T2-weighted Fat Suppression Research Articles

Radiomics of multi-parametric MRI for the prediction of lung metastasis in soft-tissue sarcoma: a feasibility study

PurposeTo investigate the value of multi-parametric MRI-based radiomics for preoperative prediction of lung metastases from soft tissue sarcoma (STS).MethodsIn total, 122 patients with clinicopathologically confirmed STS who underwent pretreatment T1-weighted contrast-enhanced (T1-CE) and T2-weighted fat-suppressed (T2FS) MRI scans were enrolled between Jul. 2017 and Mar. 2021. Radiomics signatures were established by calculating and selecting radiomics features from the two sequences. Clinical independent predictors were evaluated by statistical analysis. The radiomics nomogram was constructed from margin and radiomics features by multivariable logistic regression. Finally, the study used receiver operating characteristic (ROC) and calibration curves to evaluate performance of radiomics models. Decision curve analyses (DCA) were performed to evaluate clinical usefulness of the models.ResultsThe margin was considered as an independent predictor (p < 0.05). A total of 4 MRI features were selected and used to develop the radiomics signature. By incorporating the margin and radiomics signature, the developed nomogram showed the best prediction performance in the training (AUCs, margin vs. radiomics signature vs. nomogram, 0.609 vs. 0.909 vs. 0.910) and validation (AUCs, margin vs. radiomics signature vs. nomogram, 0.666 vs. 0.841 vs. 0.894) cohorts. DCA indicated potential usefulness of the nomogram model.ConclusionsThis feasibility study evaluated predictive values of multi-parametric MRI for the prediction of lung metastasis, and proposed a nomogram model to potentially facilitate the individualized treatment decision-making for STSs.

Magnetic resonance imaging based on radiomics for differentiating T1-category nasopharyngeal carcinoma from nasopharyngeal lymphoid hyperplasia: a multicenter study.

To investigate the role of magnetic resonance imaging (MRI) based on radiomics using T2-weighted imaging fat suppression (T2WI-FS) and contrast enhanced T1-weighted imaging (CE-T1WI) sequences in differentiating T1-category nasopharyngeal carcinoma (NPC) from nasopharyngeal lymphoid hyperplasia (NPH). This study enrolled 614 patients (training dataset: n = 390, internal validation dataset: n = 98, and external validation dataset: n = 126) of T1-category NPC and NPH. Three feature selection methods were used, including analysis of variance, recursive feature elimination, and relief. The logistic regression classifier was performed to construct the radiomics signatures of T2WI-FS, CE-T1WI, and T2WI-FS + CE-T1WI to differentiate T1-category NPC from NPH. The performance of the optimal radiomics signature (T2WI-FS + CE-T1WI) was compared with those of three radiologists in the internal and external validation datasets. Twelve, 15, and 15 radiomics features were selected from T2WI-FS, CE-T1WI, and T2WI-FS + CE-T1WI to develop the three radiomics signatures, respectively. The area under the curve (AUC) values for radiomics signatures of T2WI-FS + CE-T1WI and CE-T1WI were significantly higher than that of T2WI-FS (AUCs = 0.940, 0.935, and 0.905, respectively) for distinguishing T1-category NPC and NPH in the training dataset (Ps all < 0.05). In the internal and external validation datasets, the radiomics signatures based on T2WI-FS + CE-T1WI and CE-T1WI outperformed T2WI-FS with no significant difference (AUCs = 0.938, 0.925, and 0.874 for internal validation dataset and 0.932, 0.918, and 0.882 for external validation dataset; Ps > 0.05). The radiomics signature of T2WI-FS + CE-T1WI significantly performed better than three radiologists in the internal and external validation datasets. The MRI-based radiomics signature is meaningful in differentiating T1-category NPC from NPH and potentially helps clinicians select suitable therapy strategies.

MRI feature-based radiomics models to predict treatment outcome after stereotactic body radiotherapy for spinal metastases

