A nomogram model for predicting intramyocardial hemorrhage post-PCI based on SYNTAX score and clinical features.

OBJECTIVE: The aim of this study is to develop a nomogram model for predicting the occurrence of intramyocardial hemorrhage (IMH) in patients with Acute Myocardial Infarction (AMI) following Percutaneous Coronary Intervention (PCI). The model is constructed utilizing clinical data and the SYNTAX Score (SS), and its predictive value is thoroughly evaluated. METHODS: A retrospective study was conducted, including 216 patients with AMI who underwent Cardiac Magnetic Resonance (CMR) within a week post-PCI. Clinical data were collected for all patients, and their SS were calculated based on coronary angiography results. Based on the presence or absence of IMH as indicated by CMR, patients were categorized into two groups: the IMH group (109 patients) and the non-IMH group (107 patients). The patients were randomly divided in a 7:3 ratio into a training set (151 patients) and a validation set (65 patients). A nomogram model was constructed using univariate and multivariate logistic regression analyses. The predictive capability of the model was assessed using Receiver Operating Characteristic (ROC) curve analysis, comparing the predictive value based on the area under the ROC curve (AUC). RESULTS: In the training set, IMH post-PCI was observed in 78 AMI patients on CMR, while 73 did not show IMH. Variables with a significance level of P < 0.05 were screened using univariate logistic regression analysis. Twelve indicators were selected for multivariate logistic regression analysis: heart rate, diastolic blood pressure, ST segment elevation on electrocardiogram, culprit vessel, symptom onset to reperfusion time, C-reactive protein, aspartate aminotransferase, lactate dehydrogenase, creatine kinase, creatine kinase-MB, high-sensitivity troponin T (HS-TnT), and SYNTAX Score. Based on multivariate logistic regression results, two independent predictive factors were identified: HS-TnT (Odds Ratio [OR] = 1.61, 95% Confidence Interval [CI]: 1.21-2.25, P = 0.003) and SS (OR = 2.54, 95% CI: 1.42-4.90, P = 0.003). Consequently, a nomogram model was constructed based on these findings. The AUC of the nomogram model in the training set was 0.893 (95% CI: 0.840-0.946), and in the validation set, it was 0.910 (95% CI: 0.823-0.970). Good consistency and accuracy of the model were demonstrated by calibration and decision curve analysis. CONCLUSION: The nomogram model, constructed utilizing HS-TnT and SS, demonstrates accurate predictive capability for the risk of IMH post-PCI in patients with AMI. This model offers significant guidance and theoretical support for the clinical diagnosis and treatment of these patients.

Delays in first medical contact to primary interventional therapy and left ventricular remodelling in ST-segment elevation myocardial infarction.

BACKGROUND: Early reperfusion and early evaluation of adverse cardiovascular events have become important aspects of treatment for ST-segment elevation myocardial infarction post-primary percutaneous coronary intervention (PPCI). However, emergency medical service (EMS) delays always occur, especially in developing countries. AIMS: The aim of this study was to investigate the impact of EMS delays on short-term predictions of the severity of myocardial injury in STEMI patients after PPCI. METHODS: A total of 151 STEMI patients who underwent successful PPCI and two postoperative cardiac magnetic resonance (CMR) imaging examinations (1 week and 4 months postoperatively) were retrospectively analysed. CMR cine and late gadolinium enhancement (LGE) images were analysed to evaluate left ventricular (LV) function, LV global longitudinal peak strain (GLS) and scar characteristics. The time from first medical contact to balloon (FMC2B) and door-to-balloon (D2B) time, expressed in minutes, were recorded and compared with the recommended timelines. Unadjusted and multivariable analyses were used to assess the impact of EMS delays on short-term left ventricular remodelling (ALVR). RESULTS: EMS delays (FMC2B time > 90 min) led to larger infarct size (IS) and microcirculation obstruction (MVO) and poor recovery of the LV ejection fraction and GLS (all p < 0.05). Logistic regression analysis showed that an FMC2B time > 90 min (p = 0.028, OR = 2.661, 95% CI 1.112-6.367) and baseline IS (p = 0.016, OR = 1.079, 95% CI 1.015-1.148) were independent predictors of short-term ALVR. CONCLUSION: Delays in FMC2B time were strongly associated with short-term ALVR; shorter ischaemic times may improve the cardiac function and prognosis of patients.

