Value of orthogonal axial MR images in preoperative T staging of gastric cancer.

PURPOSE: To assess the value of orthogonal axial images (OAI) of MRI in gastric cancer T staging. METHODS: This retrospective study enrolled 133 patients (median age, 63 [range, 24-85] years) with gastric adenocarcinoma who underwent both CT and MRI followed by surgery. MRI lacking or incorporating OAI and CT images were evaluated, respectively. Diagnostic performance (accuracy, sensitivity, and specificity) for each T stage, overall diagnostic accuracy and rates of over- and understaging were quantified employing pathological T stage as a reference standard. The McNemar's test was performed to compare the overall accuracy. RESULTS: Among patients with pT1-pT4 disease, MRI with OAI (accuracy: 88.7-94.7%, sensitivity: 66.7-93.0%, specificity: 91.5-100.0%) exhibited superior diagnostic performance compared to MRI without OAI (accuracy: 81.2-88.7%, sensitivity: 46.2-83.1%, specificity: 85.5-99.1%) and CT (accuracy: 88.0-92.5%, sensitivity: 53.3-90.1%, specificity: 88.7-98.1%). The overall accuracy of MRI with OAI was significantly higher (83.5%) than that of MRI without OAI (67.7%) (p < .001). However, there was no significant difference in the overall accuracy of MRI with OAI and CT (78.9%) (p = .35). The over- and understaging rates of MRI with OAI (12.0, 4.5%) were lower than those of MRI without OAI (21.8, 10.5%) and CT (12.8, 8.3%). CONCLUSION: OAI play a pivotal role in the T staging of gastric cancer. MRI incorporating OAI demonstrated commendable performance for gastric cancer T-staging, with a slight tendency toward its superiority over CT.

Habitat Radiomics Based on MRI for Predicting Platinum Resistance in Patients with High-Grade Serous Ovarian Carcinoma: A Multicenter Study.

RATIONALE AND OBJECTIVES: This study aims to explore the feasibility of MRI-based habitat radiomics for predicting response of platinum-based chemotherapy in patients with high-grade serous ovarian carcinoma (HGSOC), and compared to conventional radiomics and deep learning models. MATERIALS AND METHODS: A retrospective study was conducted on HGSOC patients from three hospitals. K-means algorithm was used to perform clustering on T2-weighted images (T2WI), contrast-enhanced T1-weighted images (CE-T1WI), and apparent diffusion coefficient (ADC) maps. After feature extraction and selection, the radiomics model, habitat model, and deep learning model were constructed respectively to identify platinum-resistant and platinum-sensitive patients. A nomogram was developed by integrating the optimal model and clinical independent predictors. The model performance and benefit was assessed using the area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), and integrated discrimination improvement (IDI). RESULTS: A total of 394 eligible patients were incorporated. Three habitats were clustered, a significant difference in habitat 2 (weak enhancement, high ADC values, and moderate T2WI signal) was found between the platinum-resistant and platinum-sensitive groups (P < 0.05). Compared to the radiomics model (0.640) and deep learning model (0.603), the habitat model had a higher AUC (0.710). The nomogram, combining habitat signatures with a clinical independent predictor (neoadjuvant chemotherapy), yielded a highest AUC (0.721) among four models, with positive NRI and IDI. CONCLUSION: MRI-based habitat radiomics had the potential to predict response of platinum-based chemotherapy in patients with HGSOC. The nomogram combining with habitat signature had a best performance and good model gains for identifying platinum-resistant patients.

MRI-detected tumor deposits in cT3 and cT4 rectal cancer following neoadjuvant chemoradiotherapy.

OBJECTIVES: To evaluate the identification of tumor deposits (TDs) and the prognostic significance of an MRI tumor regression grade for TDs in patients with rectal cancer treated with neoadjuvant chemoradiotherapy (nCRT). METHODS: Ninety-one patients with cT3 or cT4 rectal cancer who underwent surgery following nCRT between August 2014 and June 2020 were retrospectively analyzed. Changes in pre-nCRT MRI-detected TDs (mrTDs) were described as mrTD regression grade. The diagnostic performance of post-nCRT MRI-detected TDs (ymrTDs) was compared with histopathological reference standard. The correlation between ymrTDs, mrTD regression grade, and disease-free survival (DFS) was assessed. RESULTS: The sensitivity and specificity of ymrTDs were 88.00% and 89.39%, respectively. The area under the receiver operating characteristic curve was 0.887 (95% confidence interval [CI]: 0.803-0.944). The 3-year DFS of patients with positive ymrTDs was significantly lower than of the negative group (44.83% vs 82.73%, p < 0.001). The 3-year DFS was 33.33% for patients with poor regression of mrTDs following nCRT and 55.56% for those with moderate regression, compared to 69.23% in good responders and 83.97% in patients without mrTDs (p < 0.001). On multivariable Cox regression, mrTD regression grade was the only independent MRI factor associated with DFS (p = 0.042). CONCLUSIONS: Diagnostic performance of ymrTDs was moderate. The mrTD regression grade was independently correlated with DFS, which may have a prognostic implication for treatment and follow-up. CLINICAL RELEVANCE STATEMENT: Patients with poor regression of MRI-detected tumor deposits may benefit from more aggressive treatments, such as chemoradiation therapy plus induction or consolidation chemotherapy. KEY POINTS: • MRI provides a preoperative and noninvasive way to visualize tumor deposits (TDs) after neoadjuvant chemoradiotherapy (nCRT). • Post-nCRT MRI-detected TDs are a poor prognostic marker in cT3 and cT4 rectal cancer patients. • The regression of MRI-detected TDs after nCRT is associated with an improved disease-free survival.

