th percentiles, skewness and kurtosis of both DWI sequences in patients with pT1-2 as compared to those with pT3-4 tumors (P < 0.05), in addition to parameters including ADCmin (P = 0.015) and 25th percentile (P = 0.006) from rFOV DWI. Correlations were noted between T staging and above histogram parameters from rFOV DWI (r: −0.741–0.682) and fFOV DWI (r: −0.449–0.449), besides parameters of ADCmin (0.370) and 25th percentile (−0.425) from rFOV DWI. The AUCs of 75th and 90th percentiles from rFOV DWI were significantly higher than that from fFOV DWI (P = 0.0410 and P = 0.0208). The whole-lesion histogram analysis based on rFOV DWI was overall more advantageous than the one based on fFOV DWI in differentiating T staging of rectal cancer and the 90th percentile ADC from rFOV DWI was the value with the highest AUC (0.932).

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.

Prognostic value of ADC quantification for clinical outcome in uterine cervical cancer treated with concurrent chemoradiotherapy

OBJECTIVES

To investigate the prognostic value of diffusion-weighted imaging (DWI) in predicting clinical outcome in patients with cervical cancer after concurrent chemoradiotherapy (CCRT).

METHODS

We enrolled 124 cervical cancer patients who received definitive CCRT and underwent 3 T-MRI before and 1 month after initiating treatment. The mean apparent diffusion coefficient (ADC) value was measured on the tumor and the changes in ADC percentage (ΔADC_mean) between the two time points were calculated. The Cox proportion hazard model was used to evaluate the associations between imaging or clinical variables and progression-free survival (PFS), cancer-specific survival (CSS), and overall survival (OS).

RESULTS

In multivariate analysis, ΔADC_mean was the only independent predictor of PFS (hazard ratio [HR] = 0.2379, p = 0.005), CSS (HR = 0.310, p = 0.024), and OS (HR = 0.217, p = 0.002). Squamous cell carcinoma antigen, histology, and pretreatment tumor size were significantly independent predictors of PFS. Tumor size response was significantly independent predictor of CSS and OS. Using the cutoff values of ΔADC_mean, the PFS was significantly lower for ΔADC_mean < 27.8% (p = 0.001). The CSS and OS were significantly lower for ΔADC_mean < 16.1% (p = 0.002 and p < 0.001, respectively).

CONCLUSION

The percentage change in tumor ADC may be a useful predictor of disease progression and survival in patients with cervical cancer treated with CCRT.

KEY POINTS

• DWI is widely used as a potential marker of tumor viability. • Percentage change in tumor ADC (ΔADC _mean ) was an independent marker of PFS, CSS, and OS. • Survival was better in patients with ≥ ΔADC _mean cutoff value than with < the cutoff value.

Incremental Prognostic Value of Apparent Diffusion Coefficient Histogram Analysis in Head and Neck Squamous Cell Carcinoma

RATIONALE AND OBJECTIVES

We aimed to investigate the incremental prognostic value of apparent diffusion coefficient (ADC) histogram analysis in patients with head and neck squamous cell carcinoma (HNSCC) and integrate it into a multivariate prognostic model.

MATERIALS AND METHODS

A retrospective review of magnetic resonance imaging findings was conducted in patients with pathologically confirmed HNSCC between June 2012 and December 2015. For each tumor, six histogram parameters were derived: the 10th, 50th, and 90th percentiles of ADC (ADC₁₀, ADC₅₀, and ADC₉₀); mean ADC values (ADC_mean); kurtosis; and skewness. The clinical variables included age, sex, smoking status, tumor volume, and tumor node metastasis stage. The association of these histogram and clinical variables with overall survival (OS) was determined. Further validation of the histogram parameters as independent biomarkers was performed using multivariate Cox proportional hazard models combined with clinical variables, which was compared to the clinical model. Models were assessed with C index and receiver operating characteristic curve analyses for the 12- and 36-month OS.