Associations between pericarotid fat density and image-based risk characteristics of carotid plaque.

OBJECTIVES: In this study, we aimed to evaluate the associations between pericarotid fat density (PFD) and various risk characteristics of carotid plaque. METHODS: We retrospectively evaluated consecutive patients who were subjected to both high-resolution MRI and carotid artery CT angiography CTA at our institution between January 2016 and April 2021. The section of the carotid artery with the most severe lumen stenosis was selected from each patient for analysis. Two separated regions of interest (ROI) (each with an area of 2.5 mm2 and located at least 1 mm from the outer margin of the carotid artery wall) were defined in the perivascular fat tissue. The mean value of PFD (mean HU) was measured on the plaque side and the same axial non-plaque side. Then, the bilateral difference (D-value HU) was calculated (plaque side mean HU minus non-plaque side mean HU). According to carotid plaque risk characteristics (American Heart Association VI type [AHA VI], intraplaque hemorrhage [IPH], thinning and/or rupture of the fibrous cap [TRFC], lipid-rich necrotic core [LRNC], and calcification [CA]), the associations between PFD and five different risk characteristic subgroups were analyzed. The Student's t-test, Mann-Whitney U test, and Chi-square test were used to compare differences between different risk subgroups. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive efficacy of PFD for carotid plaque risk characteristics. P < 0.05 was considered statistically significant. RESULTS: A total of 71 eligible patients (mean age 61.25 ± 10.35 years, 57 male) were examined in this study. For the plaque side and the non-plaque side, the mean PFD values were -36.25 ± 20.65 HU and -66.87 ± 15.00 HU, respectively. In the non-AHA VI and AHA VI subgroups, the values for the mean HU of the plaque side were -49.50 ± 20.53 and -33.55 ± 19.78, respectively (P = 0.014). The D-value HU was higher for the AHA VI group compared to the non-AHA VI group (33.61 ± 16.72 vs. 15.91 ± 14.52, respectively; P = 0.001). Compared to the non-IPH subgroup, the IPH subgroup had a higher mean HU value for the plaque side (-47.68 ± 18.26 vs. -29.63 ± 19.16, respectively; P < 0.001) and a higher D-value HU (17.80 ± 13.27 vs. 38.03 ± 15.46, respectively; P < 0.001). Compared to the low risk non-TRFC subgroup, the TRFC subgroup had a higher D-value HU (24.51 ± 16.16 vs. 33.55 ± 17.65, respectively; P = 0.042). The D-value of PFD was found to be a significant predictor of both AHA VI classification (AUC: 0.79; SE: 64.41%; SP: 83.33%; P = 0.0001) and IPH (AUC: 0.83; SE: 88.89%; SP: 65.38%; P < 0.0001). CONCLUSION: Our study found that PFD was significantly associated with high risk AHA VI plaque characterization, IPH, and TRFC. Therefore, PFD has the potential to be used as an indirect clinical marker of plaque instability.

CD36: an emerging therapeutic target for cancer and its molecular mechanisms.

Tumor cells need to rewire their metabolic pathways to regulate the nutrient uptake and metabolism to sustain the energy production. Lipids are important components of energy sources for tumor metabolism. Tumor cells rely on various transporters to mediate the trafficking of lipids for oxidation or activate oncogenic signaling pathways. CD36, a membrane glycoprotein presenting on the surface of cells, binds fatty acids to facilitate their transport for lipid utilization. Upregulated CD36 expression has been observed in multiple cancer types including acute myeloid leukemia, breast cancer, colorectal cancer, gastric cancer, etc. Moreover, CD36 is correlated with poor clinical outcomes and adverse clinicopathological features in various cancer types. In vitro and vivo studies have confirmed that CD36 participates in the regulation of tumor growth, metastasis, drug resistance through diverse molecular mechanisms. Thus, we firstly discussed the role of CD36 in the regulation of metabolic phenotypes, especially in glucose and fatty acid metabolism. Furthermore, we specifically focused on the molecular mechanisms of CD36 in the occurrence and development of multiple tumor types. Collectively, we explored the connection between CD36 and tumors, providing new insights for developing potential therapeutic strategies and tumor stratification targeting CD36.

