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

Amide Proton Transfer-Weighted Imaging in Assessing the Aggressive and Proliferative Potential of Bladder Cancer

BACKGROUND

Ki-67 and human epidermal growth factor receptor 2 (HER2) are known oncogenes involved in bladder cancer (BCa) patient risk stratification. Preoperative assessment of their expression level can assist in clinical treatment decision-making. Recently, amide proton transfer-weighted (APTw) MRI has shown promising potential in the diagnosis of several malignancies. However, few studies reported the value of APTw imaging in evaluating Ki-67 and HER2 status of BCa.

PURPOSE

To investigate the feasibility of APTw MRI in assessing the aggressive and proliferative potential regarding the expression levels of Ki-67 and HER2 in BCa.

STUDY TYPE

Retrospective.

SUBJECTS

114 patients (mean age, 64.78 ± 11.93 [SD] years; 97 men) were studied.

FIELD STRENGTH/SEQUENCE

APTw MRI acquired by a three-dimensional fast-spin-echo sequence at 3.0 T MRI system.

ASSESSMENT

Patient pathologic findings, included histologic grade and the expression status of Ki-67 and HER2, were reviewed by one uropathologist. The APTw values of BCa were independently measured by two radiologists and were compared between high-/low-tumor grade group, high-/low-Ki-67 expression group, and high-/low-HER2 expression group.

STATISTICAL TESTS

The interclass correlation coefficient, independent sample t-test, Mann-Whitney U test, Spearman's rank correlation, and receiver operating characteristic curve (ROC) analysis were used. P < 0.05 was considered statistically significant.

RESULTS

Significantly higher APTw values were found in high-grade BCa patients (7.72% vs. 4.29%, P < 0.001), high-Ki-67 expression BCa patients (8.40% vs. 3.25%, P < 0.001) and HER2 positive BCa patients (8.24% vs. 5.40%, P = 0.001). APTw values were positively correlated with Ki-67 (r = 0.769) and HER2 (r = 0. 356) expression status. The area under the ROC curve of the APTw values for detecting Ki-67 and HER2 expression status were 0.883 (95% CI: 0.790-0.945) and 0.713 (95% CI: 0.592-0.816), respectively.

DATA CONCLUSIONS

APTw MRI is a potential method to assess the biological and proliferation potential of BCa.

LEVEL OF EVIDENCE: 4

TECHNICAL EFFICACY

Stage 2.

Development and Validation of a Nomogram Based on DCE-MRI Radiomics for Predicting Hypoxia-Inducible Factor 1α Expression in Locally Advanced Rectal Cancer

RATIONALE AND OBJECTIVES

The expression levels of hypoxia-inducible factor 1 alpha (HIF-1α) have been identified as a pivotal marker, correlating with treatment response in patients with locally advanced rectal cancer (LARC). This study aimed to develop and validate a nomogram based on dynamic contrast-enhanced MRI (DCE-MRI) radiomics and clinical features for predicting the expression of HIF-1α in patients with LARC.

MATERIALS AND METHODS

A total of 102 patients diagnosed with locally advanced rectal cancer were divided into training (n = 71) and validation (n = 31) cohorts. The expression statuses of HIF-1α were histopathologically classified, categorizing patients into high and low expression groups. The intraclass correlation coefficient (ICC), minimum redundancy maximum relevance (mRMR), and the least absolute shrinkage and selection operator (LASSO) were employed for feature selection to construct a radiomics signature and calculate the radiomics score (Rad-score). Univariate and multivariate analyses of clinical features and Rad-score were applied, and the clinical model and the nomogram were constructed. The predictive performance of the nomogram incorporating clinical features and Rad-score was assessed using Receiver Operating Characteristics (ROC) curves, decision curve analysis (DCA), and calibration curves.

