Whole-lesion ADC histogram analysis versus single-slice ADC measurement for the differentiation of benign and malignant soft tissue tumors

Whole-lesion iodine map histogram analysis in the risk classification of gastrointestinal stromal tumors: comparison with single-slice iodine concentration measurements

PURPOSE

To evaluate and compare the diagnostic performances of whole-lesion iodine map (IM) histogram analysis and single-slice IM measurement in the risk classification of gastrointestinal stromal tumors (GISTs).

METHODS

Thirty-seven patients with GISTs, including 19 with low malignant underlying GISTs (LG-GISTs) and 18 with high malignant underlying GISTs (HG-GISTs), were evaluated with dual-energy computed tomography (DECT). Whole-lesion IM histogram parameters (mean; median; minimum; maximum; standard deviation; variance; 1st, 10th, 25th, 50th, 75th, 90th, and 99th percentile; kurtosis, skewness, and entropy) were computed for each lesion. In other sessions, iodine concentrations (ICs) were derived from the IM by placing regions of interest (ROIs) on the tumor slices and normalizing them to the iodine concentration in the aorta. Both quantitative analyses were performed on the venous phase images. The diagnostic accuracies of the two methods were assessed and compared.

RESULTS

The minimum, maximum, 1st, 10th, and 25th percentile of the whole-lesion IM histogram and the IC and normalized IC (NIC) of the single-slice IC measurement significantly differed between LG- and HG-GISTs (p < 0.001 - p = 0.042). The minimum value in the histogram analysis (AUC = 0.844) and the NIC in the single-slice measurement analysis (AUC = 0.886) showed the best diagnostic performances. The NIC of single-slice measurements had a diagnostic performance similar to that of the whole-lesion IM histogram analysis (p = 0.618).

CONCLUSIONS

Both whole-lesion IM histogram analysis and single-slice IC measurement can differentiate LG-GISTs and HG-GISTs with similar diagnostic performances.

CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies

OBJECTIVE

To systematically review radiomic feature reproducibility and model validation strategies in recent studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas, thus updating a previous version of this review which included studies published up to 2020.

METHODS

A literature search was conducted on EMBASE and PubMed databases for papers published between January 2021 and March 2023. Data regarding radiomic feature reproducibility and model validation strategies were extracted and analyzed.

RESULTS

Out of 201 identified papers, 55 were included. They dealt with radiomics of bone (n = 23) or soft-tissue (n = 32) tumors. Thirty-two (out of 54 employing manual or semiautomatic segmentation, 59%) studies included a feature reproducibility analysis. Reproducibility was assessed based on intra/interobserver segmentation variability in 30 (55%) and geometrical transformations of the region of interest in 2 (4%) studies. At least one machine learning validation technique was used for model development in 34 (62%) papers, and K-fold cross-validation was employed most frequently. A clinical validation of the model was reported in 38 (69%) papers. It was performed using a separate dataset from the primary institution (internal test) in 22 (40%), an independent dataset from another institution (external test) in 14 (25%) and both in 2 (4%) studies.

CONCLUSIONS

Compared to papers published up to 2020, a clear improvement was noted with almost double publications reporting methodological aspects related to reproducibility and validation. Larger multicenter investigations including external clinical validation and the publication of databases in open-access repositories could further improve methodology and bring radiomics from a research area to the clinical stage.

CRITICAL RELEVANCE STATEMENT

An improvement in feature reproducibility and model validation strategies has been shown in this updated systematic review on radiomics of bone and soft-tissue sarcomas, highlighting efforts to enhance methodology and bring radiomics from a research area to the clinical stage.

KEY POINTS

• 2021-2023 radiomic studies on CT and MRI of musculoskeletal sarcomas were reviewed. • Feature reproducibility was assessed in more than half (59%) of the studies. • Model clinical validation was performed in 69% of the studies. • Internal (44%) and/or external (29%) test datasets were employed for clinical validation.