RESULTS

Ninety-six patients were eligible for analysis. Median follow-up was 877 days (range, 54-1516 days). A total of 29 patients died during follow-up (30%). Patients with higher ADC values (ADC₁₀ > 0.958 × 10^-3 mm²/s, ADC₅₀ > 1.089 × 10^-3 mm²/s, ADC₉₀ > 1.152 × 10^-3 mm²/s, ADC_mean > 1.047 × 10^-3 mm²/s) and lower kurtosis (≤0.967) were significant predictors of poor OS (P < .100 for all). After adjusting for sex and tumor node metastasis stage, the ADC₉₀ and kurtosis are both significant predictors of OS with hazard ratios = 1.00 (95% confidence interval: 1.001-1.004) and 0.58 (95% confidence interval: 0.37-0.90), respectively. By adding the ADC parameters into the clinical model, the C index and diagnostic accuracies for the 12- and 36-month OS showed significant improvement.

CONCLUSIONS

ADC histogram analysis has incremental prognostic value in patients with HNSCC and increases the performance of a multivariable prognostic model in addition to clinical variables.

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.

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.

Histogram analysis of apparent diffusion coefficient maps for the differentiation between lymphoma and metastatic lymph nodes of squamous cell carcinoma in head and neck region

Background To clarify the nature of cervical malignant lymphadenopathy is highly important for the diagnosis and differential diagnosis of head and neck tumors. Purpose To investigate the role of first-order apparent diffusion coefficient (ADC) histogram analysis for differentiating lymphoma from metastatic lymph nodes of squamous cell carcinoma (SCC) in the head and neck region. Material and Methods Diffusion-weighted imaging (DWI) data of 67 patients (lymphoma, n = 20; SCC, n = 47) with malignant lymphadenopathy were retrospectively analyzed. The SCC group was divided into nasopharyngeal SCC and non-nasopharyngeal SCC groups. The ADC histogram features (ADC₁₀, ADC₂₅, ADC_mean, ADC_median, ADC₇₅, ADC₉₀, skewness, and kurtosis) were derived and then compared by independent-samples t-test and one-way analysis of variance test, respectively. Receiver operating characteristic curve analyses were employed to investigate diagnostic performance of the significant parameters. Results Lymphoma showed significantly lower ADC_mean, ADC_median, ADC₇₅, and ADC₉₀ than SCC (all P < 0.05). Setting ADC₉₀ = 0.719 × 10^-3mm²/s as the threshold value, optimal diagnostic performance was achieved (area under the curve [AUC] = 0.719, sensitivity = 95.7%, specificity = 50.0%). Subgroup analyses showed no significant difference between lymphoma and NPC (all P > 0.05). Lymphoma showed significantly lower ADC₂₅, ADC_mean, ADC_median, ADC₇₅, and ADC₉₀ than non-nasopharyngeal SCC (all P < 0.05). Optimal diagnostic performance (AUC = 0.847, sensitivity = 86.7%, specificity = 80.0%) could be achieved when setting ADC₉₀ = 0.943 × 10^-3mm²/s as the threshold value. Conclusion Given its limitations, our study has shown that first-order ADC histogram analysis is capable of differentiating lymphoma from metastatic lymph nodes of SCC, especially those of non-nasopharyngeal SCC.

Radiomics analysis of apparent diffusion coefficient in cervical cancer: A preliminary study on histological grade evaluation

BACKGROUND

The role of apparent diffusion coefficient (ADC)-based radiomics features in evaluating histopathological grade of cervical cancer is unresolved.

PURPOSE

To determine if there is a difference between radiomics features derived from center-slice 2D versus whole-tumor volumetric 3D for ADC measurements in patients with cervical cancer regarding tumor histopathological grade, and systematically assess the impact of the b value on radiomics analysis in ADC quantifications.

STUDY TYPE

Prospective.

SUBJECTS

In all, 160 patients with histopathologically confirmed squamous cell carcinoma of uterine cervix.

FIELD STRENGTH/SEQUENCE

Conventional and diffusion-weighted MR images (b values = 0, 800, 1000 s/mm² ) were acquired on a 3.0T MR scanner.

ASSESSMENT

Regions of interest (ROIs) were drawn manually along the margin of tumor on each slice, and then the center slice of the tumor was selected with naked eyes in the course of whole-tumor segmentation. A total of 624 radiomics features were derived from T₂ -weighted images and ADC maps. We randomly selected 50 cases and did the reproducibility analysis.

STATISTICAL TESTS

Parameters were compared using Wilcoxon signed rank test, Bland-Altman analysis, t-test, and least absolute shrinkage and selection operator (LASSO) regression with crossvalidation.