ObjectiveThis study aimed to extract radiomics features from MRI using machine learning (ML) algorithms and integrate them with clinical features to build response prediction models for patients with spinal metastases undergoing stereotactic body radiotherapy (SBRT).MethodsPatients with spinal metastases who were treated using SBRT at our hospital between July 2018 and April 2023 were recruited. We assessed their response to treatment using the revised Response Evaluation Criteria in Solid Tumors (version 1.1). The lesions were categorized into progressive disease (PD) and non-PD groups. Radiomics features were extracted from T1-weighted image (T1WI), T2-weighted image (T2WI), and fat-suppression T2WI sequences. Feature selection involved intraclass correlation coefficients, minimal-redundancy-maximal-relevance, and least absolute shrinkage and selection operator methods. Thirteen ML algorithms were employed to construct the radiomics prediction models. Clinical, conventional imaging, and radiomics features were integrated to develop combined models. Model performance was evaluated using receiver operating characteristic (ROC) curve analysis, and the clinical value was assessed using decision curve analysis.ResultsWe included 194 patients with 142 (73.2%) lesions in the non-PD group and 52 (26.8%) in the PD group. Each region of interest generated 2264 features. The clinical model exhibited a moderate predictive value (area under the ROC curve, AUC = 0.733), while the radiomics models demonstrated better performance (AUC = 0.745–0.825). The combined model achieved the best performance (AUC = 0.828).ConclusionThe MRI-based radiomics models exhibited valuable predictive capability for treatment outcomes in patients with spinal metastases undergoing SBRT.Critical relevance statementRadiomics prediction models have the potential to contribute to clinical decision-making and improve the prognosis of patients with spinal metastases undergoing SBRT.Key points• Stereotactic body radiotherapy effectively delivers high doses of radiation to treat spinal metastases.• Accurate prediction of treatment outcomes has crucial clinical significance.• MRI-based radiomics models demonstrated good performance to predict treatment outcomes.Graphical

MRI radiomics-based evaluation of tuberculous and brucella spondylitis.

We analyzed magnetic resonance imaging (MRI) and radiomics labels from tuberculous spondylitis (TBS) and brucella spondylitis (BS) to build machine learning models that differentiate TBS from BS and culture-positive TBS (TBS(+)) from culture-negative TBS (TBS(-). This retrospective study included 56 patients with BS, 63 patients with TBS(+) and 71 patients with TBS(-). Radiomics labels were extracted from T2-weighted fat-suppression images. MRI labels were analyzed via logistic regression (LR); radiomics labels were analyzed by t-tests, SelectKBest, and least absolute shrinkage and selection operator (LASSO). Random forest (RF) and support vector machine (SVM) models were established using radiomics or joint (radiomics+MRI) labels. Models were evaluated by receiver operating characteristic curves, areas under the curve (AUCs), decision curve analysis (DCA), and Hosmer-Lemeshow tests. When joint-label models were used to compare BS vs TBS(+) and BS vs TBS(-) groups, SVM AUCs were 0.904 and 0.944, respectively, whereas RF AUCs were 0.950 and 0.947, respectively; these were higher than the AUCs of the MRI label-based LR model. DCA showed that radiomics-based machine learning models had a greater net benefit; Hosmer-Lemeshow tests demonstrated good prediction consistency for all models. Radiomics can help distinguish TBS from BS and TBS(+) from TBS(-).

Liver-spleen contrast standardization of gadolinium-ethoxybenzyl-diethylenetriamine penta-acetic acid-enhanced magnetic resonance imaging based on cross-calibration

Liver-spleen contrast in the hepatobiliary phase highly depends on the devices used for liver function tests. This study aimed to develop and validate a method to convert liver-spleen contrast data acquired with another device to reference liver-spleen contrast data, using the regression line of phantom contrasts (i.e., cross-calibration). As cohort studies, two-dimensional gradient echo images of T1-weighted fat-suppression in the hepatobiliary phase were retrospectively obtained and analyzed for a total of 126 patients who underwent gadolinium-ethoxybenzyl-diethylenetriamine penta-acetic acid-enhanced magnetic resonance imaging using four different magnetic resonance imaging scanner-coil combinations. The liver-spleen contrast measured from these images was converted into reference liver-spleen contrast using cross-calibration with purified water and gadolinium-ethoxybenzyl-diethylenetriamine penta-acetic acid phantoms of 0.06, 0.14, 0.27, 0.63, 1.37, and 2.82 mM/L. At this point, the error of the regression lines with phantom contrasts was assessed and corrected. Lastly, the liver-spleen and converted liver-spleen contrasts, which are the values before and after cross-calibration respectively, were compared with reference liver-spleen contrast in three cases using different coils and magnetic resonance imaging scanners from a reference device. Regarding the regression lines with phantom contrasts, the coefficient of determination was 0.99. Although regression lines with phantom contrasts tended to be 0.0105 lower in logarithmic contrasts than those with liver-spleen contrast, no significant difference was observed between the two lines (P=0.0612) by analysis of covariance. In the case of different coils, there was a significant difference between liver-spleen and reference liver-spleen contrasts (P<0.00001), but there was no significant difference between converted liver-spleen and reference liver-spleen contrasts (P=0.492). Moreover, the regression equation between converted liver-spleen and reference liver-spleen contrasts corresponded with an identity line. Likewise, in the two cases of different magnetic resonance imaging scanners, there was a significant difference between liver-spleen and reference liver-spleen contrast (both P<0.00001), but there was no significant difference between converted liver-spleen and reference liver-spleen contrast (P=0.923 and P=0.541). Cross-calibration using the precision and valid regression lines with phantom contrasts had high accuracy and utility.

