Histogram analysis of ADC in rectal cancer: associations with different histopathological findings including expression of EGFR, Hif1-alpha, VEGF, p53, PD1, and KI 67. A preliminary study

Association between programmed cell death ligand-1 expression in patients with cervical cancer and apparent diffusion coefficient values: a promising tool for patient´s immunotherapy selection

OBJECTIVE

To investigate the associations between apparent diffusion coefficient (ADC) values extracted from three different region of interest (ROI) position approaches and programmed cell death ligand-1 (PD-L1) expression, and evaluate the performance of the nomogram established based on ADC values and clinicopathological parameters in predicting PD-L1 expression in cervical cancer (CC) patients.

METHODS

Through retrospective recruitment, a training cohort of 683 CC patients was created, and a validation cohort of 332 CC patients was prospectively recruited. ROIs were delineated using three different methods to measure the mean ADC (ADCmean), single-section ADC (ADCss), and the minimum ADC of tumors (ADCmin). Logistic regression was employed to identify independent factors related to PD-L1 expression. A nomogram was drawn based on ADC values combined with clinicopathological features, its discrimination and calibration performances were estimated using the area under the curve (AUC) of receiver operating characteristic and calibration curve. The clinical benefits were evaluated by decision curve analysis.

RESULTS

The ADCmin independently correlated with PD-L1 expression. The nomogram constructed with ADCmin and other independent clinicopathological-related factors: FIGO staging, pathological grade, parametrial invasion, and lymph node status demonstrated excellent diagnostic performance (AUC = 0.912 and 0.903, respectively), good calibration capacities, and greater net benefits compared to the clinicopathological model in both the training and validation cohorts.

CONCLUSION

ADCmin independently correlated PD-L1 expression, and the nomogram established with ADCmin and clinicopathological independent prognostic factors had a strong predictive performance for PD-L1 expression, thereby serving as a promising tool for selecting cases eligible for immunotherapy.

CLINICAL RELEVANCE STATEMENT

The minimum ADC can serve as a reliable imaging biomarker related to PD-L1 expression; the established nomogram combines the minimum ADC and clinicopathological factors that can assist clinical immunotherapy decisions.

The Role of Predictive and Prognostic MRI-Based Biomarkers in the Era of Total Neoadjuvant Treatment in Rectal Cancer

The role of magnetic resonance imaging (MRI) in rectal cancer management has significantly increased over the last decade, in line with more personalized treatment approaches. Total neoadjuvant treatment (TNT) plays a pivotal role in the shift from traditional surgical approach to non-surgical approaches such as 'watch-and-wait'. MRI plays a central role in this evolving landscape, providing essential morphological and functional data that support clinical decision-making. Key MRI-based biomarkers, including circumferential resection margin (CRM), extramural venous invasion (EMVI), tumour deposits, diffusion-weighted imaging (DWI), and MRI tumour regression grade (mrTRG), have proven valuable for staging, response assessment, and patient prognosis. Functional imaging techniques, such as dynamic contrast-enhanced MRI (DCE-MRI), alongside emerging biomarkers derived from radiomics and artificial intelligence (AI) have the potential to transform rectal cancer management offering data that enhance T and N staging, histopathological characterization, prediction of treatment response, recurrence detection, and identification of genomic features. This review outlines validated morphological and functional MRI-derived biomarkers with both prognostic and predictive significance, while also exploring the potential of radiomics and artificial intelligence in rectal cancer management. Furthermore, we discuss the role of rectal MRI in the 'watch-and-wait' approach, highlighting important practical aspects in selecting patients for non-surgical management.

Relationships between intravoxel incoherent motion parameters and expressions of programmed cell death-1 (PD-1) and programmed cell death ligand-1 (PD-L1) in patients with cervical cancer

AIM

To determine the associations of intravoxel incoherent motion (IVIM) parameters with expression of programmed cell death-1 (PD-1) and programmed cell death ligand-1 (PD-L1), and evaluate the performance of the combined model established based on IVIM and clinicopathological parameters in predicting PD-L1and PD-1 status of cervical cancer (CC) patients.

MATERIALS AND METHODS

Seventy-eight consecutive CC patients were enrolled prospectively and underwent magnetic resonance imaging (MRI) including IVIM. IVIM quantitative parameters were measured, compared, and correlated with PD-L1 and PD-1 expression. Independent factors related to PD-L1 and PD-1 positivity were identified and were used to establish the combined model. The combined model's diagnostic performance was evaluated using the receiver operating characteristic (ROC) analysis. The Shapley additive explanation (SHAP) algorithm was used to explain the contribution of each parameter in the combined model.

RESULTS

The real diffusion coefficient (D) value was significantly lower in the PD-L1-positive group than in the PD-L1-negative group (0.64 ± 0.12 versus 0.72 ± 0.11, p=0.021). The PD-1-positive and PD-1-negative groups showed similar trends (0.63 ± 0.13 versus 0.73 ± 0.09, p=0.003). Parametrial invasion, lymph node status, pathological grade, FIGO (International Federation of Gynecology and Obstetrics) staging, and D values were independently associated with PD-L1 and PD-1expression. A combined model incorporating these parameters showed good discrimination with the sensitivity, specificity of 90.9%, 82.6% for PD-L1, and 93.5%, 72% for PD-1. According to the SHAP value, FIGO staging and pathological grade were the most influential features of the prediction model.

CONCLUSION

IVIM parameters were found to correlate with PD-L1 and PD-1 expression. The combined model, incorporating parametrial invasion, lymph node status, pathological grade, FIGO staging, and D values, showed good discrimination in predicting PD-L1 and PD-1 status, providing the basis for CC immunotherapy.