RESULTS

Seven radiomics features from DCE-MRI were used to build the radiomics signature. The nomogram incorporating CEA, Ki-67 and Rad-score had the highest AUC values in the training cohort and in the validation cohort (AUC: 0.918 and 0.920). Decision curve analysis showed that the nomogram outperformed the clinical model and radiomics signature in terms of clinical utility. In addition, the calibration curve for the nomogram demonstrated good agreement between prediction and actual observation.

CONCLUSION

The nomogram based on DCE-MRI radiomics and clinical features showed favorable predictive efficacy and might be useful for preoperatively discriminating the expression of HIF-1α.

R2* mapping and reduced field-of-view diffusion-weighted imaging for preoperative assessment of nonenlarged lymph node metastasis in rectal cancer

The aim of the current study is to investigate the diagnostic value of R2* mapping versus reduced field-of-view diffusion-weighted imaging (rDWI) of the primary lesion of rectal cancer for preoperative prediction of nonenlarged lymph node metastasis (NLNM). Eighty-one patients with pathologically confirmed rectal cancer underwent preoperative R2* mapping and rDWI sequences before total mesorectal excisions and accompanying regional lymph node dissections. Two radiologists independently performed whole-tumor measurements of R2* and apparent diffusion coefficient (ADC) parameters on primary lesions of rectal cancer. Patients were divided into positive (NLNM+) and negative (NLNM-) groups based on their pathological analysis. The tumor location, maximum diameter of the tumor, and maximum short diameter of the lymph node were assessed. R2* and ADC, pT stage, tumor grade, status of mesorectal fascia, and extramural vascular invasion were also studied for their potential relationships with NLNM using multivariate logistic regression analysis. The NLNM+ group had significantly higher R2* (43.56 ± 8.43 vs. 33.87 ± 9.57, p < 0.001) and lower ADC (1.00 ± 0.13 vs. 1.06 ± 0.22, p = 0.036) than the NLNM- group. R2* and ADC were correlated to lymph node metastasis (r = 0.510, p < 0.001 for R2*; r = -0.235, p = 0.035 for ADC). R2* and ADC showed good and moderate diagnostic abilities in the assessment of NLNM status with corresponding area-under-the-curve values of 0.795 and 0.636. R2* provided a significantly better diagnostic performance compared with ADC for the prediction of NLNM status (z = 1.962, p = 0.0498). The multivariate logistic regression analysis demonstrated that R2* was a compelling factor of lymph node metastasis (odds ratio = 56.485, 95% confidence interval: 5.759-554.013; p = 0.001). R2* mapping had significantly higher diagnostic performance than rDWI from the primary tumor of rectal cancer in the prediction of NLNM status.

Amide proton transfer-weighted imaging of the abdomen: Current progress and future directions

The chemical exchange saturation transfer technique serves as a valuable tool for generating in vivo image contrast based on the content of various proton groups, including amide protons, amine protons, and aliphatic protons. Among these, amide proton transfer-weighted (APTw) imaging has seen extensive development as a means to assess the biochemical status of lesions. The exchange from saturated amide protons to bulk water protons during and following the saturation ratio frequency pulse contributes to detectable APT signals. While APTw imaging has garnered significant attention in the central nervous system, demonstrating noteworthy findings in cerebral neoplasia, stroke, and Alzheimer's disease over the past decade, its application in the abdomen has been a relatively recent progression. Notably, studies have explored its utility in hepatocellular carcinoma, prostate cancer, and cervical carcinoma within the abdominal context. Despite these advancements, there is a paucity of reviews on APTw imaging in abdominal applications. This paper aims to fill this gap by providing a concise overview of the fundamental theories underpinning APTw imaging. Additionally, we systematically summarize its diverse clinical applications in the abdomen, with a particular focus on the digestive and urogenital systems. Finally, the manuscript concludes by discussing technical limitations and factors influencing APTw imaging in abdominal applications, along with prospects for future research.

Deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram for predicting Ki-67 expression in rectal cancer

PURPOSE

To explore the value of deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram in predicting the Ki-67 expression in rectal cancer.

