Correlation between apparent diffusion coefficients and HER2 status in gastric cancers: pilot study

Multiparametric MR characterization for human epithelial growth factor receptor 2 expression in bladder cancer: an exploratory study

PURPOSE

To investigate the application value of multiparametric MRI in evaluating the expression status of human epithelial growth factor receptor 2 (HER2) in bladder cancer (BCa).

METHODS

From April 2021 to July 2023, preoperative imaging manifestations of 90 patients with pathologically confirmed BCa were retrospectively collected and analyzed. All patients underwent multiparametric MRI including synthetic MRI, DWI, from which the T1, T2, proton density (PD) and apparent diffusion coefficient (ADC) values were obtained. The clinical and imaging characteristics as well as quantitative parameters (T1, T2, PD and ADC values) between HER2-positive and -negative BCa were compared using student t test and chi-square test. The diagnostic efficacy of parameters in predicting HER2 expression status was evaluated by calculating the area under ROC curve (AUC).

RESULTS

In total, 76 patients (mean age, 63.59 years ± 12.84 [SD]; 55 men) were included: 51 with HER2-negative and 25 with HER2-positive BCa. HER2-positive group demonstrated significantly higher ADC, T1, and T2 values than HER2-negative group (all P < 0.05). The combination of ADC values and tumor grade yielded the best diagnostic performance in evaluating HER2 expression level with an AUC of 0.864.

CONCLUSION

The multiparametric MR characterization can accurately evaluate the HER2 expression status in BCa, which may further guide the determination of individualized anti-HER2 targeted therapy strategies.

Predicting human epidermal growth factor receptor 2 status of patients with gastric cancer by computed tomography and clinical features

Background

There have been no studies on predicting human epidermal growth factor receptor 2 (HER2) status in patients with resectable gastric cancer (GC) in the neoadjuvant and perioperative settings. We aimed to investigate the use of preoperative contrast-enhanced computed tomography (CECT) imaging features combined with clinical characteristics for predicting HER2 expression in GC.

Methods

We retrospectively enrolled 301 patients with GC who underwent curative resection and preoperative CECT. HER2 status was confirmed by postoperative immunohistochemical analysis with or without fluorescence in situ hybridization. A prediction model was developed using CECT imaging features and clinical characteristics that were independently associated with HER2 status using multivariate logistic regression analysis. Receiver operating characteristic curves were constructed and the performance of the prediction model was evaluated. The bootstrap method was used for internal validation.

Results

Three CECT imaging features and one serum tumor marker were independently associated with HER2 status in GC: enhancement ratio in the arterial phase (odds ratio [OR] = 4.535; 95% confidence interval [CI], 2.220-9.264), intratumoral necrosis (OR = 2.64; 95% CI, 1.180-5.258), tumor margin (OR = 3.773; 95% CI, 1.968-7.235), and cancer antigen 125 (CA125) level (OR = 5.551; 95% CI, 1.361-22.651). A prediction model derived from these variables showed an area under the receiver operating characteristic curve of 0.802 (95% CI, 0.740-0.864) for predicting HER2 status in GC. The established model was stable, and the parameters were accurately estimated.

Conclusions

Enhancement ratio in the arterial phase, intratumoral necrosis, tumor margin, and CA125 levels were independently associated with HER2 status in GC. The prediction model derived from these factors may be used preoperatively to estimate HER2 status in GC and guide clinical treatment.

Readout-Segmented Echo-Planar Diffusion-Weighted MR Imaging Improves the Differentiation of Breast Cancer Receptor Statuses Compared With Conventional Diffusion-Weighted Imaging

BACKGROUND

Readout-segmented echo-planar diffusion-weighted imaging (RS-EPI) can improve image quality and signal-to-noise ratio, the resulting apparent diffusion coefficient (ADC) value acts as a more sensitive biomarker to characterize tumors. However, data regarding the differentiation of breast cancer (BC) receptor statuses using RS-EPI are limited.

PURPOSE

To determine whether RS-EPI improves the differentiation of receptor statuses compared with conventional single-shot (SS) EPI in breast MRI.

STUDY TYPE

Retrospective.

POPULATION

A total of 151 BC women with the mean age of 50.6 years.

FIELD STRENGTH/SEQUENCE

A 3 T/ RS-EPI and SS-EPI.