The value of an apparent diffusion coefficient histogram model in predicting meningioma recurrence

OBJECTIVE

To investigate the predictive value of a model combining conventional MRI features and apparent diffusion coefficient (ADC) histogram parameters for meningioma recurrence.

MATERIALS AND METHODS

Seventy-two meningioma patients confirmed by surgical and pathological findings in our hospital (January 2017-June 2020) were retrospectively and divided into the recurrence and non-recurrence group. MaZda software was used to delineate the region of interest at the largest tumor level and generate histogram parameters. Univariate and multivariate logistic regression analysis were used to construct the nomogram for predicting recurrence. The predictive efficacy and diagnostic of this model were assessed by calibration and decision curve analysis, and receiver operating characteristic curve, respectively.

RESULTS

Maximum diameter, necrosis, enhancement uniformity, age, Simpson, tumor shape, and ADC first percentile (ADCp1) were significantly different between the two groups (p < 0.05), with the latter four being independent risk factors for recurrence. The model constructed combining the four factors had the best predictive efficacy, and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.965(0.892-0.994), 90.3%, 92.6%, 88.9%, 83.3%, and 95.2%, respectively. The calibration curve showed good agreement between the model-predicted and actual probabilities of recurrence. The decision curve analysis indicated good clinical availability of the model.

CONCLUSION

This model based on conventional MRI features and ADC histogram parameters can directly and reliably predict meningioma recurrence, providing a guiding basis for selecting treatment options and individualized treatment.

Differentiation between pleomorphic adenoma and schwannoma in the parapharyngeal space: histogram analysis of apparent diffusion coefficient

OBJECTIVES

To elucidate the differences between pleomorphic adenomas and schwannomas occurring in the parapharyngeal space by histogram analyses of apparent diffusion coefficient (ADC) values measured with diffusion-weighted MRI.

METHODS

This retrospective study included 29 patients with pleomorphic adenoma and 22 patients with schwannoma arising in the parapharyngeal space or extending into the parapharyngeal space from the parotid region. Using pre-operative MR images, ADC values of tumor lesions showing the maximum diameter were measured. The regions of interest for ADC measurement were placed by contouring the tumor margin, and the histogram metrics of ADC values were compared between pleomorphic adenomas and schwannomas regarding the mean, skewness, and kurtosis by Wilcoxon's rank sum test. Subsequent to the primary analysis which included all lesions, we performed two subgroup analyses regarding b-values and magnetic field strength used for MRI.

RESULTS

The mean ADC values did not show significant differences between pleomorphic adenomas and schwannomas for the primary and subgroup analyses. Schwannomas showed higher skewness (p = 0.0001) and lower kurtosis (p = 0.003) of ADC histograms compared with pleomorphic adenomas in the primary analysis. Skewness was significantly higher in schwannomas in all the subgroup analyses. Kurtosis was consistently lower in schwannomas but did not reach statistical significance in one subgroup analysis.

CONCLUSIONS

Skewness and kurtosis showed significant differences between pleomorphic adenomas and schwannomas occupying the parapharyngeal space, but the mean ADC values did not. Our results suggest that the skewness and kurtosis of ADC histograms may be useful in differentiating these two parapharyngeal tumors.

Development of a nomogram based on pericoronary adipose tissue histogram parameters to differentially diagnose acute coronary syndrome

PURPOSE

To develop a nomogram based on pericoronary adipose tissue (PCAT) histogram parameters to identify patients with acute coronary syndrome (ACS).

MATERIALS AND METHODS

This study retrospectively enrolled 114 and 383 eligible patients with ACS and stable coronary artery disease (CAD), respectively, and divided them into training and testing cohorts in a 7:3 ratio. A blinded radiologist obtained PCAT histogram parameters from the right coronary artery's proximal segment using fully automated software and compared clinical characteristics and PCAT histogram parameters between the two patient groups. The binary logistic regression included significant parameters (P < 0.05), and a nomogram was constructed.