METHODS

The data of 491 patients with rectal cancer from two centers were retrospectively analyzed and divided into training, internal validation, and external validation sets. They were categorized into high- and low-expression group based on postoperative pathological Ki-67 expression. Each patient's mp-MRI data were analyzed to extract and select the most relevant features of deep learning, and a deep learning model was constructed. Independent predictive risk factors were identified and incorporated into a clinical model, and the clinical and deep learning models were combined to obtain a nomogram for the prediction of Ki-67 expression. The performance characteristics of the DL-model, clinical model, and nomogram were assessed using ROCs, calibration curve, decision curve, and clinical impact curve analysis.

RESULTS

The strongest deep learning features were extracted and screened from mp-MRI data. Two independent predictive factors, namely Magnetic Resonance Imaging T (mrT) staging and differentiation degree, were identified through clinical feature selection. Three models were constructed: a deep learning (DL)-model, a clinical model, and a nomogram. The AUCs of clinical model in the training, internal validation, and external validation set were 0.69, 0.78, and 0.67, respectively. The AUCs of the deep model and nomogram ranged from 0.88 to 0.98. The prediction performance of the deep learning model and nomogram was significantly better than the clinical model (P < 0.001).

CONCLUSION

The nomogram based on deep learning can help clinicians accurately and conveniently predict the expression status of Ki-67 in rectal cancer.

CT-based radiomics for predicting Ki-67 expression in lung cancer: a systematic review and meta-analysis

Background

Radiomics, an emerging field, presents a promising avenue for the accurate prediction of biomarkers in different solid cancers. Lung cancer remains a significant global health challenge, contributing substantially to cancer-related mortality. Accurate assessment of Ki-67, a marker reflecting cellular proliferation, is crucial for evaluating tumor aggressiveness and treatment responsiveness, particularly in non-small cell lung cancer (NSCLC).

Methods

A systematic review and meta-analysis conducted following the preferred reporting items for systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA) guidelines. Two authors independently conducted a literature search until September 23, 2023, in PubMed, Embase, and Web of Science. The focus was on identifying radiomics studies that predict Ki-67 expression in lung cancer. We evaluated quality using both Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and the Radiomics Quality Score (RQS) tools. For statistical analysis in the meta-analysis, we used STATA 14.2 to assess sensitivity, specificity, heterogeneity, and diagnostic values.

Results

Ten retrospective studies were pooled in the meta-analysis. The findings demonstrated that the use of computed tomography (CT) scan-based radiomics for predicting Ki-67 expression in lung cancer exhibited encouraging diagnostic performance. Pooled sensitivity, specificity, and area under the curve (AUC) in training cohorts were 0.78, 0.81, and 0.85, respectively. In validation cohorts, these values were 0.78, 0.70, and 0.81. Quality assessment using QUADAS-2 and RQS indicated generally acceptable study quality. Heterogeneity in training cohorts, attributed to factors like contrast-enhanced CT scans and specific Ki-67 thresholds, was observed. Notably, publication bias was detected in the training cohort, indicating that positive results are more likely to be published than non-significant or negative results. Thus, journals are encouraged to publish negative results as well.

Conclusion

In summary, CT-based radiomics exhibit promise in predicting Ki-67 expression in lung cancer. While the results suggest potential clinical utility, additional research efforts should concentrate on enhancing diagnostic accuracy. This could pave the way for the integration of radiomics methods as a less invasive alternative to current procedures like biopsy and surgery in the assessment of Ki-67 expression.

Amide proton transfer weighted MRI in differential diagnosis of ovarian masses with cystic components: A preliminary study

RATIONALE AND OBJECTIVES

To evaluate the performance of three-dimensional (3D) amide proton transfer-weighted (APTw) MRI in the differentiation between benign and malignant ovarian masses based on single-slice and all-slice analysis of cystic regions.