ASSESSMENT

The ADCs of the lesion and normal background tissue from the two sequences were collected by two radiologists with 15 years of experience working of breast MRI (M.H.Z. and X.F.C.), and a normalized ADC was calculated by dividing the mean ADC value of the lesion by the mean ADC value of the normal background tissue.

STATISTICAL TESTS

Agreement between the ADC measurements from the two sequences was assessed using the Pearson correlation coefficient and Bland-Altman plots. One-way analysis of variance, Kruskal-Wallis test, and median difference were used to compare the ADC measurements for all lesions and different receptor statuses. A P value less than 0.05 indicated a significant result.

RESULTS

The ADC measurements of all lesions and normal background tissues were significantly higher on RS-EPI than on SS-EPI (1.82 ± 0.33 vs. 1.55 ± 0.30 and 0.83 ± 0.11 vs. 0.79 ± 0.10). The normalized ADC was lower on RS-EPI than on SS-EPI (0.47 ± 0.11 vs. 0.53 ± 0.12, a median difference of -0.04 [95% CI: -0.256 to 0.111]). For both diffusion methods, only the ADC measurement of RS-EPI was higher for human epidermal growth factor receptor-2 (HER-2)-positive tumors than for HER-2-negative tumors (0.87 ± 0.10 vs. 0.81 ± 0.11), and this measurement was associated with HER-2 positive status (adjusted odds ratio [OR] = 654.4); however, similar results were not observed for the ADC measurement of SS-EPI (0.80 ± 0.10 vs. 0.78 ± 0.11 with P = 0.199 and adjusted OR = 0.21 with P = 0.464, respectively).

DATA CONCLUSION

RS-EPI can improve the distinction between HER-2-positive and HER-2-negative breast cancer, complementing the clinical application of diffusion imaging.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 1.

Development and validation of multivariate models integrating preoperative clinicopathological and radiographic findings to predict HER2 status in gastric cancer

The combination of trastuzumab and chemotherapy is recommended as first-line therapy for patients with human epidermal growth factor receptor 2 (HER2) positive advanced gastric cancers (GCs). Successful trastuzumab-induced targeted therapy should be based on the assessment of HER2 overexpression. This study aimed to evaluate the feasibility of multivariate models based on hematological parameters, endoscopic biopsy, and computed tomography (CT) findings for assessing HER2 overexpression in GC. This retrospective study included 183 patients with GC, and they were divided into primary (n = 137) and validation (n = 46) cohorts at a ratio of 3:1. Hematological parameters, endoscopic biopsy, CT morphological characteristics, and CT value-related and texture parameters of all patients were collected and analyzed. The mean corpuscular hemoglobin concentration value, morphological type, 3 CT value-related parameters, and 22 texture parameters in three contrast-enhanced phases differed significantly between the two groups (all p < 0.05). Multivariate models based on the regression analysis and support vector machine algorithm achieved areas under the curve of 0.818 and 0.879 in the primary cohort, respectively. The combination of hematological parameters, CT morphological characteristics, CT value-related and texture parameters could predict HER2 overexpression in GCs with satisfactory diagnostic efficiency. The decision curve analysis confirmed the clinical utility.

Evaluation of Epidermal Growth Factor Receptor 2 Status in Gastric Cancer by CT-Based Deep Learning Radiomics Nomogram

Purpose

To explore the role of computed tomography (CT)-based deep learning and radiomics in preoperative evaluation of epidermal growth factor receptor 2 (HER2) status in gastric cancer.

Materials and methods

The clinical data on gastric cancer patients were evaluated retrospectively, and 357 patients were chosen for this study (training cohort: 249; test cohort: 108). The preprocessed enhanced CT arterial phase images were selected for lesion segmentation, radiomics and deep learning feature extraction. We integrated deep learning features and radiomic features (Inte). Four methods were used for feature selection. We constructed models with support vector machine (SVM) or random forest (RF), respectively. The area under the receiver operating characteristics curve (AUC) was used to assess the performance of these models. We also constructed a nomogram including Inte-feature scores and clinical factors.

Results

The radiomics-SVM model showed good classification performance (AUC, training cohort: 0.8069; test cohort: 0.7869). The AUC of the ResNet50-SVM model and the Inte-SVM model in the test cohort were 0.8955 and 0.9055. The nomogram also showed excellent discrimination achieving greater AUC (training cohort, 0.9207; test cohort, 0.9224).

