Baseline hepatobiliary MRI for predicting chemotherapeutic response and prognosis in initially unresectable colorectal cancer liver metastases.

PURPOSE: To evaluate the performance of hepatobiliary MRI parameters as predictors of clinical response to chemotherapy in patients with initially unresectable colorectal cancer liver metastases (CRLM). METHODS: Eighty-five patients with initially unresectable CRLM were retrospectively enrolled from two hospitals and scanned using gadobenate dimeglumine-enhanced MRI before treatment. Therapy response was evaluated based on the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. Conventional parameters (i.e., signal intensity [SI]) and radiomics features of portal venous phase (PVP) and hepatobiliary phase (HBP) images were analyzed between the responders and non-responders. Next, the combined model was constructed, and the area under the receiver operating characteristic (ROC) curve (AUC) was calculated. The relationship between the combined model and progression-free survival (PFS) was analyzed using Cox regression. RESULTS: Of the 85 patients from two hospitals, 42 were in the response group, and 43 were in the non-response group. Upon conducting five-fold cross-validation, the normalized relative enhancement (NRE) of CRLM during the PVP yielded an AUC of 0.625. Additionally, a radiomics feature derived from the tumor area in the HBP achieved an AUC of 0.698, while a separate feature extracted from the peritumoral region in the HBP recorded an AUC of 0.709. The model that integrated these three features outperformed the individual features, achieving an AUC of 0.818. Furthermore, the combined model exhibited a significant correlation with PFS (P < 0.001). CONCLUSION: The combined model, based on baseline hepatobiliary MRI, aids in predicting chemotherapeutic response and PFS in patients with initially unresectable CRLM.

Diffusion-derived vessel density (DDVD) computed from a simple diffusion MRI protocol as a biomarker of placental blood circulation in patients with placenta accreta spectrum disorders: A proof-of-concept study.

OBJECTIVES: Increasing trend of PAS (placenta accreta spectrum disorders) incidence is a major health concern as PAS is associated with high maternal morbidity and mortality during cesarean section. Prenatal identification of PAS is crucial for delivery planning and patients management. This study aims to explore whether diffusion-derived vessel density (DDVD) computed from a simple diffusion MRI protocol differs in PAS from normal placenta. METHODS: We enrolled 86 patients with PAS disorders and 40 pregnant women without PAS disorders. Each patient underwent intravoxel incoherent motion (IVIM) MRI sequence with 11 b-values. Placenta diffusion-derived vessel density (DDVD-b0b50) was the signal difference between b = 0 and b = 50 s/mm2 images. DDVD(b0b50) A/N was calculated as [accreta lesion DDVD(b0b50)]/ [normal placenta DDVD(b0b50)]. The correlation between DDVD and gestational age was explored using Spearman rank correlation. Differences of DDVD(b0b50) A/N in patients with normal placentas and with PAS, and in patients with different subtypes of PAS were explored. RESULTS: DDVD was negatively correlated with gestational age (p = 0.023, r = -0.359) in patients with normal placentas. DDVD(b0b50) A/N was significantly higher in patients with PAS (median:1.16, mean: 1.261) than normal placenta (median:1.02, mean: 1.032, p < 0.001) and especially higher in patients with placenta increta (median:1.14, mean: 1.278) and percreta (median: 1.20, mean: 1.396, p < 0.001). CONCLUSION: As a higher DDVD indicates higher physiological volume of micro-vessels in PAS, this study suggests DDVD can be a potential biomarker to evaluate the placenta perfusion.

T2WI-based MRI radiomics for the prediction of preoperative extranodal extension and prognosis in resectable rectal cancer.