MATERIALS AND METHODS

Patients were consecutively recruited and underwent conventional pelvic MRI and APTw MRI. Two radiologists independently assessed ovarian masses blinded to the histopathological results. Three APTw SI values were generated from the cystic regions of the masses: (1) APTw SI of a single representative slice (RS); (2) average (AVE) of APTw SIs of all slices of the mass; (3) area-weighted (AW) average of APTw SIs of all slices of the mass. O-RADS MRI score of each mass was reported. Independent sample t-test and receiver operating characteristic (ROC) curve analysis were performed for comparison. Inter- and intra-observer reliability were assessed by the intraclass correlation coefficient (ICC) and quadratic kappa coefficient.

RESULTS

46 ovarian masses were included for final analysis. The three APTw SI values were higher in cystic regions of malignant ovarian masses compared with benign lesions (p<0.0001). ROC curve analysis showed no significant difference in diagnostic performance among three APTw SI values and the O-RADS MRI score (AUC: RS-APTw SI, 0.930; AVE-APTw SI, 0.927; AW-APTw SI, 0.935; O-RADS score, 0.937).

CONCLUSIONS

APTw MRI may be used as a noninvasive tool for the differentiation of benign and malignant ovarian masses based on the analysis of the cystic regions.

Synthetic MRI in breast cancer: differentiating benign from malignant lesions and predicting immunohistochemical expression status

To evaluate and compare the performance of synthetic magnetic resonance imaging (SyMRI) in classifying benign and malignant breast lesions and predicting the expression status of immunohistochemistry (IHC) markers. We retrospectively analysed 121 patients with breast lesions who underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and SyMRI before surgery in our hospital. DCE-MRI was used to assess the lesions, and then regions of interest (ROIs) were outlined on SyMRI (before and after enhancement), and apparent diffusion coefficient (ADC) maps to obtain quantitative values. After being grouped according to benign and malignant status, the malignant lesions were divided into high and low expression groups according to the expression status of IHC markers. Logistic regression was used to analyse the differences in independent variables between groups. The performance of the modalities in classification and prediction was evaluated by receiver operating characteristic (ROC) curves. In total, 57 of 121 lesions were benign, the other 64 were malignant, and 56 malignant lesions performed immunohistochemical staining. Quantitative values from proton density-weighted imaging prior to an injection of the contrast agent (PD-Pre) and T2-weighted imaging (T2WI) after the injection (T2-Gd), as well as its standard deviation (SD of T2-Gd), were valuable SyMRI parameters for the classification of benign and malignant breast lesions, but the performance of SyMRI (area under the curve, AUC = 0.716) was not as good as that of ADC values (AUC = 0.853). However, ADC values could not predict the expression status of breast cancer markers, for which SyMRI had excellent performance. The AUCs of androgen receptor (AR), estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2), p53 and Ki-67 were 0.687, 0.890, 0.852, 0.746, 0.813 and 0.774, respectively. SyMRI had certain value in distinguishing between benign and malignant breast lesions, and ADC values were still the ideal method. However, to predict the expression status of IHC markers, SyMRI had an incomparable value compared with ADC values.

Reproducibility and usefulness of quantitative apparent diffusion coefficient measurements for predicting program death-ligand 1 expression in nasopharyngeal carcinoma

BACKGROUND

Accurate assessment of programmed death-ligand 1 (PD-L1) expression status in nasopharyngeal carcinoma (NPC) before immunotherapy is crucial. We aimed to explore the reproducibility and usefulness of the quantitative apparent diffusion coefficient (ADC) measurements for predicting PD-L1expression status in NPC.

METHODS

We retrospectively recruited 134 NPC patients who underwent MRI scans and PD-L1 detection. A PD-L1 combined positive score (CPS) ≥ 20 was identified as high expression status. Patients were divide into two cohorts based on the MRI scanning devices, including a 1.5-T MRI cohort (n = 85, 44 PD-L1 high expression) and a 3.0-T MRI cohort (n = 49, 24 PD-L1 high expression). The mean ADC (ADCmean), minimum ADC (ADCmin) and maximal ADC (ADCmax) values were independently measured by two observers. The ADC measurement reproducibility was assessed by interclass correlation coefficients (ICC). The correlations between ADC parameters and CPS were analyzed by spearman's correlation coefficient (r), and the performance for PD-L1expression status prediction was assessed by the area under receiver operating characteristic curve (AUC).