MRI for nodal restaging after neoadjuvant therapy in rectal cancer with histopathologic comparison.

BACKGROUND: After neoadjuvant therapy, most of the lymph nodes (LNs) will shrink and disappear in patients with rectal cancer. However, LNs that are still detectable on MRI carry a risk of metastasis. This study aimed to evaluate the performance of the European Society of Gastrointestinal and Abdominal Radiology (ESGAR) criterion (short-axis diameter ≥ 5 mm) in diagnosing malignant LNs in patients with rectal cancer after neoadjuvant therapy, and whether nodal morphological characteristics (including shape, border, signal homogeneity, and enhancement homogeneity) could improve the diagnostic efficiency for LNs ≥ 5 mm. METHODS: This retrospective study included 90 patients with locally advanced rectal cancer who underwent surgery after neoadjuvant therapy and performed preoperative MRI. Two radiologists independently measured the short-axis diameter of LNs and evaluated the morphological characteristics of LNs ≥ 5 mm in consensus. With a per node comparison with histopathology as the reference standard, a ROC curve was performed to evaluate the diagnostic performance of the size criterion. For categorical variables, either a χ2 test or Fisher's exact test was used. RESULTS: A total of 298 LNs were evaluated. The AUC for nodal size in determining nodal status was 0.81. With a size cutoff value of 5 mm, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy were 65.9%, 87.0%, 46.8%, 93.6% and 83.9%, respectively. No significant differences were observed in any of the morphological characteristics between benign and malignant LNs ≥ 5 mm (all P > 0.05). CONCLUSIONS: The ESGAR criterion demonstrated moderate diagnostic performance in identifying malignant LNs in patients with rectal cancer after neoadjuvant therapy. It was effective in determining the status of LNs < 5 mm but not for LNs ≥ 5 mm, and the diagnostic efficiency could not be improved by considering nodal morphological characteristics.

Development and Validation of an MRI-based Radiomics Nomogram for Assessing Deep Myometrial Invasion in Early Stage Endometrial Adenocarcinoma.

RATIONALE AND OBJECTIVES: To establish a radiomics nomogram for detecting deep myometrial invasion (DMI) in early stage endometrioid adenocarcinoma (EAC). MATERIALS AND METHODS: A total of 266 patients with stage I EAC were divided into training (n = 185) and test groups (n = 81). Logistic regression were used to identify clinical predictors. Radiomics features were extracted and selected from multiparameter MR images. The important clinical factors and radiomics features were integrated into a nomogram. A receiver operating characteristic curve was used to evaluate the nomogram. Two radiologists evaluated MR images with or without the help of the nomogram to detect DMI. The clinical benefit of using the nomogram was evaluated by decision curve analysis (DCA) and by calculating net reclassification index (NRI) and integrated discrimination index (IDI). RESULTS: Age and CA125 were independent clinical predictors. The area under the curves of the clinical parameters, radiomics signature and nomogram in evaluating DMI were 0.744, 0.869 and 0.883, respectively. The accuracies of the two radiologists increased from 79.0% and 80.2% to 90.1% and 92.5% when they used the nomogram. The NRI of the two radiologists were 0.262 and 0.318, and the IDI were 0.322 and 0.405. According to DCA, the nomogram showed a higher net benefit than the radiomics signature or unaided radiologists. Cross-validation showed the outcome of radiomics analysis may not be influenced by changes in field strength. CONCLUSION: The radiomics nomogram based on radiomics features and clinical factors can help radiologists evaluate DMI and improve their accuracy in predicting DMI in early stage EAC.

Different multiparametric MRI-based radiomics models for differentiating stage IA endometrial cancer from benign endometrial lesions: A multicenter study.

