Abstract
BACKGROUND: Conventional MRI mainly provides the morphologic information of tumors with low specificity, but its ability to distinguish between benign and malignant bone lesions is limited. Amide proton transfer (APT) is a novel MRI tool for detecting amide protons in free proteins and peptides. A few studies have reported on APT in tumors; however, studies on qualitative diagnosis of bone and soft tissue tumors are lacking. PURPOSE: This study aimed to investigate the use of APT and diffusion-weighted imaging (DWI) in distinguishing benign from malignant bone and soft tissue tumors. We evaluated the efficiency of APT and DWI sequences in diagnosing bone and soft tissue tumors. In pathology, the Ki-67 index expression reflects the tumor aggressiveness. Malignant tumors are often characterized by increased cell proliferation and increased Ki-67 expression. We assessed the correlation among APT, DWI, and Ki-67 expression. METHODS AND MATERIALS: The study enrolled 49 patients from June 2023 to January 2024. There were 20 patients in benign tumors, and 29 patients in malignant tumors. The site of the tumors were in the upper and lower extremities. The patients underwent MR scanning, and the diagnosis of bone or soft tissue tumors was confirmed by pathology. The APT, apparent diffusion coefficient (ADC), and exponential apparent diffusion coefficient (EADC) values were measured in the tumor areas. We used APT and DWI to distinguish benign from malignant bone and soft tissue tumors, and analyzed the correlation among APT, ADC, EADC, and Ki-67 expression. We compared the statistical differences in APT, ADC, and EADC values between benign and malignant tumors. The diagnostic efficiencies of APT, ADC, and EADC values and Ki-67 expression were evaluated using the receiver operating characteristic (ROC) curve. Meanwhile, Delong tests were conducted to evaluate the difference in the area under the ROC curve (AUC) among various parameters. The region of interest was outlined manually by 2 senior physicians. The intraclass correlation coefficient (ICC) was used to obtain the interobserver reprehensibility for measuring the APT value. RESULTS: The ICC was 0.848 in the interobserver agreement, and 95% confidence interval (CI) was 0.607-0.946. The APT value was 2.26% ± 1.07% in benign tumors, and 4.62% ± 1.43% in malignant tumors. The ADC value was 1.33% ± 0.4 ×10(-3) mm(2)/s in benign tumors, and 1.01% ± 0.55 ×10(-3) mm(2)/s in malignant tumors. The EADC value was 0.29 ± 0.13 in benign tumors, and 0.40 ± 0.17 in malignant tumors. Statistical differences were observed between benign and malignant tumors in APT (P <.001), ADC (P = .0032), and EADC (P = .019) values. In addition, APT exhibited a positive correlation with Ki-67 [Spearman correlation coefficient: r = 0.546, 95% confidence interval (CI): 0.217-0.762, P = .002]. In the ROC curve analysis for differentiating benign from malignant tumors, the sensitivity and specificity were respectively 72.41% and 95%, with an APT value of 3.6%;79.31% and 85%, with an ADC value of 1.047×10-(3) mm(2/)s; and 72.41% and 85%, with an EADC value of 0.34. Combining APT, ADC, and EADC, the sensitivity and specificity were 72.41% and 95%, respectively. The sensitivity and specificity were respectively 100% and 57.14% (P <.0001), with an AUC value of 0.794, for APT in assessing the Ki-67 expression; 70.59% and 85.71%, with an AUC value of 0.576, for ADC in assessing Ki-67 expression (P <.0001); and 70.59% and 85.71%, with an AUC value of 0.578 for EADC in assessing Ki-67 expression (P <.0001). CONCLUSIONS: The MR APT sequence has high qualitative diagnostic efficiency in differentiating benign and malignant bone and soft tissue tumors (when the cutoff value is 3.6%, the sensitivity is 72.41% and the specificity is 95%); the combination of APT, ADC and EADC can improve the diagnostics specificity; the APT value is positively correlated with Ki-67 expression (r = 0.546, P = .002), and can be used as a non-invasive imaging marker to evaluate the proliferative activity of bone and soft tissue tumors. These results need to be further verified by large-sample and multi-center studies.