A correlative study between IVIM-DWI parameters and VEGF and MMPs expression in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is the fourth most common cause of cancer-related death worldwide. Angiogenic factors may be valuable indices of tumor recurrence and treatment and potentially useful markers for predicting the response to antiangiogenesis therapy. Vascular endothelial growth factor (VEGF) and matrix metalloproteinases (MMPs) are major drivers of tumor angiogenesis. Preoperatively predicting the expression of VEGF and MMPs is crucial for treating HCC. Intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) has been successfully used in the differential diagnosis of HCC, pathological grading, and treatment response evaluation. However, the correlations between IVIM-DWI parameters and VEGF and MMP expression have not been reported. This study provides a preliminary analysis of the correlation between IVIM-DWI parameters and the expression of VEGF, MMP-2, and MMP-9 to investigate the value of IVIM-DWI in the noninvasive evaluation of angiogenesis in HCC.

Methods

IVIM-DWI was performed in 61 patients with HCC 1 week before they underwent surgical resection. VEGF, MMP-2, and MMP-9 expression was detected using immunohistochemistry staining. Spearman correlation analysis was used to analyze the correlations between the IVIM-DWI parameters and VEGF, MMP-2, and MMP-9 expression in HCC.

Results

The fast apparent diffusion coefficient fraction (f) value was positively correlated with the expression of VEGF (P<0.001), MMP-2 (P=0.002), and MMP-9 (P<0.001). The fast apparent diffusion coefficient (D*) was positively correlated with VEGF (P<0.001) and MMP-9 (P<0.001) expression but was not correlated with MMP-2 (P=0.659) expression. The apparent diffusion coefficient (ADC) and slow apparent diffusion coefficient (D) values were not significantly correlated with the expression of VEGF (P=0.103 and P=0.543, respectively), MMP-2 (P=0.596 and P=0.338, respectively), or MMP-9 (P=0.102 and P=0.660, respectively).

Conclusions

IVIM-DWI can be used to noninvasively evaluate angiogenesis in HCC.

Associations Between ADC and Tumor Infiltrating Lymphocytes, Tumor-Stroma Ratio and Vimentin Expression in Head and Neck Squamous Cell Cancer

RATIONALE AND OBJECTIVES

The present study used diffusion-weighted imaging (DWI) to elucidate possible associations with tumor-infiltrating lymphocytes (TIL), tumor-stroma ratio and vimentin expression in head and neck squamous cell cancer (HNSCC).

MATERIALS AND METHODS

30 patients with primary HNSCC of different localizations were involved in the study. DWI was obtained on a 3 T scanner. Apparent diffusion coefficients (ADC) images were analyzed with a whole lesion measurement using a histogram approach. TIL- and vimentin-expression was calculated on biopsy samples before any form of treatment.

RESULTS

Tumor-stroma ratio correlated with ADC kurtosis (r = 0.46, p = 0.01) and ADC skewness (r = 0.42, p = 0.02). Several ADC parameters were significantly different between stroma rich und tumor rich tumors. ADC entropy correlated significantly with the expression of TIL within the tumor compartment (r = 0.44, p = 0.01). No associations were identified between ADC parameters and vimentin expression.

CONCLUSION

ADC skewness and kurtosis histogram parameters can reflect tumor compartments in HNSCC. ADC entropy was associated with TIL of the tumor compartment but not with those of the stroma compartment, which emphasizes the ability of ADC histogram parameters to reflect distinctive differences of tumors.

Pretreatment Apparent Diffusion Coefficient Cannot Predict Histopathological Features and Response to Neoadjuvant Radiochemotherapy in Rectal Cancer: A Meta-Analysis

AIM

Our purpose was to perform a systemic literature review and meta-analysis regarding use of apparent diffusion coefficient (ADC) for prediction of histopathological features in rectal cancer (RC) and to prove if ADC can predict treatment response to neoadjuvant radiochemotherapy (NARC) in RC.

METHODS

MEDLINE library, EMBASE, Cochrane, and SCOPUS database were screened for associations between ADC and histopathology and/or treatment response in RC up to June 2020. Authors, year of publication, study design, number of patients, mean value, and standard deviation of ADC were acquired. The methodological quality of the collected studies was checked according to the Quality Assessment of Diagnostic Studies instrument. The meta-analysis was undertaken by using the RevMan 5.3 software. DerSimonian and Laird random-effects models with inverse-variance weights were used to account the heterogeneity between the studies. Mean ADC values including 95% confidence intervals were calculated.

RESULTS

Overall, 37 items (2,015 patients) were included. ADC values of tumors with different T and N stages and grades overlapped strongly. ADC cannot distinguish RC with a high- and low-carcinoembryonic antigen level. Regarding KRAS status, ADC cannot discriminate mutated and wild-type RC. ADC did not correlate significantly with expression of vascular endothelial growth factor and hypoxia-inducible factor 1a. ADC correlates with Ki 67, with the calculated correlation coefficient: -0.52. The ADC values in responders and nonresponders overlapped significantly.

CONCLUSION

ADC correlates moderately with expression of Ki 67 in RC. ADC cannot discriminate tumor stages, grades, and KRAS status in RC. ADC cannot predict therapy response to NARC in RC.

Grading of meningioma tumors based on analyzing tumor volumetric histograms obtained from conventional MRI and apparent diffusion coefficient images

Background

Our purpose was to evaluate the application of volumetric histogram parameters obtained from conventional MRI and apparent diffusion coefficient (ADC) images for grading the meningioma tumors.