Conclusion

CT-based deep learning radiomics nomogram can accurately and effectively assess the HER2 status in patients with gastric cancer before surgery and it is expected to assist physicians in clinical decision-making and facilitates individualized treatment planning.

Correlation between molecular prognostic factors and magnetic resonance imaging intravoxel incoherent motion histogram parameters in breast cancer

OBJECTIVE

To explore the efficacy of the quantitative parameter histogram analysis of intravoxel incoherent motion (IVIM) for different molecular prognostic factors of breast cancer.

MATERIALS AND METHODS

A total of 72 patients with breast cancer who were confirmed by surgical pathology and underwent preoperative magnetic resonance imaging (MRI) were analyzed retrospectively. A region of interest (ROI) was drawn in each slice of the IVIM images. Whole-tumor histogram parameters were obtained with Firevoxel's software by accumulating all ROIs. Next, Kolmogorov-Smirnov test, Student's t-test, Mann-Whitney U test, receiver operating characteristic curve analysis and spearman rank correlation analysis were used to assess the relationship between histogram parameters and molecular prognostic factors of breast cancer.

RESULTS

Among estrogen receptor (ER)-negative ROCs, the apparent diffusion coefficient (ADC) 10th percentile had the highest ROC of 0.792, with a cut-off value of 0.788 × 10^-3 mm²/s, and sensitivity and specificity of 0.714 and 0.867, respectively. The negative correlation between lymph node metastasis status and ADC standard deviation was significant (ρ = -0.44, the correlation coefficients was represented by ρ). Positive correlations were observed between hormonal expression of ER and progesterone receptor (PR) with heterogeneity metrics of ADC or perfusion fraction (f), such as ADC inhomogeneity (ρ = 0.37, ρ = 0.29) and f skewness (ρ = 0.32, ρ = 0.28). Negative correlations were observed with numerical metrics, such as the ADC median (ρ = -0.31, ρ = -0.34) and f 45th percentile (ρ = -0.35, ρ = -0.28). The positive correlations between human epidermal receptor factor-2 (HER2) and pseudo-diffusivity (Dp) numerical metrics, Ki-67 expression, and heterogeneity metrics of Dp were high.

CONCLUSIONS

The ADC 10th percentile had the largest area under the curve in the ER-negative ROC analysis, and the ADC standard deviation was the most valuable in the correlation analysis of lymph node metastasis. Whole-lesion quantitative histogram parameters of IVIM could, therefore, provide a scientific basis for radiomics to further guide clinical practice in the prognosis of breast cancer.

Prognostic prediction of resectable colorectal liver metastasis using the apparent diffusion coefficient from diffusion-weighted magnetic resonance imaging

AIM

Diffusion-weighted magnetic resonance imaging (DWI-MRI) is used to predict tumor malignancy. Here we explored the role of apparent diffusion coefficient (ADC) values in the treatment of patients with resectable colorectal liver metastasis (CRLM).

METHODS

Magnetic resonance imaging (MRI) scans were conducted using a Signa HDe or Signa Explorer 1.5-T scanner (GE Healthcare). ADC maps were calculated using DWI with b values of 0, 20, and 800 s/mm2. We enrolled 60 patients who underwent upfront hepatic resection for CRLM and divided them into ADC-high (n = 30) and ADC-low (n = 30) groups. Clinicopathological variables of the groups were compared. Immunohistochemical analysis of HIF-1α expression in tumor tissues was performed, and the relationship between the ADC value and HIF-1α expression was evaluated.

RESULTS

The disease-free survival rate of the ADC-low group was significantly lower than that of the ADC-high group (P < .05). Univariate analysis revealed that tumor number (more than five), synchronous metastasis, and low ADC were prognostic factors. Multivariate analysis identified low ADC as an independent prognostic factor. Furthermore, the ADC-low group more frequently expressed high levels of HIF-1α than the ADC-high group.

CONCLUSION

Low ADC values were an independent prognostic factor of resectable CRLM and correlated with HIF-1α expression.