Serial CT changes in different components of lung cancer associated with cystic airspace in patients treated with neoadjuvant chemotherapy.

The aim of this study was to observe changes in different components (solid, cystic airspace, or entire tumor) in lung cancer associated with cystic airspace following treatment with neoadjuvant chemotherapy (NC), using computerized tomography (CT). We analyzed serial (baseline, first-time follow-up, and last-time follow-up) clinical data and CT imaging in six patients treated with NC. The diameters, areas, and volumes of different tumor components (solid, cystic airspace, and entire tumor) were measured. Delta (Δ) was used to represent changes in these parameters between two examinations: Δ1(%) represents the change from baseline to first follow-up after NC, and Δ2(%) represents the change from baseline to last follow-up after NC. We used the intra-group correlation coefficient (ICC) to test for consistency between parameters as measured by two radiologists. The diameter of solid components in all lesions showed a trend of continuous reduction compared with baseline (Δ1 ranged from - 8.3 to - 46.0%, Δ2 from - 30.8 to - 69.2%). For cystic airspace and entire tumors, different lesions showed different trends over the course of treatment. For diameter, area, and volume, Δ1 of changes in the solid component ranged from - 8.3 to - 46.9%, - 19.4 to - 70.8%, and - 19.1 to - 94.7%, respectively; Δ2 ranged from - 30.8 to - 69.2%, - 50.8 to - 92.1%, and - 32.7 to - 99.8% in diameter, area, and volume, respectively. Results were inconsistent between different components of lung cancer associated with cystic airspace that was treated with NC, but the diameter, area, and volume of solid components were continuously reduced during treatment. Furthermore, area and volume measurements showed more-significant variation than diameter measurements.

Longitudinal evaluation for COVID-19 chest CT disease progression based on Tchebichef moments.

Blur is a key property in the perception of COVID-19 computed tomography (CT) image manifestations. Typically, blur causes edge extension, which brings shape changes in infection regions. Tchebichef moments (TM) have been verified efficiently in shape representation. Intuitively, disease progression of same patient over time during the treatment is represented as different blur degrees of infection regions, since different blur degrees cause the magnitudes change of TM on infection regions image, blur of infection regions can be captured by TM. With the above observation, a longitudinal objective quantitative evaluation method for COVID-19 disease progression based on TM is proposed. COVID-19 disease progression CT image database (COVID-19 DPID) is built to employ radiologist subjective ratings and manual contouring, which can test and compare disease progression on the CT images acquired from the same patient over time. Then the images are preprocessed, including lung automatic segmentation, longitudinal registration, slice fusion, and a fused slice image with region of interest (ROI) is obtained. Next, the gradient of a fused ROI image is calculated to represent the shape. The gradient image of fused ROI is separated into same size blocks, a block energy is calculated as quadratic sum of non-direct current moment values. Finally, the objective assessment score is obtained by TM energy-normalized applying block variances. We have conducted experiment on COVID-19 DPID and the experiment results indicate that our proposed metric supplies a satisfactory correlation with subjective evaluation scores, demonstrating effectiveness in the quantitative evaluation for COVID-19 disease progression.

Predictive value of baseline 18F-FDG PET/CT and interim treatment response for the prognosis of patients with diffuse large B-cell lymphoma receiving R-CHOP chemotherapy.