Results

Tumor volumetric histograms of preoperative MRI images from 45 patients with the diagnosis of meningioma at different grades were analyzed to find the histogram parameters. Kruskal-Wallis statistical test was used for comparison between the parameters obtained from different grades. Multi-parametric regression analysis was used to find the model and parameters with high predictive value for the classification of meningioma. Mode; standard deviation on post-contrast T1WI, T2-FLAIR, and ADC images; kurtosis on post-contrast T1WI and T2-FLAIR images; mean and several percentile values on ADC; and post-contrast T1WI images showed significant differences among different tumor grades (P < 0.05). The multi-parametric linear regression showed that the ADC histogram parameters model had a higher predictive value, with cutoff values of 0.212 (sensitivity = 79.6%, specificity = 84.3%) and 0.180 (sensitivity = 70.9%, specificity = 80.8%) for differentiating the grade I from II, and grade II from III, respectively.

Conclusions

The multi-parametric model of volumetric histogram parameters in some of the conventional MRI series (i.e., post-contrast T1WI and T2-FLAIR images) along with the ADC images are appropriate for predicting the meningioma tumors’ grade.

Differentiation of brain metastases originating from lung and breast cancers using apparent diffusion coefficient histogram analysis and the relation of histogram parameters with Ki-67

PURPOSE

A fast, reliable and non-invasive method is required in differentiating brain metastases (BMs) originating from lung cancer (LC) and breast cancer (BC). The aims of this study were to assess the role of histogram analysis of apparent diffusion coefficient (ADC) maps in differentiating BMs originated from LC and BC, and then to investigate further the association of ADC histogram parameters with Ki-67 index in BMs.

METHODS

A total of 55 patients (LC, N = 40; BC, N = 15) with BMs histopathologically confirmed were enrolled in the study. The LC group was divided into small-cell lung cancer (SCLC; N = 15) and non-small-cell lung cancer (NSCLC; N = 25) groups. ADC histogram parameters (ADC_max, ADC_mean, ADC_min, ADC_median, ADC₁₀, ADC₂₅, ADC₇₅ and ADC₉₀, skewness, kurtosis and entropy) were derived from ADC maps. Mann-Whitney U-test, independent samples t-test, receiver operating characteristic (ROC) analysis and Spearman correlation analysis were used for statistical assessment.

RESULTS

ADC histogram parameters did not show significant differences between LC and BC groups (p > 0.05). Subgroup analysis showed that various ADC histogram parameters were found to be statistically lower in the SCLC group compared to the NSCLC and BC groups (p < 0.05). ROC analysis showed that ADC_mean and ADC₁₀ for differentiating SCLC BMs from NSCLC, and ADC₂₅ for differentiating SCLC BMs from BC achieved optimal diagnostic performances. Various histogram parameters were found to be significantly correlated with Ki-67 (p < 0.05).

CONCLUSION

Histogram analysis of ADC maps may reflect tumoural proliferation potential in BMs and can be useful in differentiating SCLC BMs from NSCLC and BC BMs.

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.

Association of apparent diffusion coefficient with Ki-67 proliferation index, progesterone-receptor status and various histopathological parameters, and its utility in predicting the high grade in meningiomas

BACKGROUND

Accurate preoperative determination of the histological grade and cellular proliferative potential of meningioma by non-invasive imaging is of paramount importance.

PURPOSE

To evaluate the utility of apparent diffusion coefficient (ADC) in determining the histological grade of meningioma, and to investigate the correlation of ADC with Ki-67 proliferation index (PI), progesterone receptor (PR) status, and a number of other histopathological parameters.

MATERIAL AND METHODS

Histopathologically confirmed 94 meningioma patients (72 low-grade, 22 high-grade) who had undergone preoperative diffusion-weighted imaging were retrospectively evaluated. ADC values were obtained by manually drawing the regions of interest (ROIs) within the solid components of the tumor. The relationship between ADC and Ki-67 values, PR status, and multiple histopathological parameters were investigated, and the ADC values of high-grade and low-grade meningiomas were compared. Independent sample t-test, Mann-Whitney U test, receiver operating characteristic, Pearson correlation, and multiple logistic regression analysis were used for statistical assessment.

RESULTS

All ADC and rADC values were significantly lower in high-grade meningiomas than in low-grade meningiomas (all P < 0.05). ADC values showed significantly negative correlations with Ki-67 and mitotic index (P < 0.001 for each). Numerous ADC parameters were significantly lower in meningiomas demonstrating hypercellularity and necrosis features (P < 0.05). ADC values did not show a significant correlation with PR score (all P > 0.05).

CONCLUSION

ADC can be utilized as a reliable imaging biomarker for predicting the proliferative potential and histological grade in meningiomas.

Relationships between apparent diffusion coefficient (ADC) histogram analysis parameters and PD-L 1-expression in head and neck squamous cell carcinomas: a preliminary study

BACKGROUND

Immunotherapy has become a cornerstone of the modern cancer treatment. It might be crucial to predict its expression non-invasively by imaging. The present study used diffusion-weighted imaging (DWI) quantified by whole lesion apparent diffusion coefficient (ADC) values to elucidate possible associations with programmed cell death ligand 1(PD-L1) expression in head and neck squamous cell cancer (HNSCC).

PATIENTS AND METHODS

Overall, 29 patients with primary HNSCC of different localizations were involved in the study. DWI was obtained by using a sequence with b _- values of 0 and 800 s/mm² on a 3 T MRI. ADC values were evaluated with a whole lesion measurement and a histogram approach. PD-L1 expression was estimated on bioptic samples before any form of treatment using 3 scores, tumor positive score (TPS), immune cell score (ICS), and combined positive score (CPS).

RESULTS

An inverse correlation between skewness derived from ADC values and ICS was identified (r = -0.38, p = 0.04). ADC_max tended to correlate with ICS (r = -0.35, p = 0.06). Other ADC parameters did not show any association with the calculated scores.

CONCLUSIONS

There is a weak association between skewness derived from ADC values and PD-L1 expression in HNSCC, which might not be strong enough to predict PD-L1 expression in clinical routine. Presumably, ADC values are more influenced by complex histopathology compartments, comprising cellular and extracellular aspects of tumors than only of a single subset of tumor associated cells.