RESULTS

In all, 95 radiomics features were insensitive to ROI variation among T₂ images, ADC map of b800, and ADC map of b1000 (P > 0.0002). There was a significant statistical difference between the performances of 2D center-slice and 3D whole-tumor radiomics models in both ADC feature sets of b800 and b1000 (P < 0.0001, P < 0.0001). Compared with ADC features of b800 (0.3758 ± 0.0118), the model of b1000 ADC features appeared to be slightly lower in overall misclassification error (0.3642 ± 0.0162) (P = 0.0076).

DATA CONCLUSION

Several radiomics features extracted from T₂ images and ADC maps were highly reproducible. Whole-tumor volumetric 3D radiomics analysis had a better performance than using the 2D center-slice of tumor in stratifying the histological grade of cervical cancer. A b value of 1000 s/mm² is suggested as the optimal parameter in pelvic DWI scans.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:280-290.

Whole-tumor apparent diffusion coefficient (ADC) histogram analysis to differentiate benign peripheral neurogenic tumors from soft tissue sarcomas

BACKGROUND

Apparent diffusion coefficient (ADC) histogram analyses have been used to differentiate tumor grades and predict therapeutic responses in various anatomic sites with moderate success.

PURPOSE

To determine the ability of diffusion-weighted imaging (DWI) with a whole-tumor ADC histogram analysis to differentiate benign peripheral neurogenic tumors (BPNTs) from soft tissue sarcomas (STSs).

STUDY TYPE

Retrospective study, single institution.

SUBJECTS

In all, 25 BPNTs and 31 STSs.

FIELD STRENGTH/SEQUENCE

Two-b value DWI (b-values = 0, 1000s/mm2 ) was at 3.0T.

ASSESSMENT

The histogram parameters of whole-tumor for ADC were calculated by two radiologists and compared between BPNTs and STSs.

STATISTICAL TESTS

Nonparametric tests were performed for comparisons between BPNTs and STSs. P < 0.05 was considered statistically significant. The ability of each parameter to differentiate STSs from BPNTs was evaluated using area under the curve (AUC) values derived from a receiver operating characteristic curve analysis.

RESULTS

The mean ADC and all percentile parameters were significantly lower in STSs than in BPNTs (P < 0.001-0.009), with AUCs of 0.703-0.773. However, the coefficient of variation (P = 0.020 and AUC = 0.682) and skewness (P = 0.012 and AUC = 0.697) were significantly higher in STSs than in BPNTs. Kurtosis (P = 0.295) and entropy (P = 0.604) did not differ significantly between BPNTs and STSs.

DATA CONCLUSION

Whole-tumor ADC histogram parameters except kurtosis and entropy differed significantly between BPNTs and STSs.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

The diagnostic role of diffusion-weighted magnetic resonance imaging in hypopharyngeal carcinoma

The aim of the present study was to assess the role of diffusion-weighted magnetic resonance imaging (DWI) and apparent diffusion coefficient (ADC) values in hypopharyngeal carcinoma. A total of 40 hypopharyngeal carcinoma tissues and 15 benign lesion tissues were retrospectively analyzed. DWI, and T1- and T2-weighted magnetic resonance imaging (MRI) was performed. The sensitivity, specificity and accuracy of conventional MRI were 97.5, 66.7, and 89.1%, respectively. The mean ADC value [diffusion sensitive factor (b)=1,000× sec/mm²) for hypopharyngeal carcinomas was (1.0285±0.0328)×10⁻³ mm²/sec, which was significantly lower than the mean ADC value for benign lesions [(1.5333±0.1061)×10⁻³ mm²/sec; P<0.001]. Receiver operating characteristic (ROC) curve analysis revealed that the area under the curve (AUC) was 0.921 while the optimal threshold for the cut-off point of the ADC was 1.075×10⁻³ mm²/sec. The mean ADC value of the metastatic nodes was (0.9184±0.0538)×10⁻³ mm²/sec, lower than the mean value for the benign nodes [(1.2538±0.1145)×10⁻³ mm²/sec; P=0.005]. Two groups were created according to the mean of the ADC value of hypopharyngeal carcinomas [≤(1.0285±0.0328)×10⁻³ mm²/sec vs. >(1.0285±0.0328)×10⁻³ mm²/sec]. The 2-year survival rates of the two groups were 55.6 and 100.0%, respectively (P=0.024). ADC values may aid in distinguishing hypopharyngeal carcinomas from benign lesions and differentiating metastatic lymph nodes of hypopharyngeal squamous cell carcinomas from reactive cervical lymph nodes. In conclusion, mean ADC values may be useful prognostic factors in univariate analysis of hypopharyngeal carcinoma.