The present study aimed to investigate the prognostic value of baseline 18F-FDG PET/CT quantitative parameters and interim treatment response, and to assess whether the combination of these could improve the predictive efficacy in patients with diffuse large B-cell lymphoma (DLBCL) receiving R-CHOP chemotherapy. PET/CT images and clinical data of 64 patients with DLBCL who had undergone 18F-FDG PET/CT scan before and after 3 or 4 cycles of R-CHOP chemotherapy were retrospectively reviewed. The quantitative parameters including standardized uptake value (SUV), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and maximum diameter of the maximum lesion (Dmax) were measured on baseline PET/CT images. Cox proportional hazards model was used to evaluate the influence of baseline PET/CT parameters, clinical indicators and interim treatment response on prognosis. Survival analysis was performed using Kaplan-Meier method. Receiver operating characteristic (ROC) curve analysis was performed to estimate the predictive efficacy of the combination of baseline PET/CT parameters and interim treatment response. Ann Arbor stage, International Prognostic Index (IPI), lactate dehydrogenase (LDH), necrosis, MTVmax, TLGmax, Dmax and interim treatment response showed association with 2-year progression-free survival (PFS, P<0.05). LDH, necrosis, MTVmax, MTVsum, TLGmax, TLGsum, Dmax and interim treatment response showed association with 2-year overall survival (OS, P<0.05). Ann Arbor stage, Dmax and interim treatment response were found to be independent predictors of 2-year PFS (P<0.05), while Dmax and interim treatment response were found to be independent predictors of 2-year OS (P<0.05). The PFS and OS curves of Dmax <5.7 cm group and Dmax ≥5.7 cm group, complete response (CR) group and non-CR group were significantly different, respectively (P<0.05). The baseline 18F-FDG PET/CT parameters and interim treatment response have important prognostic values in DLBCL patients who received R-CHOP chemotherapy. Combined application of Dmax and interim treatment response improved the predictive efficacy of 2-year PFS. It may be helpful to identify patients who are at high-risk of relapse and to guide early clinical intervention of these patients.

Predicting distant metastasis and chemotherapy benefit in locally advanced rectal cancer.

Distant metastasis (DM) is the main cause of treatment failure in locally advanced rectal cancer. Adjuvant chemotherapy is usually used for distant control. However, not all patients can benefit from adjuvant chemotherapy, and particularly, some patients may even get worse outcomes after the treatment. We develop and validate an MRI-based radiomic signature (RS) for prediction of DM within a multicenter dataset. The RS is proved to be an independent prognostic factor as it not only demonstrates good accuracy for discriminating patients into high and low risk of DM in all the four cohorts, but also outperforms clinical models. Within the stratified analysis, good chemotherapy efficacy is observed for patients with pN2 disease and low RS, whereas poor chemotherapy efficacy is detected in patients with pT1-2 or pN0 disease and high RS. The RS may help individualized treatment planning to select patients who may benefit from adjuvant chemotherapy for distant control.

The Predictive Value of Pre-/Postneoadjuvant Chemoradiotherapy MRI Characteristics for Patient Outcomes in Locally Advanced Rectal Cancer.

RATIONALE AND OBJECTIVES: This study aimed to investigate the predictive value of pre-/postneoadjuvant chemoradiotherapy (nCRT) magnetic resonance imaging (MRI) characteristics for the long-term survival outcomes in patients with locally advanced rectal cancer (LARC). MATERIALS AND METHODS: We retrospectively evaluated pre- and post-nCRT MRI and clinicopathologic characteristics of LARC patients. The 3-year disease-free survival (DFS) was estimated using the Kaplan-Meier product-limit method. Associations between MRI variabilities and survival outcomes were assessed using Cox proportional hazards model. RESULTS: In total, 171 LARC patients (112 men and 59 women) with a median age of 55 years (range, 27-82 years) treated with nCRT were evaluated. The median follow-up was 47.6 months, and the 3-, 4-, and 5-year DFS in the overall cohort was 76.6%, 74.5%, and 73.7%, respectively. MRI assessment of extramural venous invasion (mrEMVI) positivity was a significant independent adverse factor of long-term survival (hazard ratio [HR] = 2.589, 95% confidence interval [CI] = 1.398-4.794, p = 0.002) on multivariate analysis. Patients with positive mrEMVI had significantly lower 3-year DFS than those with negative mrEMVI (52.6 months vs 65.1 months; p = 0.003). Moreover, the tumor regression grade on MRI (mrTRG) also significantly correlated with survival outcomes in patients with LARC. Patients with partial response on post-nCRT MRI (mrPR) showed short DFS than those with complete response (mrCR; HR = 4.914, 95% CI = 1.176-20.533, p = 0.029). The 3-year DFS of mrCR and mrPR patients were 74.3 months and 58.9 months, respectively (p = 0.011). CONCLUSION: The pre-/post-nCRT MRI characteristics may be used to long-term survival stratification in LARC patients. mrEMVI positivity was an independent adverse prognostic indicator for 3-year DFS. Further, mrTRG may also be a predictive factor for the prognosis of LARC patients. The pre-/post-nCRT MR imaging may offer more information for providing individualized treatment.