Associations between IVIM histogram parameters and histopathology in rectal cancer

PURPOSE

Histogram analysis can better reflect tumor heterogeneity than conventional imaging analysis. The present study analyzed possible correlations between histogram analysis parameters derived from Intravoxel-incoherent imaging (IVIM) and histopathological features in rectal cancer (RC).

METHODS

Seventeen patients with histopathologically proven rectal adenocarcinomas were retrospectively acquired. In all cases, pelvic MRI was performed. Diffusion weighted imaging was obtained using a multi-slice single-shot echo-planar imaging sequence with b values of 0, 50, 200, 500 and 1000 s/mm². Simplified IVIM analysis was performed using the IntelliSpace portal, version 10 and the following images were generated: f (perfusion fraction) map, D (true diffusion coefficient) map, and ADC map utilizing all b-values. Histogram based analysis of signal intensities was performed for every IVIM map using an in-house matlab tool. Histopathology was investigated using Ki 67 specimens with calculation of Ki 67-index and cellularity. CD31 stained specimens were used for calculation of microvessel density (MVD).

RESULTS

There were statistically significant correlations between Ki 67 index and mode derived from ADC as well as entropy from f, r=-0.50, p=.04 and r=-0.55, p=.02, respectively. MVD correlated well with parameters derived from f.

CONCLUSION

IVIM histogram analysis parameters can reflect histopathology in RC. ADC and D values are associated with proliferation potential. Perfusion fraction f is associated with MVD.

Associations between magnetic resonance imaging and EMG findings in myopathies

OBJECTIVES

Magnetic resonance imaging (MRI) is a cornerstone in diagnosis of myopathies. The present study sought to elucidate possible associations between electromyography (EMG) findings and histogram parameters derived from clinical MRI in myositis and other myopathies.

MATERIALS AND METHODS

Twenty six patients with myopathies were included in this retrospective study. Clinical MRI was performed with a 1.5T MRI scanner including T2- and T1-weighted images. EMG analysis was performed during clinical diagnostic workup. The histogram parameters of the MRI sequences were obtained of the same muscle, which was investigated with EMG.

RESULTS

Several correlations were identified between mean duration of the motor unit potentials (MUP) and histogram parameters derived from T1- and T2-weighted images. The highest for T1-weighted images was mode (r = -.73, P < .0001) and for T2-weighted images was p25 (r = -.57, P = .022). There were significant differences for several histogram parameters between muscles with pathological spontaneous activity and without. So, for T1-weighted images, the best discrimination was achieved with mean (P = .096), and for T2-weighted images for p10 (P = .05). Mean SI values derived from T1-weighted images achieved an AUC of 0.84 with a sensitivity of 0.81 and a specificity of 0.86 to discriminate patients with and without pathological spontaneous activity (PSA).

CONCLUSIONS

The present study identified strong associations between histogram analysis derived from morphological MRI sequences and the duration of the MUP derived from EMG in myopathies strengthening the fact that both diagnostic modalities can reflect disease state in a similar fashion. Histogram parameters can predict muscles with PSA.

Associations between apparent diffusion coefficient values and histopathological tissue alterations in myopathies

OBJECTIVES

Diffusion-weighted imaging (DWI) can reflect histopathologic changes in muscle disorders. The present study sought to elucidate possible associations between histopathology derived from muscle biopsies and DWI in myositis and other myopathies.

METHODS

Nineteen patients (10 women, 52.6%) with a mean age 51.43 ± 19 years were included in this retrospective study. Apparent diffusion coefficients (ADC) were evaluated with a histogram approach of the biopsied muscle. The histopathology analysis included the scoring systems proposed by Tateyama et al., Fanin et al., Allenbach et al. and immunhistochemical stainings for MHC, CD68, CD8, and CD4.

RESULTS

There was a tendency that skewness was lowered with increasing Tateyama score, but it did not reach statistical significance (p = .14). No statistical differences for the other scores were identified. There was a tendency that kurtosis was higher in MHC negative stained patient compared to positive patients, but statistically significance was not reached (p = .07). ADC histogram parameters did not correlate with CD68 and CD8 positive stained cells. There was a trend for skewness to correlate with the amount of CD4-positive cells (r = .57, p = .07).

CONCLUSION

The present study could not identify statistical significant associations between DWI and histopathology in muscle diseases based upon a small patient sample. Presumably, the investigated histopathology scores are more specific for certain disease aspects, whereas ADC values reflect the whole cellularity of the investigated muscle, which might cause the negative results.

MRI prediction of pathological response in locally advanced rectal cancer: when apparent diffusion coefficient radiomics meets conventional volumetry

AIM

To investigate the role of diffusion-weighted imaging (DWI), T2-weighted (W) imaging, and apparent diffusion coefficient (ADC) histogram analysis before, during, and after neoadjuvant chemoradiotherapy (CRT) in the prediction of pathological response in patients with locally advanced rectal cancer (LARC).

MATERIALS AND METHODS

Magnetic resonance imaging (MRI) at 1.5 T was performed in 43 patients with LARC before, during, and after CRT. Tumour volume was measured on both T2-weighted (V_T2W) and on DWI at b=1,000 images (V_b,1,000) at each time point, hence the tumour volume reduction rate (ΔV_T2W and ΔV_b,1,000) was calculated. Whole-lesion (three-dimensional [3D]) first-order texture analysis of the ADC map was performed. Imaging parameters were compared to the pathological tumour regression grade (TRG). The diagnostic performance of each parameter in the identification of complete responders (CR; TRG4), partial responders (PR; TRG3) and non-responders (NR; TRG0-2) was evaluated by multinomial regression analysis and receiver operating characteristics curves.