RESULTS

The measurement reproducibility of ADCmean, ADCmin and ADCmax was good in the 1.5-T MRI cohort (ICC: 0.843-0.930) and 3.0-T MRI cohort (ICC: 0.929-0.960). The ADCmean, ADCmin, and ADCmax tended to inversely correlate with the CPS (r:-0.37 - -0.52 in the 1.5-T MRI cohort, and - 0.52 - -0.60 in the 3.0-T MRI cohort; P all < 0.01). The ADCmean, ADCmin and ADCmax yielded the AUC of 0.756 (95% CI: 0.651, 0.861), 0.689 (95% CI: 0.576, 0.802), and 0.733 (95%CI: 0.626, 0.839) in the 1.5-T MRI cohort and 0.820 (95%CI: 0.703, 0.937), 0.755 (95% CI: 0.616, 0.894), and 0.760 (95%CI: 0.627, 0.893) in the 3.0-T MRI cohort for predicting PD-L1 high expression status, respectively.

CONCLUSION

ADC measurements may act as a reproducible and feasible method to predict PD-L1 expression status in NPC.

CEST-MRI for body oncologic imaging: are we there yet?

Chemical exchange saturation transfer (CEST) MRI has gained recognition as a valuable addition to the molecular imaging and quantitative biomarker arsenal, especially for characterization of brain tumors. There is also increasing interest in the use of CEST-MRI for applications beyond the brain. However, its translation to body oncology applications lags behind those in neuro-oncology. The slower migration of CEST-MRI to non-neurologic applications reflects the technical challenges inherent to imaging of the torso. In this review, we discuss the application of CEST-MRI to oncologic conditions of the breast and torso (i.e., body imaging), emphasizing the challenges and potential solutions to address them. While data are still limited, reported studies suggest that CEST signal is associated with important histology markers such as tumor grade, receptor status, and proliferation index, some of which are often associated with prognosis and response to therapy. However, further technical development is still needed to make CEST a reliable clinical application for body imaging and establish its role as a predictive and prognostic biomarker.

Breast Amide Proton Transfer Imaging at 3 T: Diagnostic Performance and Association With Pathologic Characteristics

BACKGROUND

Amide proton transfer (APT) imaging has been increasingly applied in tumor characterization. However, its value in evaluating breast cancer remains undetermined.

PURPOSE

To assess the diagnostic performance of APT imaging in breast cancer and its association with prognostic histopathologic characteristics.

STUDY TYPE

Prospective.

SUBJECTS

Eighty-four patients with breast lesions.

FIELD STRENGTH/SEQUENCE

A 3.0 T/single-shot fast spin echo APT imaging.

ASSESSMENT

APTw signal in breast lesion was quantified. Lesion malignancy, T stage, grades, Ki-67 index, molecular biomarkers (estrogen receptor [ER] expression, progesterone receptor [PR] expression, human epidermal growth factor receptor [HER-2] expression), molecular subtypes (luminal A, luminal B, triple negative, and HER-2 enriched) were determined.

STATISTICAL TESTS

Student t-test, one-way analysis of variance, receiver operating characteristic analysis, and Pearson's correlation with P < 0.05 as statistical significance.

RESULTS

APTw signal was significantly higher in malignant lesions (1.55% ± 1.24%) than in benign lesions (0.54% ± 1.13%), and in grade III lesions than in grade II lesions (1.65% ± 0.84% vs. 0.96% ± 0.96%), and in T2- (1.57% ± 0.64%) and T3-stage lesions (1.54% ± 0.63%) than in T1-stage lesions (0.81% ± 0.64%) for invasive breast carcinoma of no special type. APTw signal significantly correlated with Ki-67 index (r = 0.364) but showed no significant difference in groups of ER (P = 0.069), PR (P = 0.069), HER-2 (P = 0.961), and among molecular subtypes (P = 0.073).