Clinical-Radiomics Nomogram from T1W, T1CE, and T2FS MRI for Improving Diagnosis of Soft-Tissue Sarcoma.

To compare values of multiparametric magnetic resonance imaging (MRI) sequences and propose clinical-radiomics nomogram for diagnosis of soft-tissue sarcoma (STS). This study enrolled 148 patients from Dec. 2017 to Feb. 2021. All patients underwent T1-weighted (T1W), contrast-enhanced T1-weighted (T1CE), and T2-weighted fat-suppressed (T2FS) MRI scans. A total of 1967 radiomic features were extracted from the segmented regions of interest (ROIs) in each MRI sequence. Highly diagnostic radiomic features were selected with Mann-Whitney U test, elastic net, and Akaike's information criterion (AIC) based on MRI images. Logistical regression was used to build Rad scores. Clinical factors were analyzed using the chi-square test or Mann-Whitney U test. The performance of the Rad scores was judged using the area under the receiver operating characteristic area under the curve (ROC AUC), sensitivity, specificity, and accuracy. The nomogram was developed by integrating the Rad score and the most important clinical factor. By combining the three MRI sequences, the Rad-Com was developed consisting of twelve features selected by with Mann-Whitney U test, elastic net, and AIC: four from T1W, three from TICE, and five from T2FS MRI. The margin (P < 0.05) demonstrated a statistically significant difference between patients with benign and malignant soft-tissue tumors (STT). The nomogram was constructed by integrating the Rad-Com and margin, which yielded favorable diagnostic AUCs of 0.919 (sensitivity (Sen) = 0.784, specificity (Spe) = 0.936) and 0.913 (Sen = 0.923, Spe = 0.792) in the training and validation cohort. The proposed nomogram may have potential as a noninvasive marker for STS diagnosis.

Development and externally validate MRI-based nomogram to assess EGFR and T790M mutations in patients with metastatic lung adenocarcinoma.

This study aims to explore values of multi-parametric MRI-based radiomics for detecting the epidermal growth factor receptor (EGFR) mutation and resistance (T790M) mutation in lung adenocarcinoma (LA) patients with spinal metastasis. This study enrolled a group of 160 LA patients from our hospital (between Jan. 2017 and Feb. 2021) to build a primary cohort. An external cohort was developed with 32 patients from another hospital (between Jan. 2017 and Jan. 2021). All patients underwent spinal MRI (including T1-weighted (T1W) and T2-weighted fat-suppressed (T2FS)) scans. Radiomics features were extracted from the metastasis for each patient and selected to develop radiomics signatures (RSs) for detecting the EGFR and T790M mutations. The clinical-radiomics nomogram models were constructed with RSs and important clinical parameters. The receiver operating characteristics (ROC) curve was used to evaluate the predication capabilities of each model. Calibration and decision curve analyses (DCA) were constructed to verify the performance of the models. For detecting the EGFR and T790M mutation, the developed RSs comprised 9 and 4 most important features, respectively. The constructed nomogram models incorporating RSs and smoking status showed favorite prediction efficacy, with AUCs of 0.849 (Sen = 0.685, Spe = 0.885), 0.828 (Sen = 0.964, Spe = 0.692), and 0.778 (Sen = 0.611, Spe = 0.929) in the training, internal validation, and external validation sets for detecting the EGFR mutation, respectively, and with AUCs of 0.0.842 (Sen = 0.750, Spe = 0.867), 0.823 (Sen = 0.667, Spe = 0.938), and 0.800 (Sen = 0.875, Spe = 0.800) in the training, internal validation, and external validation sets for detecting the T790M mutation, respectively. Radiomics features from the spinal metastasis were predictive on both EGFR and T790M mutations. The constructed nomogram models can be potentially considered as new markers to guild treatment management in LA patients with spinal metastasis. • To our knowledge, this study was the first approach to detect the EGFR T790M mutation based on spinal metastasis in patients with lung adenocarcinoma. • We identified 13 MRI features that were strongly associated with the EGFR T790M mutation. • The proposed nomogram models can be considered as potential new markers for detecting EGFR and T790M mutations based on spinal metastasis.