RESULTS

After surgery, 11 patients were CR, 22 PR, and 10 NR. Before CRT, predictions of CR resulted in an ADC value of the 75th percentile and median, with good accuracy (74% and 86%, respectively) and sensitivity (73% and 82%, respectively). During CRT, the best predictor of CR was ΔV_T2W (-58.3%) with good accuracy (81%) and excellent sensitivity (91%). After CRT, the best predictors of CR were ΔV_T2W (-82.8%) and ΔV_{b, 1,000} (-86.8%), with 84% accuracy in both cases and 82% and 91% sensitivity, respectively.

CONCLUSIONS

The median ADC value at pre-treatment MRI and ΔV_T2W (from pre-to-during CRT MRI) may have a role in early and accurate prediction of response to treatment. Both ΔV_T2W and ΔV_b,1,000 (from pre-to-post CRT) can help in the identification of CR after CRT.

Associations between histogram analysis parameters derived from dynamic-contrast enhanced MRI and PD L1-expression in head and neck squamous cell carcinomas. A preliminary study

PURPOSE

Programmed death 1 ligand (PD-L 1) plays an essential role in oncology. It might be crucial to predict its expression non-invasively by imaging. Dynamic-contrast enhanced MRI (DCE MRI) is one of the important imaging modalities in head and neck squamous cell cancer (HNSCC). The aim of the present study was to analyze possible associations between histogram analysis parameters of DCE MRI and PD-L 1 expression in HNSCC METHODS: Overall, 26 patients with primary HNSCC of different localizations were involved in the study. DCE MRI was obtained on a 3 T MRI and analyzed with a whole lesion measurement using a histogram approach. PD-L 1 expression was estimated on bioptic samples before any form of treatment using 3 scores (Tumor positive score (TPS), Immune cell score (ICS) and Combined positive score (CPS)).

RESULTS

CPS correlated with mode derived from Ktrans (r = 0.40, p = .04). Also CPS correlated with P90 derived from Kep (r = 0.40, p = .04). ICS correlated with the maximum derived from Kep (r = 0.41, p = .03) and entropy derived from Kep (r = 0.43, p = .02). There were no associations between DCE MRI parameters and TPS.

CONCLUSION

Ktrans and Kep related histogram analysis parameters derived from DCE MRI correlated moderately with PD-L 1 expression of immune cells in HNSCC.

Value of intravoxel incoherent motion magnetic resonance imaging for differentiating metastatic from nonmetastatic mesorectal lymph nodes with different short-axis diameters in rectal cancer

Background

Conventional magnetic resonance imaging (MRI) does not accurately evaluate lymph node (LN) status, which is essential for the treatment and prognosis assessment in patients with rectal cancer.

Objective

The aim of this study is to evaluate the diagnostic value of intravoxel incoherent motion (IVIM) MRI in differentiating metastatic and nonmetastatic mesorectal LNs with different short-axis diameters in rectal cancer patients.

Materials and Methods

Forty patients (154 LNs) were divided into three groups based on short-axis diameter: 3 mm ≤ × ≤5 mm, 5 mm < × ≤7 mm, and × >7 mm. MRI characteristics and IVIM parameters were compared between the metastatic and nonmetastatic LNs to determine the diagnostic value for discriminating them.

Results

In the 3 mm ≤ × ≤ 5 mm group, mean D values were significantly lower in metastatic than in the nonmetastatic LNs (P < 0.001). In the 5 mm < × ≤7 mm group, mean f values were significantly lower in metastatic than nonmetastatic LNs (P < 0.05). In the × >7 mm group, only the short-axis diameter of metastatic LNs was significantly greater than that of nonmetastatic LNs (P < 0.05). The area under the curve, sensitivity, specificity, and cutoff values were used for differentiating the metastatic from the nonmetastatic LNs.

Conclusion

IVIM parameters can differentiate metastatic from nonmetastatic LNs with smaller short-axis diameters (× ≤7 mm) in rectal cancer, and the short-axis diameter is a significant factor in identifying metastatic and nonmetastatic LNs in larger short-axis diameter groups (× >7 mm).

Comparative Analysis of Amide Proton Transfer MRI and Diffusion-Weighted Imaging in Assessing p53 and Ki-67 Expression of Rectal Adenocarcinoma

BACKGROUND

The evaluation of prognostic factors in rectal carcinoma patients has important clinical significance. P53 status and the Ki-67 index have served as prognostic factors in rectal carcinoma. Amide proton transfer (APT) imaging has shown great potential in tumor diagnosis. However, few studies reported the value of APT imaging in evaluating p53 and Ki-67 status of rectal carcinoma.

PURPOSE

To investigate the feasibility of amide proton transfer MRI in assessing p53 and Ki-67 expression of rectal adenocarcinoma, and compare it with conventional diffusion-weighted imaging (DWI).

STUDY TYPE

Retrospective.

POPULATION

Forty-three patients with rectal adenocarcinoma (age: 34-85 years).

FIELD STRENGTH/SEQUENCE

3T/APT imaging using a 3D turbo spin echo (TSE)-Dixon pulse sequence with chemical shift-selective fat suppression, 2D DWI, and 2D T₂ -weighted TSE.

ASSESSMENT

Mean tumor APT signal intensity (SI_mean ) and apparent diffusion coefficient (ADC_mean ) were measured. Traditional tumor pathological analysis included WHO grades, pT (pathologic tumor) stages, and pN (pathologic node) stages. Expression levels of p53 and Ki-67 were determined by immunohistochemical assay.

STATISTICAL TESTS

One-way analysis of variance (ANOVA); Student's t-test; Spearman's correlation coefficient; receiver operating characteristic (ROC) curve analysis.