CT Radiomics for Distinction of Human Epidermal Growth Factor Receptor 2 Negative Gastric Cancer

RATIONALE AND OBJECTIVES

The purpose of this study was to investigate the role of computed tomography (CT) radiomics for the prediction of the human epidermal growth factor 2 (HER2) status in patients with gastric cancer.

METHODS

One hundred and thirty two consecutive patients with advanced gastric cancer undergoing radical gastrectomy were retrospectively reviewed. All patients received preoperative contrast CT examination, and immunohistochemistry results of their HER2 status were available. All the subjects were randomly divided into a training cohort (n = 90) and a test cohort (n = 42). Arterial phase (AP) and portal phase (PP) contrast CT images were retrieved for tumor segmentation and feature extraction. Receiver operating characteristics (ROC) curves and area under the curve (AUC) were used to evaluate the performance of the radiomics classifiers.

RESULTS

Among the 132 patients, a total of 99 patients were HER2 negative, and the remaining 33 patients were border line or positive. The AP radiomics model could distinguish HER2-negative cases with an AUC of 0.756 (95% confidence interval [CI]: 0.656-0.840) in the training cohort, which was confirmed in the test cohort with AUC of 0.830 (95% CI: 0.678-0.930). The PP radiomics model showed AUCs of 0.715 (95% CI: 0.612-0.804) and 0.718 (95% CI: 0.554-0.849) in the training and test cohort for distinction of negative HER2 cases, respectively.

CONCLUSION

Radiomics models based on standard-of-care CT images hold promise for distinguishing HER2-negative gastric cancer.

Use of Diffusion-Weighted Magnetic Resonance Imaging and Apparent Diffusion Coefficient in Gastric Cancer Staging

BACKGROUND

The apparent diffusion coefficient (ADC), the quantitative parameter of diffusion-weighted magnetic resonance imaging (DW-MRI), is a measure for this restricted diffusion, and its role in gastric cancer (GC) including distinguishing malignant segments from healthy gastric wall, metastatic perigastric lymph nodes from benign nodes and evaluating response of GC to neoadjuvant chemotherapy has been investigated in previous studies. Evidence suggests that ADC may also be of help in assessment of aggressiveness and preoperative staging of gastric cancer, which needs to be explored in further studies.

OBJECTIVE

To investigate the role of DW-MRI and its quantitative parameter, ADC in staging of gastric cancer.

METHODS

Forty-six patients (28 male, 18 female, mean age 62 years) with non-metastatic biopsy- proven GC who underwent abdominal DW-MRI before surgery were included in this retrospective study. Tumor invasion depth (T-stage) and nodal involvement (N-stage) were evaluated using signal increase on DW-MRI, and tumor ADC was measured. Diagnostic performance of these results was assessed by comparing them with postsurgical histopathology based on 8th TNM classification.

RESULTS

Sensitivity, specificity, and accuracy of DW-MRI in T-staging were 92.1%, 75%, 89.1% for ≤T2 vs. ≥T3; and 75%, 88.5%, 82.6% for ≤T3 vs. T4. However, sensitivity, specificity, and accuracy of DW-MRI in N-staging were 89.3%, 88.9%, 89.1% for ≤N1 vs. ≥N2; and 73.7%, 96.3%, 86.9% for ≤N2 vs. N3, respectively. Relative preoperative ADC values correlated with pT staging (r=-0.397, p=0.006). There was also a statistically significant difference of relative ADC values between ≤T3 and T4 stages, and a cut-off of 0.64 s/mm2 could differentiate these stages with an odds ratio of 7.714 (95% confidence interval, 1.479-40.243). The area under the receiver operating characteristic curve for differentiating ≤T3 and T4 stages was 0.725.

CONCLUSION

DW-MRI may contribute to the clinical staging of non-metastatic GC. In particular, relative ADC of DW-MRI can distinguish T4 gastric cancer from less advanced T-stages.

Imaging biomarkers in upper gastrointestinal cancers

In parallel with the increasingly widespread availability of high performance imaging platforms and recent progresses in pathobiological characterisation and treatment of gastrointestinal malignancies, imaging biomarkers have become a major research topic due to their potential to provide additional quantitative information to conventional imaging modalities that can improve accuracy at staging and follow-up, predict outcome, and guide treatment planning in an individualised manner. The aim of this review is to briefly examine the status of current knowledge about imaging biomarkers in the field of upper gastrointestinal cancers, highlighting their potential applications and future perspectives in patient management from diagnosis onwards.