OBJECTIVE: To investigate whether T2-weighted imaging (T2WI)-based intratumoral and peritumoral radiomics can predict extranodal extension (ENE) and prognosis in patients with resectable rectal cancer. METHODS: One hundred sixty-seven patients with resectable rectal cancer including T3T4N + cases were prospectively included. Radiomics features were extracted from intratumoral, peritumoral 3 mm, and peritumoral-mesorectal fat on T2WI images. Least absolute shrinkage and selection operator regression were used for feature selection. A radiomics signature score (Radscore) was built with logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each Radscore. A clinical-radiomics nomogram was constructed by the most predictive radiomics signature and clinical risk factors. A prognostic model was constructed by Cox regression analysis to identify 3-year recurrence-free survival (RFS). RESULTS: Age, cT stage, and lymph node-irregular border and/or adjacent fat invasion were identified as independent clinical risk factors to construct a clinical model. The nomogram incorporating intratumoral and peritumoral 3 mm Radscore and independent clinical risk factors achieved a better AUC than the clinical model in the training (0.799 vs. 0.736) and validation cohorts (0.723 vs. 0.667). Nomogram-based ENE (hazard ratio [HR] = 2.625, 95% CI = 1.233-5.586, p = 0.012) and extramural vascular invasion (EMVI) (HR = 2.523, 95% CI = 1.247-5.106, p = 0.010) were independent risk factors for predicting 3-year RFS. The prognostic model constructed by these two indicators showed good performance for predicting 3-year RFS in the training (AUC = 0.761) and validation cohorts (AUC = 0.710). CONCLUSION: The nomogram incorporating intratumoral and peritumoral 3 mm Radscore and clinical risk factors could predict preoperative ENE. Combining nomogram-based ENE and MRI-reported EMVI may be useful in predicting 3-year RFS. CRITICAL RELEVANCE STATEMENT: A clinical-radiomics nomogram could help preoperative predict ENE, and a prognostic model constructed by the nomogram-based ENE and MRI-reported EMVI could predict 3-year RFS in patients with resectable rectal cancer. KEY POINTS: • Intratumoral and peritumoral 3 mm Radscore showed the most capability for predicting ENE. • Clinical-radiomics nomogram achieved the best predictive performance for predicting ENE. • Combining clinical-radiomics based-ENE and EMVI showed good performance for 3-year RFS.

Standard diffusion-weighted, diffusion kurtosis and intravoxel incoherent motion in differentiating invasive placentas.

PURPOSE: To investigate the diagnostic value of monoexponential, biexponential, and diffusion kurtosis MR imaging (MRI) in distinguishing invasive placentas. METHODS: A total of 53 patients with invasive placentas and 47 patients with noninvasive placentas undergoing conventional diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) were retrospectively enrolled. The mean, minimum, and maximum parameters including the apparent diffusion coefficient (ADC) and exponential ADC (eADC) from standard DWI, diffusion kurtosis (MK), and diffusion coefficient (MD) from DKI and pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) from IVIM were measured and compared from the volumetric analysis. Receiver operating characteristics (ROC) curve and logistic regression analyses were conducted to evaluate the diagnostic efficiency of different diffusion parameters for distinguishing invasive placentas. RESULTS: Comparisons between accreta lesions in patients with invasive placentas (AL) and lower 1/3 part of the placenta in patients with noninvasive placentas (LP) demonstrated that MD mean, D mean, and D* mean were significantly lower while ADC max and D max were significantly higher in invasive placentas (all p < 0.05). Multivariate analysis demonstrated that D mean, D max and D* mean differed significantly among all the studied parameters for invasive placentas. A combined use of these three parameters yielded an AUC of 0.86 with sensitivity, specificity, and accuracy of 84.91%, 76.60%, and 80%, respectively. CONCLUSION: The combined use of different IVIM parameters is helpful in distinguishing invasive placentas.

Diffusion kurtosis and intravoxel incoherent motion in predicting postpartum hemorrhage in patients at high risk for placenta accreta spectrum disorders.