Purpose: The aim of this study was to evaluate the value of different multiparametric MRI-based radiomics models in differentiating stage IA endometrial cancer (EC) from benign endometrial lesions. Methods: The data of patients with endometrial lesions from two centers were collected. The radiomics features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) map, and late contrast-enhanced T1-weighted imaging (LCE-T1WI). After data dimension reduction and feature selection, nine machine learning algorithms were conducted to determine which was the optimal radiomics model for differential diagnosis. The univariate analyses and logistic regression (LR) were performed to reduce valueless clinical parameters and to develop the clinical model. A nomogram using the radscores combined with clinical parameters was developed. Two integrated models were obtained respectively by the ensemble strategy and stacking algorithm based on the clinical model and optimal radiomics model. The area under the curve (AUC), clinical decisive curve (CDC), net reclassification index (NRI), and integrated discrimination index (IDI) were used to evaluate the performance and clinical benefits of the models. Results: A total of 371 patients were incorporated. The LR model was the optimal radiomics model with the highest average AUC (0.854) and accuracy (0.802) in the internal and external validation groups (AUC = 0.910 and 0.798, respectively), and outperformed the clinical model (AUC = 0.739 and 0.592, respectively) or the radiologist (AUC = 0.768 and 0.628, respectively). The nomogram (AUC = 0.917 and 0.802, respectively) achieved better discrimination performance than the optimal radiomics model in two validation groups. The stacking model (AUC = 0.915) and ensemble model (AUC = 0.918) had a similar performance compared with the nomogram in the internal validation group, whereas the AUCs of the stacking model (AUC = 0.792) and ensemble model (AUC = 0.794) were lower than those of the nomogram and radiomics model in the external validation group. According to the CDC, NRI, and IDI, the optimal radiomics model, nomogram, stacking model, and ensemble model achieved good net benefits. Conclusions: Multiparametric MRI-based radiomics models can non-invasively differentiate stage IA EC from benign endometrial lesions, and LR is the best machine learning algorithm. The nomogram presents excellent and stable diagnostic efficiency.

Preliminary Application of Magnetization Transfer Imaging in the Study of Normal Uterus and Uterine Lesions.

Purpose: The aim of this study is to evaluate the utility of magnetization transfer (MT) imaging in the study of normal uterus and common uterine lesions. Methods: This prospective study enrolled 160 consecutive patients with suspected uterine lesions. MT ratio (MTR) map was obtained by pelvic MT imaging on a 3.0T MRI scanner. Patients confirmed by pathology were divided into microscopic lesion group and lesion group, according to whether the maximum diameter of the lesion was less than 5 mm. After evaluating and eliminating patients with poor image quality by a three-point Likert scale, MTR values of lesions and normal endometrium, myometrium, and cervix were independently measured on the MTR map by two radiologists. Inter-reader agreement was evaluated. MTR values were compared among different uterine lesions and normal uterine structures using the Mann-Whitney U test with Bonferroni correction. Receiver operating characteristic curve was performed. The correlations between age and MTR values were explored by Pearson correlation analyses. Results: A total of 96 patients with 121 uterine lesions in the lesion group and 41 patients in the microscopic lesion group were measured. The MTR values among normal endometrium, myometrium, and cervix were statistical significant differences (P < 0.05). There were significant differences between endometrial cancer and normal endometrium and between cervical cancer and normal cervix (both P ≤ 0.001). Area under the curve (AUC) for diagnosing endometrial and cervical cancer were 0.73 and 0.86. Myometrial lesions had significantly higher MTR values than endometrial lesions and cervical cancer (both P < 0.001), and the AUC for differentiating myometrial lesions from them were 0.89 and 0.94. MTR values of endometrial cancer were significantly higher than those of cervical cancer (P = 0.02). There was a critical correlation between age and MTR values in endometrial cancer (r = 0.81, P = 0.04). Conclusions: MTR values showed significant differences among normal uterine structures. It was valuable for diagnosing and differentiating uterine cancer. MTR values could differentiate myometrial lesions from endometrial or cervical lesions.

Multiparameter Magnetic Resonance Quantitative Evaluation of Pancreatic Cancer with Vascular Invasion.