The predictive value of energy spectral CT parameters for assessing Ki-67 expression of lung cancer.

BACKGROUND: To investigate the predictive value of energy spectral CT parameters for Ki-67 expression in lung cancer. METHODS: In this retrospective analysis, 27 primary lung cancer patients confirmed by pathological examination were enrolled between December 2018 and February 2019. All patients underwent baseline arterial phase (AP) and venous phase (VP) energy spectral CT scanning followed by surgery in our institution. The iodine concentration (IC), normalized iodine concentration (NIC) and the slope of 40-80 keV energy spectrum curve (λHU) were derived from dual-energy virtual imaging on a Siemens postprocessed workstation. Immunohistochemical examination was performed to analyze Ki-67 expression. The ROC curves were used for predicting the performance of energy spectral parameters for Ki-67 expression. RESULTS: The tumors appeared larger in Ki-67 high expression group than the low expression group (P=0.046). The energy spectral parameters were higher in venous phase when compared to arterial phase, but only the venous phase NIC (vpNIC) was significantly different from that of the arterial phase NIC (apNIC) (P<0.01). There are significant differences in high and low Ki-67 expression groups for vpNIC and venous λHU (vpλHU), (P=0.033 and 0.037 for vpNIC and vpλHU, respectively). vpNIC ROC analysis showed borderline P value (P=0.056) with an AUC, sensitivity (SE), specificity (SP) and cut-off value (0.717, 92.86, 61.54 and ≤0.347), respectively. The AUC, SE, SP and cut-off value of vpλHU were 0.698, 92.86, 53.85 and ≤2.407, respectively. CONCLUSIONS: The energy spectral parameters (NIC and λHU) of venous phase might be used for predicting Ki-67 stratification. The venous phase energy spectral parameters were higher than the arterial phase. Furthermore, low expression Ki-67 group showed association with higher IC, NIC and λHU than high expression group.

Developing a prediction model based on MRI for pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer.

PURPOSE: The aim of this study was to build an appropriate diagnostic model for predicting pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC), by combining magnetic resonance imaging (MRI) parameters with clinical factors. METHODS: Eighty-four patients with LARC who underwent MR examination before and after nCRT were enrolled in this study. MRI parameters including cylindrical approximated tumor volume (CATV) and relative signal intensity of tumor (rT2wSI) were measured; corresponding reduction rates (RR) were calculated; and MR tumor regression grade (mrTRG) and other conventional MRI parameters were assessed. Logistic regression with lasso regularization was performed and the appropriate prediction model for pCR was built up. An external cohort of thirty-six patients was used as the validation group for testing the model. Receiver-operating characteristic (ROC) analysis was used to assess the diagnostic performance. RESULTS: In the development and the validation group, 17 patients (20.2%) and 11 patients (30.6%), respectively, achieved pCR. Two CATV-related parameters (CATVpost, which is the CATV measured after nCRT and CATVRR), one rT2wSI-related parameter (rT2wSIRR), and mrTRG were the most important parameters for predicting pCR and were retained in the diagnostic model. In the development group, the area under the receiver-operating characteristic curve (AUC) for predicting pCR is 0.88 [95% confidence interval (CI) 0.78-0.97, p < 0.001], with a sensitivity of 82.4% and a specificity of 83.6%. In the validation group, the AUC is 0.84 (95% CI 0.70-0.98, p = 0.001), with a sensitivity of 81.8% and a specificity of 76.0%. CONCLUSION: A diagnostic model including CATVpost, CATVRR, rT2wSIRR, and mrTRG was useful for predicting pCR after nCRT in patients with LARC and may be used as an effective organ-preservation strategy.