DATA CONCLUSION

APT imaging shows potential in differentiating breast lesion malignancy and associates with prognosis-related tumor grade, T stage, and proliferative activity.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

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.

Investigation of breast cancer microstructure and microvasculature from time-dependent DWI and CEST in correlation with histological biomarkers

We investigated the associations of time-dependent DWI, non-Gaussian DWI, and CEST parameters with histological biomarkers in a breast cancer xenograft model. 22 xenograft mice (7 MCF-7 and 15 MDA-MB-231) were scanned at 4 diffusion times [T_d = 2.5/5 ms with 11 b-values (0–600 s/mm²) and T_d = 9/27.6 ms with 17 b-values (0–3000 s/mm²), respectively]. The apparent diffusion coefficient (ADC) was estimated using 2 b-values in different combinations (ADC^0–600 using b = 0 and 600 s/mm² and shifted ADC [sADC^200–1500] using b = 200 and 1500 s/mm²) at each of those diffusion times. Then the change (Δ) in ADC/sADC between diffusion times was evaluated. Non-Gaussian diffusion and intravoxel incoherent motion (IVIM) parameters (ADC₀, the virtual ADC at b = 0; K, Kurtosis from non-Gaussian diffusion; f, the IVIM perfusion fraction) were estimated. CEST images were acquired and the amide proton transfer signal intensity (APT SI) were measured. The ΔsADC_9–27.6 (between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{sADC}}_{{9\,{\text{ms}}}}^{200{-}1500}$$\end{document}sADC9ms200-1500 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{sADC}}_{{27.6\,{\text{ms}}}}^{200{-}1500}$$\end{document}sADC27.6ms200-1500 and ΔADC_2.5_sADC_27.6 (between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{ADC}}_{{2.5\, {\text{ms}}}}^{0{-}600}$$\end{document}ADC2.5ms0-600 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{sADC}}_{{27.6\,{\text{ms}}}}^{200{-}1500}$$\end{document}sADC27.6ms200-1500) was significantly larger for MCF-7 groups, and ΔADC_2.5_sADC_27.6 was positively correlated with Ki67_max and APT SI. ADC₀ decreased significantly in MDA-MB-231 group and K increased significantly with T_d in MCF-7 group. APT SI and cellular area had a moderately strong positive correlation in MDA-MB-231 and MCF-7 tumors combined, and there was a positive correlation in MDA-MB-231 tumors. There was a significant negative correlation between APT SI and the Ki-67-positive ratio in MDA-MB-231 tumors and when combined with MCF-7 tumors. The associations of ΔADC_2.5_sADC_27.6 and API SI with Ki-67 parameters indicate that the T_d-dependent DW and CEST parameters are useful to predict the histological markers of breast cancers.

Amide Proton Transfer Weighted and Intravoxel Incoherent Motion Imaging in Evaluation of Prognostic Factors for Rectal Adenocarcinoma

Objectives

To analyze the value of amide proton transfer (APT) weighted and intravoxel incoherent motion (IVIM) imaging in evaluation of prognostic factors for rectal adenocarcinoma, compared with diffusion weighted imaging (DWI).

Materials and Methods

Preoperative pelvic MRI data of 110 patients with surgical pathologically confirmed diagnosis of rectal adenocarcinoma were retrospectively evaluated. All patients underwent high-resolution T₂-weighted imaging (T₂WI), APT, IVIM, and DWI. Parameters including APT signal intensity (APT SI), pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), and apparent diffusion coefficient (ADC) were measured in different histopathologic types, grades, stages, and structure invasion statuses. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy, and the corresponding area under the curves (AUCs) were calculated.