Objective: To analyze the value of multiparameter magnetic resonance (mpMRI) in the diagnosis of pancreatic cancer with vascular invasion from two aspects: morphology and function, so as to provide a reliable diagnostic basis for preparing the clinical treatment plans. Methods: Totally 31 case data of pancreatic cancer patients diagnosed in our hospital from January 2020 to March 2021 were enrolled in this study. All patients underwent multiparameter magnetic resonance imaging (T1WI, T2WI, DKI, and DCE-MRI) before surgery, and then all patients underwent pancreatic cancer surgery. Two experienced radiologists analyzed these obtained images according to the image reports and combined them with the pathological results. Taking pathological results as gold standard, the sensitivity, specificity, and accuracy of quantitative parameters derived from T2WI, DKI, DCE, T2WI + DKI, T2WI + DCE, and T2WI + DKI + DCE for the diagnostic capabilities of pancreatic cancer vascular invasion were calculated using diagnostic laboratory methods. Kappa consistency test was used to estimate the consistency of two radiologists' diagnosis and analysis. The images obtained by DKI sequence were input into the postprocessing software MITK-Diffusion v2014.10.02, The images obtained from DCE sequence were processed by the Tissue 4D software on the Siemens syngo via workstation to calculate and analyze each tumor ROI's MD, MK values from DKI, and K trans, K ep, V e values from DCE. Independent samples t-test was used to compare the parameters of pancreatic cancer with vascular invasion group (16 cases) and nonvascular invasion group (15 cases). ROC curve was used to analyze the efficacy of each parameter in diagnosing pancreatic cancer vascular invasion. Results: The sensitivity, specificity, and accuracy of T2WI were 62.5%, 53.5%, and 58.1%; those of DKI were 56.3%, 60.0%, and 58.1%; those of DCE were 68.8%, 60.0%, and 64.5%; those of T2WI + DKI were 68.8%, 66.7%, and 67.7%; those of T2WI + DCE were 75.0%, 66.7%, and 71.1%; those of T2WI + DKI + DCE were 81.2%, 73.3%, and 77.4%, respectively. These two diagnostic radiologists analyzed image data with good consistency, Kappa = 0.834. MD, MK, K trans, K ep, and V e were significantly different between the vascular invasion group and the nonvascular invasion group (p < 0.05). Each parameter's AUC of ROC curve was 0.773, 0.829, 0.794, 0.802, and 0.846 (p < 0.05). Take MD = 2.285 × 10-3 mm/s2, MK = 0.72, K trans = 0.103, K ep = 0.337, and V e = 0.353 as thresholds; the sensitivity of these parameters to diagnose vascular invasion of pancreatic cancer was 73.33%, 75%, 87.5%, 68.8%, and 68.8%. The specificity of them was 75%, 80%, 60%, 86.7%, and 86.7%, respectively. Conclusion: The combined analysis of T2WI + DKI + DCE can improve the specificity and accuracy of diagnostic efficiency of vascular invasion of pancreatic cancer and provide an important diagnostic basis for pancreatic cancer's preoperative treatment.

Metastatic lymph node calcification in rectal cancer: comparison of CT and high-resolution MRI.

Calcification causes mixed signal intensity in the lymph node (LN) on high-resolution magnetic resonance imaging (MRI), which is a strong indicator of regional LN metastasis in rectal cancer. Calcified metastatic LNs in rectal cancer commonly display scattered fine punctate calcifications to varying degrees on computed tomography (CT). On high-resolution MRI, the calcifications manifest a patchy area of signal loss in corresponding calcified area that is larger than on CT. It is necessary to recognize the appearance of metastatic LN calcifications on high-resolution MRI in rectal cancer because it is the primary imaging method for local staging in rectal cancer. This pictorial essay aims to introduce an important imaging finding that can contribute to the diagnosis of LN metastasis by illustrating features and differences between CT and high-resolution MRI of metastatic LN calcifications in rectal cancer.

Ultrasmall hybrid protein-copper sulfide nanoparticles for targeted photoacoustic imaging of orthotopic hepatocellular carcinoma with a high signal-to-noise ratio.

Although photoacoustic imaging combined with second near infrared (NIR II) molecular probes for tumor diagnosis has drawn tremendous attention during the past few decades, the targeted photoacoustic imaging of orthotopic hepatocellular carcinoma (HCC) still remains a challenge due to high liver vascularization and non-specificity of probes in liver tumors. Herein, we report on cyclic arginine-glycine-aspartic acid (cRGD) peptide conjugated ultrasmall CuS nanoparticles (CuS@BSA-RGD NPs) which encapsulate bovine serum albumin (BSA) and possess high optical absorption at 1064 nm. The encapsulation of BSA results in great biocompatibility of CuS@BSA-RGD NPs along with excellent photostability and physiological stability. The cRGD conjugation enables the improvement of tumor uptake of CuS@BSA-RGD NPs by virtue of its positive tumor cell targeting capability. The efficient accumulation of CuS@BSA-RGD NPs in the tumor over time after intravenous administration to orthotopic HCC bearing mice was achieved, which resulted in highly sensitive photoacoustic visualization of the tumor region. Toxicity studies indicate that CuS@BSA-RGD NPs exhibited negligible systemic toxicity in vivo. The results demonstrate that the CuS@BSA-RGD NPs might hold great promise for future imaging and diagnosis of cancer.