Machine Learning-based Analysis of Rectal Cancer MRI Radiomics for Prediction of Metachronous Liver Metastasis.

RATIONALE AND OBJECTIVES: To use machine learning-based magnetic resonance imaging radiomics to predict metachronous liver metastases (MLM) in patients with rectal cancer. MATERIALS AND METHODS: This study retrospectively analyzed 108 patients with rectal cancer (54 in MLM group and 54 in nonmetastases group). Feature selection were performed in the radiomic feature sets extracted from images of T2-weighted image (T2WI) and venous phase (VP) sequence respectively, and the combining feature set with 2058 radiomic features incorporating two sequences with the least absolute shrinkage and selection operator method. Five-fold cross-validation and two machine learning algorithms (support vector machine [SVM]; logistic regression [LR]) were utilized for predictive model constructing. The diagnostic performance of the models was evaluated by receiver operating characteristic curves with indicators of accuracy, sensitivity, specificity and area under the curve, and compared by DeLong test. RESULTS: Five, 8, and 22 optimal features were selected from 1029 T2WI, 1029 VP, and 2058 combining features, respectively. Four-group models were constructed using the five T2WI features (ModelT2), the 8 VP features (ModelVP), the combined 13 optimal features (Modelcombined), and the 22 optimal features selected from 2058 features (Modeloptimal). In ModelVP, the LR was superior to the SVM algorithm (P = 0.0303). The Modeloptimal using LR algorithm showed the best prediction performance (P = 0.0019-0.0081) with accuracy, sensitivity, specificity, and area under the curve of 0.80, 0.83, 0.76, and 0.87, respectively. CONCLUSION: Radiomics models based on baseline rectal magnetic resonance imaging has high potential for MLM prediction, especially the Modeloptimal using LR algorithm. Moreover, except for ModelVP, the LR was not superior to the SVM algorithm for model construction.

MRI morphologic and clinicopathologic characteristics for predicting outcomes in patients with locally advanced rectal cancer.

PURPOSE: The aim of this study was to investigate the value of MRI morphologic and clinicopathologic factors for predicting 3-year disease-free survival (DFS) in patients with locally advanced rectal cancer (LARC). METHOD: In this retrospective study, pre- and post-neoadjuvant chemoradiotherapy (nCRT) MRI morphologic (e.g., pre-nCRT MRI-detected extramural venous invasion) and clinicopathologic variabilities (e.g., pathological complete response) were evaluated in all patients. Three-year DFS was estimated using Kaplan-Meier product-limit method, and Cox proportional hazards models were used to determine associations between morphologic or clinicopathologic variabilities and survival outcomes. RESULTS: A total of 115 patients (39 females and 76 males; median age, 54 years; age range, 28-82 years) with LARC treated with nCRT were enrolled. With a median follow-up of 48.0 months, the 3-year DFS was 79.0% for all patients. During follow-up, 18 patients died, 28 patients experienced relapse (26 distant, one local, and one both), and 69 patients were censored. MRI-detected extramural venous invasion (mrEMVI) was the only significantly independent factor of long-term survival, while HR was 2.308 (95% CI 1.151-4.629, P = 0.018) on univariate and 2.495 (95% CI 1.243-5.012, P = 0.010) on multivariate analysis. The 3-year cumulative survival rate in patients with mrEMVI negativity compared with positivity were 86.6% versus 65.0% (P = 0.015), respectively. CONCLUSION: In conclusion, pre-nCRT mrEMVI status was the independent significant risk factor for long-term outcomes in LARC patients treated with nCRT, while the other morphologic and clinicopathologic characteristics were not related to the patient survival.

MRI texture analysis in predicting treatment response to neoadjuvant chemoradiotherapy in rectal cancer.