Clinical and MRI features of sacral insufficiency fractures after radiotherapy in patients with cervical cancer

BackgroundTo determine the incidence, clinical and MRI features of sacral insufficiency fracture (SIF) after radiotherapy (RT) in patients with cervical cancer.MethodsOur study included 167 patients with cervical cancer after radiotherapy that underwent pelvic MRI for follow-up. MRIs included pre-enhanced T1-weighted, coronal fat-Suppressed T2-weighted (FS-T2W) and enhanced T1-weighted imaging. The clinical and MRI dates were reviewed. The gold standard of SIF was based on radiologic findings, clinical data and follow-up at least 12 months.Results28 patients (10.8%) with 47 sites were diagnosed with SIFs, including 9 patients with unilateral SIF and 19 patients with bilateral SIFs. The median age was 60 years (range 41–72 years), and 89.3% (25/28) of patients were postmenopausal. 64.3% (18/28) of patients were symptomatic, and 53.6% of patients (15/28) had concomitant pelvic fractures. The median interval time from RT to SIFs was 10 months (range 3–34 months). For the lesion-wise analysis based on all MR images, all lesions were detected by visualizing bone marrow edema patterns, and fracture lines were detected in 64.6% (31/47) of SIFs. No soft-tissue tumors were founded. For each MRI sequence analysis, coronal FS-T2WI detected the most bone marrow edema pattern and fracture line than T1WI or enhanced T1WI.ConclusionSIF is a common complication in cervical cancer after radiotherapy, which has some certain clinical and MRI features. Coronal FS-T2WI may be more useful to detect and characterize these fractures than other imaging sequences.

Multi-parametric MRI-based radiomics for the diagnosis of malignant soft-tissue tumor

PurposeTo develop and validate a multiparametric magnetic resonance imaging-based radiomics nomogram for differentiating malignant and benign soft-tissue tumors. MethodsA total of 91 patients with pathologically confirmed soft-tissue tumors were enrolled between January 2017 and October 2020. Forty-eight patients were consecutively enrolled between November 2020 and March 2022, as a time-independent cohort. All patients underwent contrast-enhanced T1-weighted and T2-weighted fat-suppression magnetic resonance scans at 3.0 T. Radiomics features were extracted and selected from the two modalities to develop the radiomics signature. Significant clinical/morphological characteristics were identified using a multivariate logistic regression analysis. The least absolute shrinkage and selection operator regression were applied to identify discriminative features. A clinical-radiomics nomogram was constructed based on clinical/morphological characteristics and radiomics features. Finally, the performance of the nomogram was validated using the receiver operating characteristic and decision curve analysis (DCA). ResultsSix features were selected to establish the combined RS. Size, margin, and peritumoral edema were identified as the most important clinical and morphological factors, respectively. The radiomics signature outperformed the clinical model in terms of AUC and sensitivity. The nomogram integrating the combined RS, size, margin, and peritumoral edema achieved favorable predictive efficacy, generating AUCs of 0.954 (95% confidence interval [CI]: 0.907–1.000, Sen = 0.861, Spe = 0.917), 0.962 (95% CI: 0.901–1.000, Sen = 0.944, Spe = 0.923), and 0.935 (95% CI: 0.871–0.998, Sen = 0.815, Spe = 0.952) in the training (n = 60), validation (n = 31) and time-independent (n = 48) cohorts, respectively. The DCA curve indicated good clinical usefulness of the nomogram. ConclusionsOur study demonstrated the clinical potential of multiparametric MRI-based radiomics in distinguishing malignant from benign soft-tissue tumors, which can be considered as a noninvasive tool for individual treatment management.

A MRI-based radiomics nomogram for evaluation of renal function in ADPKD

ObjectivesThis study is aimed to establish a fusion model of radiomics-based nomogram to predict the renal function of autosomal dominant polycystic kidney disease (ADPKD).MethodsOne hundred patients with ADPKD were randomly divided into training group (n = 69) and test group (n = 31). The radiomics features were extracted from T1-weighted fat suppression images (FS-T1WI) and T2-weighted fat suppression images (FS-T2WI). Decision tree algorithm was employed to build radiomics model to get radiomics signature. Then multivariate logistic regression analysis was used to establish the radiomics nomogram based on independent clinical factors, conventional MR imaging variables and radiomics signature. The receiver operating characteristic (ROC) analysis and Delong test were used to compare the performance of radiomics model and radiomics nomogram model, and the decision curve to evaluate the clinical application value of radiomics nomogram model in the evaluation of renal function in patients with ADPKD.ResultsFourteen radiomics features were selected to establish radiomics model. Based on FS-T1WI and FS-T2WI sequences, the radiomics model showed good discrimination ability in training group and test group [training group: (AUC) = 0.7542, test group (AUC) = 0.7417]. The performance of radiomics nomogram model was significantly better than that of radiomics model in all data sets [radiomics model (AUC) = 0.7505, radiomics nomogram model (AUC) = 0.8435, p value = 0.005]. The analysis of calibration curve and decision curve showed that radiomics nomogram model had more clinical application value.Conclusionradiomics analysis of MRI can be used for the preliminary evaluation and prediction of renal function in patients with ADPKD. The radiomics nomogram model shows better prediction effect in renal function evaluation, and can be used as a non-invasive renal function prediction tool to assist clinical decision-making.Trial Registration ChiCTR, ChiCTR2100046739. Registered 27 May 2021—retrospectively registered, http://www.ChiCTR.org.cn/showproj.aspx?proj=125955.Graphical abstract