Differentiation of prostate cancer and stromal hyperplasia in the transition zone with histogram analysis of the apparent diffusion coefficient

Background Prostate cancer and stromal hyperplasia (SH) in the transition zone (TZ) are difficult to discriminate by conventional magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI). Purpose To investigate the apparent diffusion coefficient (ADC) of prostate cancer and SH in the TZ with histogram analysis and the ability of ADC metrics to differentiate between these two tissues. Material and Methods Thirty-three cancer and 29 SH lesions in the TZ of 54 patients undergoing preoperative DWI (b-value 0, 1000 s/mm²) were analyzed. All the lesions on the MR images were localized based on histopathologic correlations. The 10th, 25th, and 50th percentiles, and the mean ADC values were calculated for the two tissues and compared. The efficiencies of the 10th, 25th, and 50th ADC percentiles in differentiating the two tissues were compared with that of the mean ADC with receiver operating characteristic (ROC) analysis. Results The 10th, 25th, and 50th percentiles and mean ADC values (×10^-3 mm²/s) were 0.86 ± 0.15, 0.89 ± 0.16, 0.94 ± 0.16, and 1.03 ± 0.17 in SH and 0.64 ± 0.12, 0.69 ± 0.12, 0.72 ± 0.16, and 0.83 ± 0.15 in TZ cancer, respectively. The parameters were all significantly lower in cancer than SH. The 10th ADC percentile yielded an area under the ROC curve (AUC) of 0.87 for the differentiation of carcinomas from SH, which was higher than the mean ADC (0.80) ( P < 0.05), and the AUCs of the 25th (0.82) and 50th (0.83) percentiles exhibited no differences from those of the mean ADC ( P > 0.05). Conclusion Histogram analysis of ADC values may potentially improve the differentiation of prostate cancer from SH in the TZ.

Developing and validating a multivariable prediction model to improve the diagnostic accuracy in determination of cervical versus endometrial origin of uterine adenocarcinomas: A prospective MR study combining diffusion-weighted imaging and spectroscopy

BACKGROUND

A triage test to assist clinical decision-making on choosing primary chemoradiation for cervical carcinomas or primary surgery for endometrial carcinomas is important.

PURPOSE OR HYPOTHESIS

To develop and validate a multiparametric prediction model based on MR imaging and spectroscopy in distinguishing adenocarcinomas of uterine cervical or endometrial origin.

STUDY TYPE

Prospective diagnostic accuracy study.

POPULATION

Eighty-seven women: 25 cervical and 62 endometrial adenocarcinomas divided into training (n = 43; cervical/endometrial adenocarcinomas = 11/32) and validation (n = 44; 14/30) datasets.

FIELD STRENGTH/SEQUENCE

The 3T diffusion-weighted (DW) MR imaging and MR spectroscopy.

ASSESSMENT

Morphology, volumetric DW MR imaging and spectroscopy (MDS) scoring system with total points 0-5, based on presence of the following MR features assessed independently by two radiologists: (a) epicenter at the cervix, (b) rim enhancement, (c) disrupted cervical stromal integrity, (d) mean volumetric apparent diffusion coefficient values (ADCmean) higher than 0.98 × 10-3 mm² /s, (e) fatty acyl δ 1.3 ppm more than 161.92 mM. Histopathology as gold standard.

STATISTICAL TESTS

Logistic regression and receiver operator characteristic (ROC) curves analysis.

RESULTS

For both the training and validation datasets, the MDS score achieved an accuracy of 93.0% and 84.1%, significantly higher than that of morphology (88.4% and 79.5%), ADC value (74.4% and 68.2%), and spectroscopy (81.4% and 68.2%; P < 0.05 for all). The performances of the scoring were superior to the morphology in the training dataset (areas under the receiver operating characteristics curve [AUC] = 0.95 vs. 0.89; P = 0.046), but not in the validation dataset (AUC = 0.90 vs. 0.85; P = 0.289).

DATA CONCLUSION

MDS score has potentials to improve distinguishing adenocarcinomas of cervical or endometrial origin, and warrants large-scale studies for further validation.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1654-1666.