RESULTS

High-grade tumors, more advanced stage tumors, and tumors with lymph node involvement had higher APT SI_mean values: high grade (n = 15) vs. low-grade (n = 28), P < 0.001; pT2 (n = 10) vs. pT3 (n = 20) vs. pT4 (N = 13), P = 0.021; pN0 (n = 24) vs. pN1-2 (n = 19), P = 0.019. ADC_mean differences were found in tumors with different pT stage: pT2 (n = 10) vs. pT3 (n = 20) vs. pT4 (N = 13), P = 0.013, but not in tumors with different histologic grade: high grade (n = 15) vs. low-grade (n = 28), P = 0.3536; or pN stage: pN0 (n = 24) vs. pN1-2 (n = 19), P = 0.624. Tumor with p53 positive status had higher APT SI_mean than tumor with negative p53 status (2.363 ± 0.457 vs. 2.0150 ± 0.3552, P = 0.014). There was no difference in ADC_mean with p53 status (1.058 ± 0.1163 10^-3  mm² /s vs. 1.055 ± 0.128 10^-3  mm² /s, P = 0.935). APT SI_mean and ADC_mean were significantly different in tumors with low and high Ki-67 status (1.7882 ± 0.11386 vs. 2.3975 ± 0.41586, P < 0.001; 1.1741 ± 0.093 10^-3  mm² /s vs. 1.0157 ± 0.10459 10^-3  mm² /s, P < 0.001, respectively). APT SI_mean exhibited a positive correlation with p53 labeling index and Ki-67 labeling index (r = 0.3741, P = 0.0135; r = 0.7048; P < 0.001, respectively). ADC_mean showed no correlation with p53 labeling index, but a negative correlation with Ki-67 labeling index (r = -0.5543, P < 0.0001). ROC curves demonstrated that APT SI_mean had significantly higher diagnostic ability for differentiation of high Ki-67 expression of rectal adenocarcinoma than ADC_mean (81.2% vs. 78.12%, 90.91% vs. 63.64; P < 0.001 vs. P = 0.017), while no difference was found in predicting p53 status (92.86% vs. 71.4%, 53.33% vs. 66.7%; P < 0.001 vs. P = 0.0471).

DATA CONCLUSION

APT SI_mean was related to p53 and Ki-67 expression levels in rectal adenocarcinoma. APT imaging may serve as a noninvasive biomarker for assessing genetic prognostic factors of rectal adenocarcinoma.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

Apparent Diffusion Coefficient Histogram Analysis for Assessing Tumor Staging and Detection of Lymph Node Metastasis in Epithelial Ovarian Cancer: Correlation with p53 and Ki-67 Expression

PURPOSE

To investigate the potential of apparent diffusion coefficient (ADC) histogram parameters in epithelial ovarian cancer (EOC) for distinguishing different tumor stages and determining lymph node status and correlations between ADC values and p53 and Ki-67 expression.

PROCEDURES

Forty-nine EOC patients underwent preoperative magnetic resonance imaging. Staging and lymph node status were determined postoperatively. ADC values were measured using histogram analysis and compared between groups. Relationships between ADCs and Ki-67 and p53 expression were explored.

RESULTS

DC parameters differed significantly between stage I vs II, I vs III, and I vs IV. The parameters were significantly lower in the lymph node-positive group than in the lymph node-negative group, were significantly negatively correlated with Ki-67 labeling index, and were all significantly lower in the mutation-type p53 group than in the wild-type p53 group.

CONCLUSIONS

ADC histogram analysis can help discriminate stage I from advanced-stage EOC and predict lymph node metastasis. ADC parameters were correlated with Ki-67 labeling index; the parameters may help indicate p53 expression.

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.

Apparent diffusion coefficient cannot predict molecular subtype and lymph node metastases in invasive breast cancer: a multicenter analysis

Background

Radiological imaging plays a central role in the diagnosis of breast cancer (BC). Some studies suggest MRI techniques like diffusion weighted imaging (DWI) may provide further prognostic value by discriminating between tumors with different biologic characteristics including receptor status and molecular subtype. However, there is much contradictory reported data regarding such associations in the literature. The purpose of the present study was to provide evident data regarding relationships between quantitative apparent diffusion coefficient (ADC) values on DWI and pathologic prognostic factors in BC.

Methods

Data from 5 centers (661 female patients, mean age, 51.4 ± 10.5 years) were acquired. Invasive ductal carcinoma (IDC) was diagnosed in 625 patients (94.6%) and invasive lobular carcinoma in 36 cases (5.4%). Luminal A carcinomas were diagnosed in 177 patients (28.0%), luminal B carcinomas in 279 patients (44.1%), HER 2+ carcinomas in 66 cases (10.4%), and triple negative carcinomas in 111 patients (17.5%). The identified lesions were staged as T1 in 51.3%, T2 in 43.0%, T3 in 4.2%, and as T4 in 1.5% of the cases. N0 was found in 61.3%, N1 in 33.1%, N2 in 2.9%, and N3 in 2.7%. ADC values between different groups were compared using the Mann–Whitney U test and by the Kruskal-Wallis H test. The association between ADC and Ki 67 values was calculated by Spearman’s rank correlation coefficient.

Results

ADC values of different tumor subtypes overlapped significantly. Luminal B carcinomas had statistically significant lower ADC values compared with luminal A (p = 0.003) and HER 2+ (p = 0.007) lesions. No significant differences of ADC values were observed between luminal A, HER 2+ and triple negative tumors. There were no statistically significant differences of ADC values between different T or N stages of the tumors. Weak statistically significant correlation between ADC and Ki 67 was observed in luminal B carcinoma (r = − 0.130, p = 0.03). In luminal A, HER 2+ and triple negative tumors there were no significant correlations between ADC and Ki 67.

Conclusion

ADC was not able to discriminate molecular subtypes of BC, and cannot be used as a surrogate marker for disease stage or proliferation activity.