Imaging strategies in the management of gastric cancer: current role and future potential of MRI

Accurate preoperative staging of gastric cancer and the assessment of tumor response to neoadjuvant treatment is of importance for treatment and prognosis. Current imaging techniques, mainly endoscopic ultrasonography (EUS), computed tomography (CT) and ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET), have their limitations. Historically, the role of magnetic resonance imaging (MRI) in gastric cancer has been limited, but with the continuous technical improvements, MRI has become a more potent imaging technique for gastrointestinal malignancies. The accuracy of MRI for T- and N-staging of gastric cancer is similar to EUS and CT, making MRI a suitable alternative to other imaging strategies. There is limited evidence on the performance of MRI for M-staging of gastric cancer specifically, but MRI is widely used for diagnosing liver metastases and shows potential for diagnosing peritoneal seeding. Recent pilot studies showed that treatment response assessment as well as detection of lymph node metastases and systemic disease might benefit from functional MRI (e.g. diffusion weighted imaging and dynamic contrast enhancement). Regarding treatment guidance, additional value of MRI might be expected from its role in better defining clinical target volumes and setup verification with MR-guided radiation treatment.

Combined Detection of HER2, Ki67, and GSTP1 Genes on the Diagnosis and Prognosis of Breast Cancer

OBJECTIVE

Breast cancer (BC) is a common malignant tumor in females. The combined assay of multiple molecular markers benefits the diagnosis and prognostic prediction. Human epidermal growth factor receptor 2 (HER2) facilitates the proliferation and differentiation of cancer cells through ligand binding. Ki67 is a tumor proliferation-related gene, whereas GSTP1 is a DNA repair-related gene. This study thus investigated the significance of HER2 and Ki67/GSTP1 gene combined assay in the diagnosis and prognosis of BC.

MATERIALS AND METHODS

A total of 86 breast tumor tissues and adjacent tissues were collected. Gene expression and protein levels of HER2 and Ki67 were quantified by real-time polymerase chain reaction (PCR) and Western blot, respectively. Methylation frequency of GSTP1 was analyzed by methylation-specific PCR. The correlation between HER2 and Ki67/GSTP1 and clinical/pathological features of BC was analyzed.

RESULTS

Gene and protein expression levels of HER2 and Ki67 in tumor tissues were increased (p < 0.05 compared with adjacent tissues). Methylation frequency of GSTP1 gene was 37.2%, which was significantly higher in breast tumor tissues than in adjacent tissues (12.79%, p < 0.05). HER2 expression was positively correlated with TNM stage, tumor size, and lymph node metastasis, and negatively correlated with tissue grade and estrogen receptor (ER)/progesterone receptor (PR) expression (p < 0.05). GSTP1 methylation was positively correlated with TNM stage and tumor size, and negatively correlated with ER/PR expression (p < 0.05).

CONCLUSIONS

HER2, Ki67, and GSTP1 methylation were correlated with clinical and pathological features of BC. The combined assay benefits the early diagnosis and prognostic prediction of cancer.

Histogram parameters derived from T2 weighted images are associated with histopathological findings in rectal cancer - a preliminary study

Histogram analysis can better reflect tumor heterogeneity than conventional imaging analysis. The present study analyzed possible correlations between histogram parameters derived from T2 weighted images and histopathological features in rectal cancer. Seventeen patients with histopathological proven rectal adenocarcinoma were retrospectively acquired with prebioptic 3 T MRI and available histopathological specimens. Histogram analysis was performed using an in-house matlab tool conducting a whole lesion measurement. Histopathology was investigated using Ki67 specimens with calculation of Ki67-index as well as cellularity and nucleic areas and CD31 specimens, with estimation of microvessel density. Several histogram parameters correlated with average nucleic area. Skewness showed a moderate correlation with microvessel density (P = 0.54, P = 0.02). None of the parameters correlated with Ki67-index. Skewness derived from T2 weighted images might be used as a surrogate parameter for average nucleic area and microvessel density. However, none of the parameters were associated with proliferation index.

Role of intravoxel incoherent motion MR imaging in preoperative assessing HER2 status of gastric cancers

Purpose

To explore the role of intravoxel incoherent motion (IVIM) magnetic resonance (MR) imaging in evaluating human epidermal growth factor receptor 2 (HER2) status of gastric cancers preoperatively.