Histogram analysis of apparent diffusion coefficient for monitoring early response in patients with advanced cervical cancers undergoing concurrent chemo-radiotherapy

Background Apparent diffusion coefficient (ADC) histogram analysis has been widely used in determining tumor prognosis. Purpose To investigate the dynamic changes of ADC histogram parameters during concurrent chemo-radiotherapy (CCRT) in patients with advanced cervical cancers. Material and Methods This prospective study enrolled 32 patients with advanced cervical cancers undergoing CCRT who received diffusion-weighted (DW) magnetic resonance imaging (MRI) before CCRT, at the end of the second and fourth week during CCRT and one month after CCRT completion. The ADC histogram for the entire tumor volume was generated, and a series of histogram parameters was obtained. Dynamic changes of those parameters in cervical cancers were investigated as early biomarkers for treatment response. Results All histogram parameters except AUC_low showed significant changes during CCRT (all P < 0.05). There were three variable trends involving different parameters. The mode, 5th, 10th, and 25th percentiles showed similar early increase rates (33.33%, 33.99%, 34.12%, and 30.49%, respectively) at the end of the second week of CCRT. The pre-CCRT 5th and 25th percentiles of the complete response (CR) group were significantly lower than those of the partial response (PR) group. Conclusion A series of ADC histogram parameters of cervical cancers changed significantly at the early stage of CCRT, indicating their potential in monitoring early tumor response to therapy.

Apparent diffusion coefficient for prediction of parametrial invasion in cervical cancer: a critical evaluation based on stratification to a Likert scale using T2-weighted imaging

PURPOSE

To evaluate the value of apparent diffusion coefficient (ADC) for determining parametrial invasion (PMI) in cervical cancer, by stratifying them into subgroups based on a Likert scale using T2-weighted imaging (T2WI).

MATERIALS AND METHODS

This retrospective study included 87 patients with FIGO stage IA2-IIB cervical cancer who underwent preoperative MRI followed by radical hysterectomy. Radiological PMI was assessed on T2WI using a six-point Likert scale and ADC values of the tumors were measured. MRI findings were compared between patients with and without PMI. Differences in ADC according to the Likert scale were also assessed.

RESULTS

19 (21.8%) patients had pathological PMI. The prevalence of PMI was significantly associated with Likert scale (P < 0.001). ADC values significantly differed according to Likert scale (P < 0.001). However, only tumors with a Likert score of 0 (MRI-invisible) had significantly greater ADC than others (P < 0.001) while no significant difference was observed among tumors with Likert scores of 1-5 (P = 0.070-0.889). Patients with PMI had significantly lower ADC values than those without PMI (P = 0.034). However, no significant difference was seen between patients with and without PMI within each Likert score group (P = 0.180-0.857).

CONCLUSION

T2WI-based Likert score for radiological PMI and ADC values of the tumor were significantly associated with pathological PMI. However, the apparent association seen between ADC values and PMI may be due to contribution of high ADC values of MRI-invisible tumors rather than reflecting their relationship.

The value of advanced MRI techniques in the assessment of cervical cancer: a review

OBJECTIVES

To assess the value of new magnetic resonance imaging (MRI) techniques in cervical cancer.

METHODS

We searched PubMed and MEDLINE and reviewed articles published from 1990 to 2016 to identify studies that used MRI techniques, such as diffusion weighted imaging (DWI), intravoxel incoherent motion (IVIM) and dynamic contrast enhancement (DCE) MRI, to assess parametric invasion, to detect lymph node metastases, tumour subtype and grading, and to detect and predict tumour recurrence.

RESULTS

Seventy-nine studies were included. The additional use of DWI improved the accuracy and sensitivity of the evaluation of parametrial extension. Most studies reported improved detection of nodal metastases. Functional MRI techniques have the potential to assess tumour subtypes and tumour grade differentiation, and they showed additional value in detecting and predicting treatment response. Limitations included a lack of technical standardisation, which limits reproducibility.

CONCLUSIONS

New advanced MRI techniques allow improved analysis of tumour biology and the tumour microenvironment. They can improve TNM staging and show promise for tumour classification and for assessing the risk of tumour recurrence. They may be helpful for developing optimised and personalised therapy for patients with cervical cancer.

TEACHING POINTS

• Conventional MRI plays a key role in the evaluation of cervical cancer. • DWI improves tumour delineation and detection of nodal metastases in cervical cancer. • Advanced MRI techniques show promise regarding histological grading and subtype differentiation. • Tumour ADC is a potential biomarker for response to treatment.