Association Between VEGF Expression and Diffusion Weighted Imaging in Several Tumors-A Systematic Review and Meta-Analysis

To date, only a few studies have investigated relationships between Diffusion-weighted imaging (DWI) and Vascular endothelial growth factor (VEGF) expression in tumors. The reported results are contradictory. The aim of the present analysis was to review the published results and to perform a meta-analysis regarding associations between apparent diffusion coefficients (ADC) derived from DWI and VEGF expression. MEDLINE library was screened for relationships between ADC and VEGF expression up to January 2019. Overall, 14 studies with 578 patients were identified. In 10 studies (71.4%) 3 T scanners were used and in four studies (28.6%) 1.5 T scanners. Furthermore, seven studies (50%) had a prospective design and seven studies (50%) had a retrospective design. Most frequently, prostate cancer, followed by rectal cancer, cervical cancer and esophageal cancer were identified. The pooled correlation coefficient of all tumors was r = -0.02 [95% CI -0.26-0.21]. ADC values derived from routinely acquired DWI do not correlate with VEGF expression in various tumors. Therefore, DWI is not sensitive enough to reflect angiogenesis-related microstructure of tumors.

Radiogenomics of breast cancer using dynamic contrast enhanced MRI and gene expression profiling

Background

Imaging techniques can provide information about the tumor non-invasively and have been shown to provide information about the underlying genetic makeup. Correlating image-based phenotypes (radiomics) with genomic analyses is an emerging area of research commonly referred to as “radiogenomics” or “imaging-genomics”. The purpose of this study was to assess the potential for using an automated, quantitative radiomics platform on magnetic resonance (MR) breast imaging for inferring underlying activity of clinically relevant gene pathways derived from RNA sequencing of invasive breast cancers prior to therapy.

Methods

We performed quantitative radiomic analysis on 47 invasive breast cancers based on dynamic contrast enhanced 3 Tesla MR images acquired before surgery and obtained gene expression data by performing total RNA sequencing on corresponding fresh frozen tissue samples. We used gene set enrichment analysis to identify significant associations between the 186 gene pathways and the 38 image-based features that have previously been validated.

Results

All radiomic size features were positively associated with multiple replication and proliferation pathways and were negatively associated with the apoptosis pathway. Gene pathways related to immune system regulation and extracellular signaling had the highest number of significant radiomic feature associations, with an average of 18.9 and 16 features per pathway, respectively. Tumors with upregulation of immune signaling pathways such as T-cell receptor signaling and chemokine signaling as well as extracellular signaling pathways such as cell adhesion molecule and cytokine-cytokine interactions were smaller, more spherical, and had a more heterogeneous texture upon contrast enhancement. Tumors with higher expression levels of JAK/STAT and VEGF pathways had more intratumor heterogeneity in image enhancement texture. Other pathways with robust associations to image-based features include metabolic and catabolic pathways.

Conclusions

We provide further evidence that MR imaging of breast tumors can infer underlying gene expression by using RNA sequencing. Size and shape features were appropriately correlated with proliferative and apoptotic pathways. Given the high number of radiomic feature associations with immune pathways, our results raise the possibility of using MR imaging to distinguish tumors that are more immunologically active, although further studies are necessary to confirm this observation.

Electronic supplementary material

The online version of this article (10.1186/s40644-019-0233-5) contains supplementary material, which is available to authorized users.

Texture analysis parameters derived from T1-and T2-weighted magnetic resonance images can reflect Ki67 index in soft tissue sarcoma

BACKGROUND AND OBJECTIVES

Texture analysis derived from morphological magnetic resonance (MR) images might be associated with histopathology in tumors. The present study sought to elucidate possible associations between texture features derived from T1-and T2-weighted images with proliferation index Ki67 in soft tissue sarcomas.

METHODS

Overall, 29 patients (n = 13, 44.8% female) with a median age of 52 years were included into this retrospective study. Several soft tissue sarcomas were investigated. Texture analysis was performed on pre-contrast T1-weighted and T2-weighted images using the free available Mazda software.

RESULTS

The best correlation coefficients with Ki67 index were identified for the following parameters: T1-weighted images "45dgr_RLNonUni (p = 0.50, P = 0.006), T2-weighted images "S (4,0)SumAverg" (p = -0.45, P = 0.02). A ROC analysis was performed for Ki67-index with a threshold of 10%. The highest area under the curve (AUC) was found for the parameter "T1_WavEnHL_s-7" with an AUC of 0.90. For the threshold of Ki67 = 20% the highest AUC was identified for the parameter "T2_S (1,1)Entropy" with an AUC of 0.77.

CONCLUSION

Several texture features derived from T1-and T2-weighted images correlated with proliferation index Ki67 and might be used as valuable novel biomarkers in soft tissue sarcomas.

Metabolo-volumetric parameters of 18F-FDG-PET can predict expression of EGFR and HIF 1alpha in uterine cervical cancer

BACKGROUND

The aim of our study was to investigate possible relationships between 18F-FDG-PET parameters and clinically relevant histopathological findings in patients with cervical cancer (CC).

METHODS

Eighteen female patients (mean age 55.4 years) with histologically confirmed squamous cell CC were involved into the study. In all cases, 18F-FDG-PET CT was performed. Mean and maximum standardized uptake values (SUVmean and SUVmax), total lesion glycolysis (TLG) and metabolic tumor volume (MTV) were determined on PET-images. For every tumor the following specimen stainings were performed: epidermal growth factor receptor (EGFR), vascular endothelial growth factor (VEGF), tumor suppressor protein p53, hypoxia-inducible factor (HIF)-1α, and histone 3. All stained specimens were digitalized and analyzed by using the ImageJ software 1.48v. Spearman's correlation coefficient (p) was used to analyze associations between investigated parameters. p-values < 0.05 were taken to indicate statistical significance.