To evaluate the importance of MRI texture analysis in prediction and early assessment of treatment response before and early neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). This retrospective study comprised of 59 patients. The tumoral texture parameters were compared between pre- and early nCRT. Area Under receiver operating characteristic (ROC) Curves [AUCs] were used to compare the diagnostic performance of statistically significant difference parameters and logistic regression analysis predicted probabilities for discriminating responders and nonresponders. The Standard Deviation (SD), kurtosis and uniformity were statistically significantly difference between pre- and early nCRT (p = 0.0012, 0.0001, and < 0.0001, respectively). In pathological complete response (pCR) group, pre-uniformity and pre-Energy were significantly higher than that of nonresponders (p = 0.03 and p < 0.01, respectively), while the pre-entropy in nonresponder was reverse (p = 0.01). The diagnostic performance of pre-kurtosis and pre-Energy were higher in tumor regression grade (TRG) and pCR group (AUC = 0.67, 0.73, respectively). Logistic regression analysis showed that diagnostic performance for prediction responder and nonresponder did not significantly improve compared with to pre-uniformity, energy and entropy in pCR group (AUC = 0.76, p = 0.2794, 0.4222 and 0.3512, respectively). Texture parameters as imaging biomarkers have the potential to prediction and early assessment of tumoral treatment response to neoadjuvant chemoradiotherapy in patients with LARC.

Novel radiomic signature as a prognostic biomarker for locally advanced rectal cancer.

BACKGROUND: Locally advanced rectal cancer (LARC) patient stratification by clinicoradiologic factors may yield variable results. Therefore, more efficient prognostic biomarkers are needed for improved risk stratification of LARC patients, personalized treatment, and prognostication. PURPOSE/HYPOTHESIS: To compare the ability of a radiomic signature to predict disease-free survival (DFS) with that of a clinicoradiologic risk model in individual patients with LARC. STUDY TYPE: Retrospective study. POPULATION: In all, 108 consecutive patients (allocated to a training and validation set with a 1:1 ratio) with LARC treated with neoadjuvant chemoradiotherapy (nCRT) followed by total mesorectal excision (TME). FIELD STRENGTH/SEQUENCE: Axial 3D LAVA multienhanced MR sequence at 3T. ASSESSMENT: ITK-SNAP software was used for manual segmentation of 3D pre-nCRT MR images. All manual tumor segmentations were performed by a gastrointestinal tract radiologist, and validated by a senior radiologist. The clinicoradiologic risk factors with potential prognostic outcomes were identified in univariate analysis based on the Cox regression model for the whole set. The results showed that ypT, ypN, EMVI, and MRF were potential clinicoradiologic risk factors. Interestingly, only ypN and MRF were identified as independent predictors in multivariate analysis based on the Cox regression model. STATISTICAL TESTS: A radiomic signature based on 485 3D features was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model. The association of the radiomic signature with DFS was investigated by Kaplan-Meier survival curves. Survival curves were compared by the log-rank test. Three models were built and assessed for their predictive values, using the Harrell concordance index and integrated time-dependent area under the curve. RESULTS: The novel radiomic signature stratified patients into low- and high-risk groups for DFS in the training set (hazard ratio [HR] = 6.83; P < 0.001), and was successfully validated in the validation set (HR = 2.92; P < 0.001). The model combining the radiomic signature and clinicoradiologic findings had the best performance (C index = 0.788, 95% confidence interval [CI] 0.72-0.86; integrated time-dependent area under the curve of 0.837 at 3 years). DATA CONCLUSION: The novel radiomic signature could be used to predict DFS in patients with LARC. Furthermore, combining this radiomic signature with clinicoradiologic features significantly improved the ability to estimate DFS (P = 0.001, 0.005 in training set and in validation set, respectively), and may help guide individualized treatment in such patients. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018.

Askin tumor: four case reports and a review of the literature.

Askin tumor is an uncommon malignant neoplasm in the thoracopulmonary region mainly occurring in children and adolescents. Four young patients with histologically proven Askin tumors were treated in our hospital. In all patients, chest computed tomography imaging demonstrated a chest wall mass with or without destruction of ribs. All patients underwent radical mass resection and postoperative chemotherapy. By the time this article was completed, two of the patients had died with local chest wall recurrences. Here we focus on the imaging features, differential diagnosis, pathology and prognosis of this rare disease.