Baseline MRI-based radiomics model assisted predicting disease progression in nasopharyngeal carcinoma patients with complete response after treatment

BackgroundAccurate pretreatment prediction for disease progression of nasopharyngeal carcinoma is key to intensify therapeutic strategies to high-risk individuals. Our aim was to evaluate the value of baseline MRI-based radiomics machine-learning models in predicting the disease progression in nasopharyngeal carcinoma patients who achieved complete response after treatment.MethodsIn this retrospective study, 171 patients with pathologically confirmed nasopharyngeal carcinoma were included. Using hold-out cross validation scheme (7:3), relevant radiomic features were selected with the least absolute shrinkage and selection operator method based on baseline T2-weighted fat suppression and contrast-enhanced T1-weighted images in the training cohort. After Pearson’s correlation analysis of selected radiomic features, multivariate logistic regression analysis was applied to radiomic features and clinical characteristics selection. Logistic regression analysis and support vector machine classifier were utilized to build the predictive model respectively. The predictive accuracy of the model was evaluated by ROC analysis along with sensitivity, specificity and AUC calculated in the validation cohort.ResultsA prediction model using logistic regression analysis comprising 4 radiomics features (HGLZE_T2H, HGLZE_T1, LDLGLE_T1, and GLNU_T1) and 5 clinical features (histology, T stage, N stage, smoking history, and age) showed the best performance with an AUC of 0.75 in the training cohort (95% CI: 0.66–0.83) and 0.77 in the validation cohort (95% CI: 0.64–0.90). The nine independent impact factors were entered into the nomogram. The calibration curves for probability of 3-year disease progression showed good agreement. The features of this prediction model showed satisfactory clinical utility with decision curve analysis.ConclusionsA radiomics model derived from pretreatment MR showed good performance for predicting disease progression in nasopharyngeal carcinoma and may help to improve clinical decision making.

Intramammary edema of invasive breast cancers on MRI T2-weighted fat suppression sequence: Correlation with molecular subtypes and clinical-pathologic prognostic factors

ObjectiveTo investigate the associations between different patterns of intramammary edema on preoperative 3.0 T magnetic resonance imaging (MRI) T2-weighted fat suppression sequence and invasive breast cancer molecular subtypes and clinical-pathologic prognostic factors. MethodsBetween May 2014 and December 2020, 191 patients with invasive breast cancer who had undergone preoperative MRI and mastectomy or breast-conserving surgery were retrospectively enrolled. The relationships between different patterns of intramammary edema and invasive breast cancer molecular subtypes and clinical-pathologic features were evaluated using the Student's t-test or Mann–Whitney U test and the χ2 test or Fisher's exact test. ResultsPatients with luminal B (HER2 positive), HER2-enriched and triple negative breast cancers respectively had different patterns of intramammary edema (P < 0.001). There was a significant association between intramammary edema and clinical-pathologic factors, including larger tumor size, higher Ki-67 index, lymph node metastasis and lymphovascular invasion (all P < 0.001). ConclusionsIntramammary edema may provide added values of predicting molecular subtypes and clinical-pathologic prognosis, enhancing the ability to individualize the treatment of patients with invasive breast cancer.

A diagnostic conundrum: Role of multimodality imaging in evaluating a rare intracardiac mass.