RESULTS

In both the training and testing cohorts, the mean, 10th percentile, 90th percentile, median, and minimum values of PCAT were higher, and the interquartile range, skewness, and variance values of PCAT were lower in patients with ACS than in those with stable CAD (P ≤ 0.001). The mean (OR = 4.007), median (OR = 0.576), minimum (OR = 0.893), skewness (OR = 85,158.806) and variance (OR = 1.013) values of PCAT were independent risk factors for ACS and stable CAD in the training cohort. The nomogram was constructed using the five variables mentioned above with area under the curve values of 0.903 and 0.897, respectively, while the calibration and decision curves showed the nomogram's good clinical efficacy for the training and testing cohorts.

CONCLUSIONS

The constructed nomogram had good discrimination and accuracy and can be a noninvasive tool to intuitively and individually distinguish between ACS and stable CAD.

Impact of Adding Mean Apparent Diffusion Coefficient (ADCmean) Measurements to O-RADS MRI Scoring For Adnexal Lesions Characterization: A Combined O-RADS MRI/ADCmean Approach

RATIONALE AND OBJECTIVES

Evaluate the impact of adding mean apparent diffusion coefficient (ADCmean) measurements to the Ovarian-Adnexal Imaging Reporting and Data System MRI (O-RADS MRI) scoring for adnexal lesion characterization using a combined O-RADS MRI/ADCmean reading approach.

MATERIALS AND METHODS

This prospective study included 90 women who underwent pelvic MRI for adnexal lesions diagnosis and characterization. Two readers scored the adnexal lesions using the O-RADS MRI scoring independently and in consensus. A third reader calculated ADCmean measurements. The final diagnoses were determined by histo-pathology (n = 77) or follow-up imaging (n = 13). Areas under the curves (AUCs) and diagnostic performance metrics were calculated for the O-RADS MRI scoring, ADCmean, and combined O-RADS MRI/ADCmean thresholds. P-value <0.05 was significant.

RESULTS

116 adnexal lesions (71 benign, 45 malignant) were analyzed. The optimal thresholds to predict malignant adnexal lesions were O-RADS MRI score >3 and ADCmean value ≤1.08 × 10^-3 mm²/s (AUC 0.926 and 0.823; sensitivity 97.7% and 95.5%; specificity 87.3% and 68%; positive predictive value (PPV) 83% and 66.2%; positive likelihood ratio (PLR) 7.7 and 3.08, respectively). Compared to the O-RADS MRI scoring, a combined threshold of O-RADS MRI >3/ADCmean ≤1.08 × 10^-3 mm²/s, yielded a reduction of false positives, a significant increase in the specificity (97.1%, p = 0.005), PPV (95.4%, p = 0.002), and PLR (33.1, p <0.0001), and non-significant change in the AUC (0.953, p = 0.252), and sensitivity (93.3%, p = 0.467).

CONCLUSION

The diagnostic performance of O-RADS MRI scoring to characterize adnexal lesions could be improved by adding the ADCmean values through reducing false positives, increasing specificity, and maintaining good sensitivity.

The diagnostic value of ADC histogram and direct ADC measurements for coexisting isocitrate dehydrogenase mutation and O6-methylguanine-DNA methyltransferase promoter methylation in glioma

Objectives

To non-invasively predict the coexistence of isocitrate dehydrogenase (IDH) mutation and O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation in adult-type diffuse gliomas using apparent diffusion coefficient (ADC) histogram and direct ADC measurements and compare the diagnostic performances of the two methods.