RESULTS

TLG and MTV correlated well with expression of EGFR (p= 0.601, P= 0.008 and p= 0.586, P= 0.011, respectively). SUVmedian correlated inversely with expression of HIF 1alpha (p=-0.509, P= 0.031). SUVmean tended to correlate with expression of EGFR and HIF 1alpha. None of the PET parameters correlated with expression of Histone 3, p53 and VEGF.

CONCLUSION

TLG and MTV can reflect expression of EGFR and SUVmedian correlated significantly with expression of HIF-1α. None of the PET parameters can predict expression of Histone 3, p53 and VEGF.

Whole-lesion ADC histogram analysis is not able to reflect microvessel density in HNSCC

Diffusion-weighted imaging (DWI) is a functional imaging technique sensitive to microstructure in tissues. It is widely acknowledged to reflect cellularity in tumors. A small part of DWI is also sensitive to perfusion-related information and might therefore be also be able to reflect microvessel density in tumor tissues. Aim of the present study was to elucidate possible correlations between microvessel density and apparent diffusion coefficient (ADC) values in head and neck squamous cell carcinoma (HNSCC).Thirty-four patients with histologically proven primary HNSCC were included in the study. DWI was performed with a 3 T magnetic resonance imaging (MRI) (b-values 0 and 800 s/mm) and histogram analysis was calculated with a whole lesion measurement. In every case, microvessel density was estimated with CD105-stained specimens.There were no statistically significant correlations between ADC histogram parameters and microvessel density. The calculated correlation coefficients ranged from r = -0.27, P = .13 for entropy and vessel area to r = 0.16, P = .40 for ADCmin and vessel count.Whole-lesion histogram analysis of ADC values cannot reflect microvessel density in HNSCC.

Combined Metabolo-Volumetric Parameters of 18F-FDG-PET and MRI Can Predict Tumor Cellularity, Ki67 Level and Expression of HIF 1alpha in Head and Neck Squamous Cell Carcinoma: A Pilot Study

BACKGROUND: Our purpose was to evaluate associations of combined ¹⁸F-FDG-PET and MRI parameters with histopathological features in head and neck squamous cell carcinoma (HNSCC). METHODS: Overall, 22 patients with HNSCC were acquired (10 with G1/2 tumors and 12 with G3 tumors).¹⁸F-FDG-PET/CT and MRI was performed and maximum standardized uptake value (SUV_max), total lesion glycolysis (TLG) and metabolic tumor volume (MTV) were estimated. Neck MRI was obtained on a 3 T scanner. Diffusion weighted imaging was performed with estimation of apparent diffusion coefficient (ADC). Perfusion parameters K_trans,V_e, and K_ep were derived from dynamic contrast-enhanced (DCE) imaging. Different combined PET/MRI parameters were calculated as ratios: PET parameters divided by ADC or DCE MRI parameters. The following histopathological features were estimated: Ki 67, EGFR, VEGF, p53, hypoxia-inducible factor (HIF)-1α, and cell count. Spearman's correlation coefficient (p) was used for correlation analysis. P < .05 was taken to indicate statistical significance. RESULTS: In overall sample, cellularity correlated with SUV_max/ADC_min (P = .558, P = .007), TLG/ADC_min (P = .546, P = .009), and MTV/ADC_min (P = .468, P = .028). MTV/K_ep correlated with expression of HIF-1α (P = .450, P = 0,047). In G1/2 tumors, SUV_max/ADC_min correlated with HIF-1α (P = −.648, P = .043); MTV/K_ep (P = −.669, P = .034) and TLG/K_ep (P = −.644, P = .044) with Ki67. In G3 tumors, cellularity correlated with SUV_max/ADC_min (P = .832, P = .001), SUV_max/ADC_mean (P = .741, P = .006), and TLG/ADC_min (P = .678, P = .015). MTV/ADC_min and TLG/ADC_min tended to correlate with HIF-1α. CONCLUSION: Combined parameters of ¹⁸F-FDG-PET and MRI can reflect Ki 67, tumor cellularity and expression of HIF-1α in HNSCC. Associations between parameters of ¹⁸F-FDG-PET and MRI and histopathology depend on tumor grading.

Histogram Analysis Parameters Apparent Diffusion Coefficient for Distinguishing High and Low-Grade Meningiomas: A Multicenter Study

Low grade meningiomas have better prognosis than high grade meningiomas. The aim of this study was to measure apparent diffusion coefficient (ADC) histogram analysis parameters in different meningiomas in a large multicenter sample and to analyze the possibility of several parameters for predicting tumor grade and proliferation potential. Overall, 148 meningiomas from 7 institutions were evaluated in this retrospective study. Grade 1 lesions were diagnosed in 101 (68.2%) cases, grade 2 in 41 (27.7%) patients, and grade 3 in 6 (4.1%) patients. All tumors were investigated by MRI (1.5 T scanner) by using diffusion weighted imaging (b values of 0 and 1000 s/mm²). For every lesion, the following parameters were calculated: mean ADC, maximum ADC, minimum ADC, median ADC, mode ADC, ADC percentiles P10, P25, P75, P90, kurtosis, skewness, and entropy. The comparison of ADC values was performed by Mann–Whitney-U test. Correlation between different ADC parameters and KI 67 was calculated by Spearman's rank correlation coefficient. Grade 2/3 meningiomas showed statistically significant lower ADC histogram analysis parameters in comparison to grade 1 tumors, especially ADC median. A threshold value of 0.82 for ADC median to predict tumor grade was estimated (sensitivity = 82.2%, specificity = 63.8%, accuracy = 76.4%, positive and negative predictive values were 83% and 62.5%, respectively).

All ADC parameters except maximum ADC showed weak significant correlations with KI 67, especially ADC P25 (P = −.340, P = .0001).