Whole-lesion ADC histogram and texture analysis in predicting recurrence of cervical cancer treated with CCRT

Purpose

To explore the value of whole-lesion apparent diffusion coefficient (ADC) histogram and texture analysis in predicting tumor recurrence of advanced cervical cancer treated with concurrent chemo-radiotherapy (CCRT).

Methods

36 women with pathologically confirmed advanced cervical squamous carcinomas were enrolled in this prospective study. 3.0 T pelvic MR examinations including diffusion weighted imaging (b = 0, 800 s/mm²) were performed before CCRT (pre-CCRT) and at the end of 2nd week of CCRT (mid-CCRT). ADC histogram and texture features were derived from the whole volume of cervical cancers.

Results

With a mean follow-up of 25 months (range, 11 ∼ 43), 10/36 (27.8%) patients ended with recurrence. Pre-CCRT 75th, 90th, correlation, autocorrelation and mid-CCRT ADC_mean, 10th, 25th, 50th, 75th, 90th, autocorrelation can effectively differentiate the recurrence from nonrecurrence group with area under the curve ranging from 0.742 to 0.850 (P values range, 0.001 ∼ 0.038).

Conclusions

Pre- and mid-treatment whole-lesion ADC histogram and texture analysis hold great potential in predicting tumor recurrence of advanced cervical cancer treated with CCRT.

Diffusion-weighted imaging in gynaecological malignancy

Diffusion weighted imaging (DWI) has become an essential part of the gynaecological magnetic resonance imaging (MRI) protocol. DWI is used as an adjunct to conventional MRI sequences and has been shown to improve reporting accuracy in the imaging of gynaecological malignancy. In this review, we discuss the role of DWI in the diagnosis, staging, and assessment of treatment response of endometrial, cervical, and ovarian cancer. We also review the role of DWI in the assessment of the sonographically indeterminate ovarian lesion. Further, we highlight potential pitfalls that can beset the accurate interpretation of DWI in patients with gynaecological malignancy.

Intravoxel Incoherent Motion Diffusion-Weighted Magnetic Resonance Imaging of Cervical Cancer With Different b-Values

OBJECTIVE

The aims of this study were to evaluate the dependence of diffusion parameters on the b values adopted for intravoxel incoherent motion diffusion-weighted magnetic resonance imaging and to investigate the application value of multiple diffusion parameters obtained from monoexponential and biexponential models in subjects with a normal cervix and in cervical cancer patients.

METHODS

A total of 120 female patients with cervical cancer and 21 female control subjects with a normal cervix underwent diffusion-weighted magnetic resonance imaging with 13 b values (0-2000 s/mm) at 3 T. The standard apparent diffusion coefficient (Dst), diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) were calculated by fitting with monoexponential and biexponential models at 2 different ranges of b values: 0 to 1000 and 0 to 2000 s/mm. A univariate analysis was performed to identify factors that could distinguish cervical carcinoma from normal cervical tissue. Parameters that correlated with the pathological grade and stage of cervical cancer were also evaluated. Receiver operating characteristic curves were used to evaluate the diagnostic efficiency of every parameter.

RESULTS

All the tested parameters, except the D* of the 2 different ranges of b value groups, significantly differed between the patients with cervical carcinoma and control subjects (P < 0.01). D2000, Dst2000, and D1000 showed comparable diagnostic value, with an area under the curve of 0.923, 0.909, and 0.907, respectively. Dst2000, D2000, Dst1000, and D1000 differed significantly among the 3 degrees of cervical stromal infiltration depth (P < 0.05).

CONCLUSIONS

D2000 and Dst2000 tended to outperform D1000 in terms of diagnostic efficiency, but there was no significant difference in their ability to differentiate cervical carcinoma from normal cervix. Cervical cancers with lower Dst and D values tended to have greater infiltration depth.

Investigation of volumetric apparent diffusion coefficient histogram analysis for assessing complete response and clinical outcomes following pre-operative chemoradiation treatment for rectal carcinoma

PURPOSE

To investigate the relationship of pre-treatment volumetric apparent diffusion coefficient (ADC) histogram parameters with post-operative histopathologic treatment response and clinical outcomes following pre-operative chemoradiation treatment (CRT) in rectal cancer.