A 26-year-old male with history of generalized anxiety disorder presented to the emergency department with palpitations. He was diagnosed with supraventricular tachycardia; administration of adenosine restored normal sinus rhythm. Subsequent cardiovascular examinations and serial electrocardiograms (ECG) were unremarkable. Transthoracic echocardiogram identified a large, mid-septal mass that appeared to extend into both ventricular cavities (Panels A and B). The mass demonstrated perflutren uptake, suggesting hypervascularity (Panel B). No other significant abnormalities were identified. Cardiac magnetic resonance imaging demonstrated a hyperintense 2.5 cm × 1.8 cm mass (Supplementary data online, Video S1) within the interventricular septum on T2-weighted (T2W) images (Panel C), with hypointensity on T2W fat suppression imaging (Panel D). These findings were consistent with fat content and suggested intracardiac lipoma. Gated computed tomography revealed negative Hounsfield units (−71.33) and importantly, no features suggesting malignancy (Panel E). The collective...

Laparoscopic resection of schwannoma in the hepatoduodenal ligament: a case report

BackgroundThe occurrence of schwannomas in the hepatoduodenal ligament is rare, and its preoperative accurate diagnosis is difficult. Only few cases have been treated with laparoscopic surgery.Case presentationA 54-year-old man visited our hospital following abnormal abdominal computed tomography findings. He had no complaints, and his laboratory investigations were normal. Abdominal contrast-enhanced computed tomography revealed a tumor with enhancement at the margin of the hepatoduodenal ligament. The abdominal magnetic resonance imaging findings of the tumor showed hypointensity on the T1-weighted images and mixed hypointensity and hyperintensity on the T2-weighted fat-suppression images. Positron emission tomography showed localized accumulation of fludeoxyglucose only in the hepatoduodenal ligament tumor. The patient underwent laparoscopic tumor resection for accurate diagnosis. Histopathologically, the tumor was mainly composed of spindle cells, which were strongly positive for S-100 protein on immunohistochemical staining. The patient was discharged without any postoperative complications on day 5.ConclusionsComplete tumor resection is essential for schwannomas to avoid recurrence. Laparoscopic surgery is useful for schwannomas occurring in the hepatoduodenal ligament and can be performed safely by devising an appropriate surgical method.

MRI‐based radiomics analysis for predicting the EGFR mutation based on thoracic spinal metastases in lung adenocarcinoma patients

This study aims to develop and evaluate multi-parametric MRI-based radiomics for preoperative identification of epidermal growth factor receptor (EGFR) mutation, which is important in treatment planning for patients with thoracic spinal metastases from primary lung adenocarcinoma. A total of 110 patients were enrolled between January 2016 and March 2019 as a primary cohort. A time-independent validation cohort was conducted containing 52 patients consecutively enrolled from July 2019 to April 2021. The patients were pathologically diagnosed with thoracic spinal metastases from primary lung adenocarcinoma; all underwent T1-weighted (T1W), T2-weighted (T2W), and T2-weighted fat-suppressed (T2FS) MRI scans of the thoracic spinal. Handcrafted and deep learning-based features were extracted and selected from each MRI modality, and used to build the radiomics signature. Various machine learning classifiers were developed and compared. A clinical-radiomics nomogram integrating the combined rad signature and the most important clinical factor was constructed with receiver operating characteristic (ROC), calibration, and decision curves analysis (DCA) to evaluate the prediction performance. The combined radiomics signature derived from the joint of three modalities can effectively classify EGFR mutation and EGFR wild-type patients, with an area under the ROC curve (AUC) of 0.886 (95% confidence interval [CI]: 0.826-0.947, SEN=0.935, SPE=0.688) in the training group and 0.803 (95% CI: 0.682-0.924, SEN=0.700, SPE=0.818) in the time-independent validation group. The nomogram incorporating the combined radiomics signature and smoking status achieved the best prediction performance in the training (AUC=0.888, 95% CI: 0.849-0.958, SEN=0.839, SPE=0.792) and time-independent validation (AUC=0.821, 95% CI: 0.692-0.929, SEN=0.667, SPE=0.909) cohorts. The DCA confirmed potential clinical usefulness of our nomogram. Our study demonstrated the potential of multi-parametric MRI-based radiomics on preoperatively predicting the EGFR mutation. The proposed nomogram model can be considered as a new biomarker to guide the selection of individual treatment strategies for patients with thoracic spinal metastases from primary lung adenocarcinoma.

Multiparametric MRI-based Radiomics approaches on predicting response to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer.