Results

The apparent diffusion coefficient (ADC) and pure diffusion coefficient (D) values correlated positively with HER2 scores of gastric cancers significantly (r = 0.276, P = 0.048; r = 0.481, P < 0.001, respectively). The ADC and D values of HER2 positive gastric cancers were significantly higher than those of HER2 negative tumors (P = 0.033, 0.007, respectively). With a cut-off value of 1.321 and 1.123 × 10⁻³ mm²/sec, the ADC and D values could distinguish HER2 positive gastric cancers from HER2 negative ones with an area under the curve of 0.733 and 0.762, respectively (P = 0.023, 0.011, respectively).

Materials and methods

Fifty-three patients with gastric cancers underwent IVIM MR imaging preoperatively. The values of ADC, D, pseudo diffusion coefficient (D*) and perfusion related fraction (f) of the lesions were obtained. Partial correlation test including tumor volume was performed to analyze correlations between IVIM values and HER2 scores excluding the impact of tumor size. IVIM parameters of gastric cancers with different HER2 status were compared using independent samples t test. Diagnostic performance of IVIM parameters in distinguishing HER2 positive gastric cancers from negative ones was tested with receiver operating characteristic analysis.

Conclusions

We confirmed the feasibility of IVIM MR imaging in preoperative assessment of HER2 status of gastric cancers, which might make up the shortfall of biopsy and facilitate personalized treatment for patients with gastric cancers.

Apparent diffusion coefficients in prostate cancer: correlation with molecular markers Ki-67, HIF-1α and VEGF

Prostate cancer (PCa) is the second most common cancer in men. The Gleason score (GS) and biomarkers play important roles in the diagnosis and treatment of patients with PCa. The purpose of this study was to investigate the relationship between the apparent diffusion coefficient (ADC) and the molecular markers Ki-67, hypoxia-inducible factor-1α (HIF-1α) and vascular endothelial growth factor (VEGF) in PCa. Thirty-nine patients with 39 lesions, who had been diagnosed with PCa, were enrolled in this study. All patients underwent diffusion-weighted magnetic resonance imaging (DW-MRI) (b = 800 s/mm² ). The expression of Ki-67, HIF-1α and VEGF was assessed by immunohistochemistry. Statistical analysis was applied to analyze the association between ADC and prostate-specific antigen (PSA), GS and the expression of Ki-67, HIF-1α and VEGF. The group differences in ADC among different grades of Ki-67, HIF-1α and VEGF were also analyzed. The mean ± standard deviation of ADC was (0.76 ± 0.27) × 10^-3  mm² /s. ADC correlated negatively with PSA and GS (p < 0.05). The Ki-67 staining index (SI), HIF-1α expression and VEGF expression in PCa were correlated inversely with ADC, controlling for age (r = -0.332, p < 0.05; r = -0.662, p < 0.0005; and r = -0.714, p < 0.0005, respectively). ADC showed a significant difference among different grades of Ki-67 (F = 9.164, p = 0.005), HIF-1α (F = 40.333, p < 0.0005) and VEGF (F = 22.048, p < 0.0005). In conclusion, ADC was correlated with PSA, GS, and Ki-67, HIF-1α and VEGF expression in patients with PCa. ADC may be used to evaluate tumor proliferation, hypoxia and angiogenesis in PCa.

Human Epidermal Growth Factor Receptor 2 Expression in Unresectable Gastric Cancers: Relationship with CT Characteristics

Objective

To retrospectively analyze the qualitative CT features that correlate with human epidermal growth factor receptor 2 (HER2)-expression in pathologically-proven gastric cancers.

Materials and Methods

A total of 181 patients with pathologically-proven unresectable gastric cancers with HER2-expression (HER2-positive [n = 32] and negative [n = 149]) were included. CT features of primary gastric and metastatic tumors were reviewed. The prevalence of each CT finding was compared in both groups. Thereafter, binary logistic regression determined the most significant differential CT features. Clinical outcomes were compared using Kaplan-Meier method.