MATERIALS AND METHODS

In a Health Insurance Portability and Accountability Act compliant retrospective study, 78 rectal cancer patients treated with pre-operative CRT and rectal MRI were included. MR imaging analysis was performed using OncoTREAT (software tool). Multiple volumetric ADC histogram parameters (voxel distribution across ADC ranges, kurtosis, and skewness) were assessed. Correlation was made to post-operative pathological complete response, clinical, or radiological evidence of disease progression using the Mann-Whitney test.

RESULTS

Post CRT, 8 patients showed pathologic complete response and 13 patients showed distant disease progression. Pre-treatment mean ADC was 1.2 × 10^-3 mm²/s (range 0.3-1.99 × 10^-3 mm²/s). Mean kurtosis measured was 0.56 (range -1 to 6; SD 1.36). Mean skewness was 0.3 (range -1 to 2; SD 0.69). Skewness had significant correlation (p value = 0.006) with disease progression. The mean rectal tumor volume was 24cc (range 1cc-134cc). Pre-treatment MRI tumor volume showed significant correlation (p value = 0.013) with pathologic complete response. Mean ADC and percentage voxels distribution against ADC ranges had no significant correlation with treatment response or disease outcomes.

CONCLUSION

Volumetric ADC histogram analysis of pre-CRT rectal cancer MRI appears promising for prediction of post-CRT complete response and disease progression.

Predicting tumor recurrence in patients with cervical carcinoma treated with definitive chemoradiotherapy: value of quantitative histogram analysis on diffusion-weighted MR images

Background Further research is required for evaluating the use of ADC histogram analysis in more advanced stages of cervical cancer treated with definitive chemoradiotherapy (CRT). Purpose To investigate the utility of apparent diffusion coefficient (ADC) histogram derived from diffusion-weighted magnetic resonance images in cervical cancer patients treated with definitive CRT. Material and Methods The clinical and radiological data of 50 patients with histologically proven cervical squamous cell carcinoma treated with definitive CRT were retrospectively analyzed. The impact of clinicopathological factors and ADC histogram parameters on prognostic factors and treatment outcomes was assessed. Results The mean and median ADC values for the cohort were 1.043 ± 0.135 × 10^-3 mm²/s and 1.018 × 10^-3 mm²/s (range, 0.787-1.443 × 10^-3 mm²/s). The mean ADC was significantly lower for patients with advanced stage (≥IIB) or lymph node metastasis compared with patients with stage <IIB or no lymph node metastasis. The mean ADC, 75th percentile ADC (ADC75), 90th percentile ADC (ADC90), and 95th percentile ADC (ADC95) were significantly lower in patients with tumor recurrence compared with patients without recurrence. In multivariate analysis, tumor size, ADC75 and ADC95 were independent prognostic factors for both overall survival and disease-free survival. Conclusion ADC histogram parameters could be markers for disease recurrence and for predicting survival outcomes. ADC75, ADC90, and ADC95 of the primary tumor were significant predictors of disease recurrence in cervical cancer patients treated with definitive CRT.

Non-small cell lung cancer: Whole-lesion histogram analysis of the apparent diffusion coefficient for assessment of tumor grade, lymphovascular invasion and pleural invasion

PURPOSE

Investigating the diagnostic accuracy of histogram analyses of apparent diffusion coefficient (ADC) values for determining non-small cell lung cancer (NSCLC) tumor grades, lymphovascular invasion, and pleural invasion.

MATERIALS AND METHODS

We studied 60 surgically diagnosed NSCLC patients. Diffusion-weighted imaging (DWI) was performed in the axial plane using a navigator-triggered single-shot, echo-planar imaging sequence with prospective acquisition correction. The ADC maps were generated, and we placed a volume-of-interest on the tumor to construct the whole-lesion histogram. Using the histogram, we calculated the mean, 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles of ADC, skewness, and kurtosis. Histogram parameters were correlated with tumor grade, lymphovascular invasion, and pleural invasion. We performed a receiver operating characteristics (ROC) analysis to assess the diagnostic performance of histogram parameters for distinguishing different pathologic features.

RESULTS

The ADC mean, 10th, 25th, 50th, 75th, 90th, and 95th percentiles showed significant differences among the tumor grades. The ADC mean, 25th, 50th, 75th, 90th, and 95th percentiles were significant histogram parameters between high- and low-grade tumors. The ROC analysis between high- and low-grade tumors showed that the 95th percentile ADC achieved the highest area under curve (AUC) at 0.74. Lymphovascular invasion was associated with the ADC mean, 50th, 75th, 90th, and 95th percentiles, skewness, and kurtosis. Kurtosis achieved the highest AUC at 0.809. Pleural invasion was only associated with skewness, with the AUC of 0.648.