To investigate the value of multiparametric MRI-based radiomics on predicting response to nCRT in patients with rectal cancer. This study enrolled 193 patients with pathologically confirmed LARC who received nCRT treatment between Apr. 2014 and Jun. 2018. All patients underwent baseline T1-weighted (T1W), T2-weighted (T2W) and T2-weighted fat-suppression (T2FS) MRI scans before neoadjuvant chemoradiotherapy. Radiomics features were extracted and selected from the MRI data to establish the radiomics signature. Important clinical predictors were identified by Mann-Whitney U test and Chi-square test. The nomogram integrating the radiomics signature and important clinical predictors was constructed using multivariate logistic regression. Prediction capabilities of each model were assessed with receiver operating characteristic (ROC) curve analysis. Performance of the nomogram was evaluated by its calibration and potential clinical usefulness. For the prediction of good response (GR) and pathologic complete response (pCR), the developed radiomics signature comprising 10 and 7 features, respectively, were significantly associated with the therapeutic response to nCRT. The nomogram incorporating the radiomics signature and important clinical predictors (CEA and CA19-9 for predicting GR; CEA, posttreatment length and posttreatment thickness for predicting pCR) achieved favorable prediction efficacy, with AUCs of 0.918 (95% confidence interval [CI]: 0.867-0.971, Sen = 0.972, Spe = 0.828) and 0.944 (95% CI: 0.891-0.997, Sen = 0.943, Spe = 0.828) in the training and validation cohort for predicting GR, respectively; with AUCs of 0.959 (95% CI: 0.927-0.991, Sen = 1.000, Spe = 0.833) and 0.912 (95% CI: 0.843-0.982, Sen = 1.000, Spe = 0.815) in the training and validation cohort for predicting pCR, respectively. Decision curve analysis confirmed potential clinical usefulness of our nomogram. This study demonstrated that the MRI-based radiomics nomogram is predictive of response to nCRT and can be considered as a promising tool for facilitating treatment decision-making for patients with LARC.

Accelerated single-shot T2-weighted fat-suppressed (FS) MRI of the liver with deep learning-based image reconstruction: qualitative and quantitative comparison of image quality with conventional T2-weighted FS sequence.

To compare the image quality of an accelerated single-shot T2-weighted fat-suppressed (FS) MRI of the liver with deep learning-based image reconstruction (DL HASTE-FS) with conventional T2-weighted FS sequence (conventional T2 FS) at 1.5 T. One hundred consecutive patients who underwent clinical MRI of the liver at 1.5 T including the conventional T2-weighted fat-suppressed sequence (T2 FS) and accelerated single-shot T2-weighted MRI of the liver with deep learning-based image reconstruction (DL HASTE-FS) were included. Images were reviewed independently by three blinded observers who used a 5-point confidence scale for multiple measures regarding the artifacts and image quality. Descriptive statistics and McNemar's test were used to compare image quality scores and percentage of lesions detected by each sequence, respectively. Intra-class correlation coefficient (ICC) was used to assess consistency in reader scores. Acquisition time for DL HASTE-FS was 51.23 +/ 10.1 s, significantly (p < 0.001) shorter than conventional T2-FS (178.9 ± 85.3 s). DL HASTE-FS received significantly higher scores than conventional T2-FS for strength and homogeneity of fat suppression; sharpness of liver margin; sharpness of intra-hepatic vessel margin; in-plane and through-plane respiratory motion; other ghosting artefacts; liver-fat contrast; and overall image quality (all, p < 0.0001). DL HASTE-FS also received higher scores for lesion conspicuity and sharpness of lesion margin (all, p < .001), without significant difference for liver lesion contrast (p > 0.05). Accelerated single-shot T2-weighted MRI of the liver with deep learning-based image reconstruction showed superior image quality compared to the conventional T2-weighted fat-suppressed sequence despite a 4-fold reduction in acquisition time. • Conventional fat-suppressed T2-weighted sequence (conventional T2 FS) can take unacceptably long to acquire and is the most commonly repeated sequence in liver MRI due to motion. • DL HASTE-FS demonstrated superior image quality, improved respiratory motion and other ghosting artefacts, and increased lesion conspicuity with comparable liver-to-lesion contrast compared to conventional T2FS sequence. • DL HASTE- FS has the potential to replace conventional T2 FS sequence in routine clinical MRI of the liver, reducing the scan time, and improving the image quality.

A posterior shiny-corner lesion of the tibia is observed in the early phase after medial meniscus posterior root tear.