Materials and methods

A total of 118 patients with adult-type diffuse glioma who underwent preoperative brain magnetic resonance imaging (MRI) and diffusion weighted imaging (DWI) were included in this retrospective study. The patient group included 40 patients with coexisting IDH mutation and MGMT promoter methylation (IDHmut/MGMTmet) and 78 patients with other molecular status, including 32 patients with IDH wildtype and MGMT promoter methylation (IDHwt/MGMTmet), one patient with IDH mutation and unmethylated MGMT promoter (IDHmut/MGMTunmet), and 45 patients with IDH wildtype and unmethylated MGMT promoter (IDHwt/MGMTunmet). ADC histogram parameters of gliomas were extracted by delineating the region of interest (ROI) in solid components of tumors. The minimum and mean ADC of direct ADC measurements were calculated by placing three rounded or elliptic ROIs in solid components of gliomas. Receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) were used to evaluate the diagnostic performances of the two methods.

Results

The 10th percentile, median, mean, root mean squared, 90th percentile, skewness, kurtosis, and minimum of ADC histogram analysis and minimum and mean ADC of direct measurements were significantly different between IDHmut/MGMTmet and the other glioma group (P < 0.001 to P = 0.003). In terms of single factors, 10th percentile of ADC histogram analysis had the best diagnostic efficiency (AUC = 0.860), followed by mean ADC obtained by direct measurements (AUC = 0.844). The logistic regression model combining ADC histogram parameters and direct measurements had the best diagnostic efficiency (AUC = 0.938), followed by the logistic regression model combining the ADC histogram parameters with statistically significant difference (AUC = 0.916) and the logistic regression model combining minimum ADC and mean ADC (AUC = 0.851).

Conclusion

Both ADC histogram analysis and direct measurements have potential value in predicting the coexistence of IDHmut and MGMTmet in adult-type diffuse glioma. The diagnostic performance of ADC histogram analysis was better than that of direct ADC measurements. The combination of the two methods showed the best diagnostic performance.

Correlation of Apparent Diffusion Coefficient With Proliferation and Apoptotic Indexes in a Murine Model of Fibrosarcoma: Comparison of Four Methods for MRI Region of Interest Positioning

BACKGROUND

Diffusion-weighted imaging (DWI) has demonstrated great potential in predicting the expression of tumor cell proliferation and apoptosis indexes.

PURPOSE

To evaluate the impact of four region of interest (ROI) methods on interobserver variability and apparent diffusion coefficient (ADC) values and to examine the correlation of ADC values with Ki-67, Bcl-2, and P53 labeling indexes (LIs) in a murine model of fibrosarcoma.

STUDY TYPE

Prospective, animal model.

ANIMAL MODEL

A total of 22 female BALB/c mice bearing intramuscular fibrosarcoma xenografts.

FIELD STRENGTH/SEQUENCE

A 3.0 T/T1-weighted fast spin-echo (FSE), T2-weighted fast relaxation fast spin-echo, and DWI PROPELLER FSE sequences.

ASSESSMENT

Four radiologists measured ADC values using four ROI methods (oval, freehand, small-sample, and whole-volume). Immunohistochemical assessment of Ki-67, Bcl-2, and P53 LIs was performed.

STATISTICAL TESTS

Interclass correlation coefficient (ICC), one-way analysis of variance followed by LSD-t post hoc analysis, and Pearson correlation test were performed. The statistical threshold was defined as a P-value of <0.05.

RESULTS

All ROI methods for ADC measurements showed excellent interobserver agreement (ICC range, 0.832-0.986). The ADC values demonstrated significant differences among the four ROI methods. The ADC values for oval, freehand, small-sample, and whole-volume ROI methods showed a moderately negative correlation with Ki-67 (r = -0.623; r = -0.629; r = -0.642, and r = -0.431) and Bcl-2 (r = -0.590; r = -0.597; r = -0.659, and r = -0.425) LIs, but no correlation with P53 LI (r = 0.364, P = 0.104; r = 0.350, P = 0.120; r = 0.379, P = 0.091; r = 0.390, P = 0.080).

DATA CONCLUSION

The ADC value can be used to evaluate cell proliferation and apoptosis indexes in a murine model of fibrosarcoma, employing the small-sample ROI as a reliable method.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 3.