CONCLUSIONS

ADC histogram analyses on the basis of the entire tumor volume are able to stratify NSCLCs' tumor grade, lymphovascular invasion and pleural invasion.

Apparent diffusion coefficient histogram shape analysis for monitoring early response in patients with advanced cervical cancers undergoing concurrent chemo-radiotherapy

Background

To explore the role of apparent diffusion coefficient (ADC) histogram shape related parameters in early assessment of treatment response during the concurrent chemo-radiotherapy (CCRT) course of advanced cervical cancers.

Methods

This prospective study was approved by the local ethics committee and informed consent was obtained from all patients. Thirty-two patients with advanced cervical squamous cell carcinomas underwent diffusion weighted magnetic resonance imaging (b values, 0 and 800 s/mm²) before CCRT, at the end of 2nd and 4th week during CCRT and immediately after CCRT completion. Whole lesion ADC histogram analysis generated several histogram shape related parameters including skewness, kurtosis, s-sD_av, width, standard deviation, as well as first-order entropy and second-order entropies. The averaged ADC histograms of 32 patients were generated to visually observe dynamic changes of the histogram shape following CCRT.

Results

All parameters except width and standard deviation showed significant changes during CCRT (all P < 0.05), and their variation trends fell into four different patterns. Skewness and kurtosis both showed high early decline rate (43.10 %, 48.29 %) at the end of 2nd week of CCRT. All entropies kept decreasing significantly since 2 weeks after CCRT initiated. The shape of averaged ADC histogram also changed obviously following CCRT.

Conclusions

ADC histogram shape analysis held the potential in monitoring early tumor response in patients with advanced cervical cancers undergoing CCRT.

ADC histogram analysis for adrenal tumor histogram analysis of apparent diffusion coefficient in differentiating adrenal adenoma from pheochromocytoma

PURPOSE

To determine the diagnostic performance of apparent diffusion coefficient (ADC) histogram analysis in diffusion-weighted (DW) magnetic resonance imaging (MRI) for differentiating adrenal adenoma from pheochromocytoma.

MATERIALS AND METHODS

We retrospectively evaluated 52 adrenal tumors (39 adenomas and 13 pheochromocytomas) in 47 patients (21 men, 26 women; mean age, 59.3 years; range, 16-86 years) who underwent DW 3.0T MRI. Histogram parameters of ADC (b-values of 0 and 200 [ADC₂₀₀ ], 0 and 400 [ADC₄₀₀ ], and 0 and 800 s/mm² [ADC₈₀₀ ])-mean, variance, coefficient of variation (CV), kurtosis, skewness, and entropy-were compared between adrenal adenomas and pheochromocytomas, using the Mann-Whitney U-test. Receiver operating characteristic (ROC) curves for the histogram parameters were generated to differentiate adrenal adenomas from pheochromocytomas. Sensitivity and specificity were calculated by using a threshold criterion that would maximize the average of sensitivity and specificity.

RESULTS

Variance and CV of ADC₈₀₀ were significantly higher in pheochromocytomas than in adrenal adenomas (P < 0.001 and P = 0.001, respectively). With all b-value combinations, the entropy of ADC was significantly higher in pheochromocytomas than in adrenal adenomas (all P ≤ 0.001), and showed the highest area under the ROC curve among the ADC histogram parameters for diagnosing adrenal adenomas (ADC₂₀₀ , 0.82; ADC₄₀₀ , 0.87; and ADC₈₀₀ , 0.92), with sensitivity of 84.6% and specificity of 84.6% (cutoff, ≤2.82) with ADC₂₀₀ ; sensitivity of 89.7% and specificity of 84.6% (cutoff, ≤2.77) with ADC₄₀₀ ; and sensitivity of 94.9% and specificity of 92.3% (cutoff, ≤2.67) with ADC₈₀₀ .

CONCLUSION

ADC histogram analysis of DW MRI can help differentiate adrenal adenoma from pheochromocytoma.

LEVEL OF EVIDENCE

3 J. Magn. Reson. Imaging 2017;45:1195-1203.