Results

APT SI, D and ADC values of rectal mucinous adenocarcinoma (MC) were significantly higher than those of rectal common adenocarcinoma (AC) ([3.192 ± 0.661%] vs. [2.333 ± 0.471%], [1.153 ± 0.238×10^-3 mm²/s] vs. [0.792 ± 0.173×10^-3 mm²/s], and [1.535 ± 0.203×10^-3 mm²/s] vs. [0.986 ± 0.124×10^-3 mm²/s], respectively; all P<0.001). In AC group, the APT SI and D values showed significant differences between low- and high-grade tumors ([2.226 ± 0.347%] vs. [2.668 ± 0.638%], and [0.842 ± 0.148×10^-3 mm²/s] vs. [0.777 ± 0.178×10^-3 mm²/s], respectively, both P<0.05). The D value had significant difference between positive and negative extramural vascular invasion (EMVI) tumors ([0.771 ± 0.175×10^-3 mm²/s] vs. [0.858 ± 0.151×10^-3 mm²/s], P<0.05). No significant difference of APT SI, D, D*, f or ADC was observed in different T stages, N stages, perineural and lymphovascular invasions (all P>0.05). The ROC curves showed that the AUCs of APT SI, D and ADC values for distinguishing MC from AC were 0.921, 0.893 and 0.995, respectively. The AUCs of APT SI and D values in distinguishing low- from high-grade AC were 0.737 and 0.663, respectively. The AUC of the D value for evaluating EMVI involvement was 0.646.

Conclusion

APT and IVIM were helpful to assess the prognostic factors related to rectal adenocarcinoma, including histopathological type, tumor grade and the EMVI status.

A Brief History and Future Prospects of CEST MRI in Clinical Non-Brain Tumor Imaging

Chemical exchange saturation transfer (CEST) MRI is a promising molecular imaging tool which allows the specific detection of metabolites that contain exchangeable amide, amine, and hydroxyl protons. Decades of development have progressed CEST imaging from an initial concept to a clinical imaging tool that is used to assess tumor metabolism. The first translation efforts involved brain imaging, but this has now progressed to imaging other body tissues. In this review, we summarize studies using CEST MRI to image a range of tumor types, including breast cancer, pelvic tumors, digestive tumors, and lung cancer. Approximately two thirds of the published studies involved breast or pelvic tumors which are sites that are less affected by body motion. Most studies conclude that CEST shows good potential for the differentiation of malignant from benign lesions with a number of reports now extending to compare different histological classifications along with the effects of anti-cancer treatments. Despite CEST being a unique 'label-free' approach with a higher sensitivity than MR spectroscopy, there are still some obstacles for implementing its clinical use. Future research is now focused on overcoming these challenges. Vigorous ongoing development and further clinical trials are expected to see CEST technology become more widely implemented as a mainstream imaging technology.

Amide proton transfer imaging in differentiation of type II and type I endometrial carcinoma: a pilot study

Purpose

This study aimed at evaluating the efficacy of amide proton transfer (APT) imaging in differentiation of type II and type I uterine endometrial carcinoma.

Materials and methods

Thirty-three patients diagnosed with uterine endometrial carcinoma, including 24 with type I and 9 with type II carcinomas, underwent APT imaging. Two readers evaluated the magnetization transfer ratio at 3.5 ppm [MTR_asym (3.5 ppm)] in each type of carcinoma. The average MTR_asym (APT_mean) and the maximum MTR_asym (APT_max) were analyzed. The receiver operating characteristic (ROC) curve analysis was performed.

Results

The APT_max was significantly higher in type II carcinomas than in type I carcinomas (reader1, p = 0.004; reader 2, p = 0.014; respectively). However, APT_mean showed no significant difference between type I and II carcinomas. Based on the results reported by reader 1, the area under the curve (AUC) pertaining to the APT_max for distinguishing type I from type II carcinomas was 0.826, with a cut-off, sensitivity, and specificity of 9.90%, 66.7%, and 91.3%, respectively. Moreover, based on the results reported by reader 2, the AUC was 0.750, with a cut-off, sensitivity, and specificity of 9.80%, 62.5%, and 87.5%, respectively.

Conclusion

APT imaging has the potential to determine the type of endometrial cancer.