Medial meniscus (MM) posterior root tear (PRT) results in joint overloading and degenerative changes in the knee, and pullout repair is recommended to prevent subsequent osteoarthritis. Diagnosing MMPRTis sometimes difficult, especially in the case of an incomplete tear. A posterior shiny-corner lesion (PSCL) is reported to be useful for diagnosis, although the association between MMPRT and PSCL is unknown. This study aimed to investigate the properties of PSCL, such as the location, volume, and duration from injury to the time of MRI (duration). We hypothesized that PSCL is observed in the early phase after the MMPRT onset. T2-weighted fat-suppression magnetic resonance imaging (MRI) was obtained from 55 patients with MMPRT preoperatively. The prevalence of the PSCL; giraffe neck, cleft, and ghost signs; severe MM extrusion (> 3mm); and the PSCL volume were evaluated. The PSCL lesion elliptical volume (mm3) was calculated by measuring the anteroposterior, transverse, and craniocaudal dimensions. PSCL was observed in 34 (62%) cases. The mean volume of the PSCL was 102.0mm3. A significantly shorter duration was observed in the PSCL-positive group (5.6weeks) than that in the PSCL-negative group (40.9weeks, P < 0.01), although no significant correlation was observed between the PSCL volume and duration. The sensitivity for the MMPRT was 90.5% when the cut-off duration value was 3weeks and 81.8% when the cut-off value was 8weeks. MRI examination may detect PSCL if it is performed early following MMPRT onset. Detecting PSCL may be useful in diagnosing MMPRT with high sensitivity.

Evaluating Heterogeneity of Primary Lung Tumor Using Clinical Routine Magnetic Resonance Imaging and a Tumor Heterogeneity Index.

ObjectiveTo improve the assessment of primary tumor heterogeneity in magnetic resonance imaging (MRI) of non-small cell lung cancer (NSCLC), we proposed a method using basic measurements from T1- and T2-weighted MRI.MethodsOne hundred and four NSCLC patients with different T stages were studied. Fifty-two patients were analyzed as training group and another 52 as testing group. The ratios of standard deviation (SD)/mean signal value of primary tumor from T1-weighted (T1WI), T1-enhanced (T1C), T2-weighted (T2WI), and T2 fat suppression (T2fs) images were calculated. In the training group, correlation analyses were performed between the ratios and T stages. Then an ordinal regression model was built to generate the tumor heterogeneous index (THI) for evaluating the heterogeneity of tumor. The model was validated in the testing group.ResultsThere were 11, 32, 40, and 21 patients with T1, T2, T3, and T4 disease, respectively. In the training group, the median SD/mean on T1WI, T1C, T2WI, and T2fs sequences was 0.11, 0.19, 0.16, and 0.15 respectively. The SD/mean on T1C (p=0.003), T2WI (p=0.000), and T2fs sequences (p=0.002) correlated significantly with T stages. Patients with more advanced T stage showed higher SD/mean on T2-weighted, T2fs, and T1C sequences. The median THI in the training group was 2.15. THI correlated with T stage significantly (p=0.000). In the testing group, THI was also significantly related to T stages (p=0.001). Higher THI had relevance to more advanced T stage.ConclusionsThe proposed ratio measurements and THI based on MRI can serve as functional radiomic markers that correlated with T stages for evaluating heterogeneity of lung tumors.

Self-derived organ attention for unpaired CT-MRI deep domain adaptation based MRI segmentation

To develop and evaluate a deep learning method to segment parotid glands from MRI using unannotated MRI and unpaired expert-segmented CT datasets. We introduced a new self-derived organ attention deep learning network for combined CT to MRI image-to-image translation (I2I) and MRI segmentation, all trained as an end-to-end network. The expert segmentations available on CT scans were combined with the I2I translated pseudo MR images to train the MRI segmentation network. Once trained, the MRI segmentation network alone is required. We introduced an organ attention discriminator that constrains the CT to MR generator to synthesize pseudo MR images that preserve organ geometry and appearance statistics as in real MRI. The I2I translation network training was regularized using the organ attention discriminator, global image-matching discriminator, and cycle consistency losses. MRI segmentation training was regularized by using cross-entropy loss. Segmentation performance was compared against multiple domain adaptation-based segmentation methods using the Dice similarity coefficient (DSC) and Hausdorff distance at the 95th percentile (HD95). All networks were trained using 85 unlabeled T2-weighted fat suppressed (T2wFS) MRIs and 96 expert-segmented CT scans. Performance upper-limit was based on fully supervised MRI training done using the 85 T2wFS MRI with expert segmentations. Independent evaluation was performed on 77 MRIs never used in training. The proposed approach achieved the highest accuracy (left parotid: DSC 0.82 ± 0.03, HD95 2.98 ± 1.01 mm; right parotid: 0.81 ± 0.05, HD95 3.14 ± 1.17 mm) compared to other methods. This accuracy was close to the reference fully supervised MRI segmentation (DSC of 0.84 ± 0.04, a HD95 of 2.24 ± 0.77 mm for the left parotid, and a DSC of 0.84 ± 0.06 and HD95 of 2.32 ± 1.37 mm for the right parotid glands).