Results

HER2-postive cancers showed lower clinical T stage (21.9% vs. 8.1%; p = 0.015), hyperattenuation on portal phase (62.5% vs. 30.9%; p = 0.003), and was more frequently metastasized to the liver (62.5% vs. 32.2%; p = 0.001), than HER2-negative cancers. On binary regression analysis, hyperattenuation of the tumor (odds ratio [OR], 4.68; p < 0.001) and hepatic metastasis (OR, 4.43; p = 0.001) were significant independent factors that predict HER2-positive cancers. Median survival of HER2-positive cancers (13.7 months) was significantly longer than HER2-negative cancers (9.6 months) (p = 0.035).

Conclusion

HER2-positive gastric cancers show less-advanced T stage, hyperattenuation on the portal phase, and frequently metastasize to the liver, as compared to HER2-negative cancers.

Whole-volume apparent diffusion coefficient-based entropy parameters for assessment of gastric cancer aggressiveness

PURPOSE

To explore the role of whole-volume apparent diffusion coefficient (ADC)-based entropy parameters in the preoperative assessment of gastric cancer's aggressiveness.

MATERIALS AND METHODS

In all, 64 patients with gastric cancers who underwent 3.0T magnetic resonance imaging (MRI) were retrospectively included. Regions of interest were drawn manually using in-house software, around gastric cancer lesions on each slice of the diffusion-weighted images and ADC maps. Entropy-related parameters based on ADC maps were calculated automatically: (1) first-order entropy; (2-5) second-order entropies, including entropy(H)₀ , entropy(H)₄₅ , entropy(H)₉₀ , and entropy(H)₁₃₅ ; (6) entropy(H)_mean ; and (7) entropy(H)_range . Correlations between entropy-related parameters and pathological characteristics were analyzed with the Spearman correlation test. The parameters were compared among different pathological characteristics with independent-samples Kruskal-Wallis or Mann-Whitney U-test. Additionally, diagnostic performances of parameters in differentiating different pathological characteristics were analyzed by receiver operating characteristic (ROC) curve analysis.

RESULTS

All the entropy-related parameters significantly correlated with T, N, and overall stages, especially the first-order entropy (r = 0.588, 0.585, and 0.677, respectively, all P < 0.05). All the entropy-related parameters showed significant differences in gastric cancers at different T, N, and overall stages, as well as at different status of vascular invasion (P < 0.001-0.027). And four parameters, including entropy, entropy(H)₀ , entropy(H)₄₅ , and entropy(H)₉₀ , showed significant differences between gastric cancers with and without perineural invasion (P 0.006-0.040).

CONCLUSION

Entropy-related parameters derived from whole-volume ADC texture analysis could help assess the aggressiveness of gastric cancers via analyzing intratumoral heterogeneity quantitatively, especially the first-order entropy.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:168-175.

Assessment of histological differentiation in gastric cancers using whole-volume histogram analysis of apparent diffusion coefficient maps

PURPOSE

To investigate the efficacy of histogram analysis of the entire tumor volume in apparent diffusion coefficient (ADC) maps for differentiating between histological grades in gastric cancer.

MATERIALS AND METHODS

Seventy-eight patients with gastric cancer were enrolled in a retrospective 3.0T magnetic resonance imaging (MRI) study. ADC maps were obtained at two different b values (0 and 1000 sec/mm² ) for each patient. Tumors were delineated on each slice of the ADC maps, and a histogram for the entire tumor volume was subsequently generated. A series of histogram parameters (eg, skew and kurtosis) were calculated and correlated with the histological grade of the surgical specimen. The diagnostic performance of each parameter for distinguishing poorly from moderately well-differentiated gastric cancers was assessed by using the area under the receiver operating characteristic curve (AUC).

RESULTS

There were significant differences in the 5^th , 10^th , 25^th , and 50^th percentiles, skew, and kurtosis between poorly and well-differentiated gastric cancers (P < 0.05). There were correlations between the degrees of differentiation and histogram parameters, including the 10^th percentile, skew, kurtosis, and max frequency; the correlation coefficients were 0.273, -0.361, -0.339, and -0.370, respectively. Among all the histogram parameters, the max frequency had the largest AUC value, which was 0.675.

CONCLUSION

Histogram analysis of the ADC maps on the basis of the entire tumor volume can be useful in differentiating between histological grades for gastric cancer.

LEVEL OF EVIDENCE

4 J. Magn. Reson. Imaging 2017;45:440-449.