Background: Placenta accreta spectrum (PAS) disorder encompasses a spectrum of pathologies, from placenta accreta to placenta percreta, which is usually associated with postpartum hemorrhage (PPH). Methods: This cross-sectional study enrolled 109 patients suspected of having PAS disorders based on previous ultrasound results or clinical risk factors from November 2018 to March 2022 in Sichuan Provincial People's Hospital. Of the 109 patients, 34 had PPH and 75 did not have PPH. Magnetic resonance imaging (MRI) including diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) was performed for each patient and the apparent diffusion coefficient (ADC) from DWI, perfusion fraction (f), pure diffusion coefficient (D), and pseudo-diffusion coefficient (D*) from IVIM, and mean diffusion kurtosis (MK) and mean diffusion coefficient (MD) from DKI were measured and compared. The correlation between the DWI parameters and estimated blood loss (EBL) during surgery was identified using correlation analysis. The diagnostic performance for predicting PPH was compared between the two methods. Results: The amount of bleeding during delivery was positively correlated with D [r=0.331, P<0.001, 95% confidence interval (CI): 0.170 to 0.477], D* (r=0.389, P<0.001, 95% CI: 0.207 to 0.527), f (r=0.222, P=0.02, 95% CI: 0.036 to 0.398), and MD (r=0.277, P=0.003, 95% CI: 0.108 to 0.439), but negatively correlated with MK (r=-0.280, P=0.003, 95% CI: -0.431 to -0.098). In predicting PPH, multivariate analyses showed the independent risk factors were placenta previa and D; the area under the curve (AUC) was 0.795 (95% CI: 0.711 to 0.878) when the two risk factors were combined together. Conclusions: IVIM and DKI parameters are correlated with EBL. The combined use of placenta previa and D are helpful for predicting PPH in patients at high risk of PAS disorders.

Longitudinal course of hyperintensity on diffusion weighted imaging in adult-onset neuronal intranuclear inclusion disease patients.

Background: High signals on diffusion weighted imaging along the corticomedullary junction (CMJ) have demonstrated excellent diagnostic values for adult-onset neuronal intranuclear inclusion disease (NIID). However, the longitudinal course of diffusion weighted imaging high intensities in adult-onset NIID patients has rarely been investigated. Methods: We described four NIID cases that had been discovered using skin biopsy and NOTCH2NLC gene testing, after diffusion weighted imaging exhibiting the distinctive corticomedullary junction high signals. Then using complete MRI data from NIID patients, we analyzed the chronological diffusion weighted imaging alterations of those individuals that had been published in Pub Med. Results: We discussed 135 NIID cases with comprehensive MRI data, including our four cases, of whom 39 had follow-up outcomes. The following are the four primary diffusion weighted imaging dynamic change patterns: (1) high signal intensities in the corticomedullary junction were negative on diffusion weighted imaging even after an 11-year follow-up (7/39); (2) diffusion weighted imagings were initially negative but subsequently revealed typical findings (9/39); (3) high signal intensities vanished during follow-up (3/39); (4) diffusion weighted imagings were positive at first and developed in a step-by-step manner (20/39). We discovered that NIID lesions eventually damaged the deep white matter, which comprises the cerebral peduncles, brain stem, middle cerebellar peduncles, paravermal regions, and cerebellar white matter. Conclusion: The longitudinal dynamic changes in NIID of diffusion weighted imaging are highly complex. We find that there are four main patterns of dynamic changes on diffusion weighted imaging. Furthermore, as the disease progressed, NIID lesions eventually involved the deep white matter.

Differentiation of placenta percreta through MRI features and diffusion-weighted magnetic resonance imaging.

OBJECTIVES: To identify whether parameters measured from diffusion kurtosis and intravoxel incoherent motion help diagnose placenta percreta. METHODS: We retrospectively enrolled 75 patients with PAS disorders including 13 patients with placenta percreta and 40 patients without PAS disorders. Each patients underwent diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI). The apparent diffusion coefficient (ADC), perfusion fraction (f), pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), mean diffusion kurtosis (MK) and mean diffusion coefficient (MD) were measured by the volumetric analysis and compared. MRI features were also analyzed and compared. The receiver operating characteristic (ROC) curve and logistic regression analysis were used to evaluate the diagnostic efficiency of different diffusion parameters and MRI features for distinguishing placental percreta. RESULTS: D* was an independent risk factor from DWI for predicting placenta percreta with sensitivity of 73% and specificity of 76%. Focal exophytic mass remained as independent risk factor from MRI features for predicting placenta percreta with sensitivity of 72.7% and specificity of 88.1%. When the two risk factors were combined together, the AUC was the highest, 0.880 (95% CI 0.8-0.96). CONCLUSION: D* and focal exophytic mass were associated with placenta percreta. A combination of the 2 risk factors can be used to predict placenta percreta. CRITICAL RELEVANCE STATEMENT: A combination of D* and focal exophytic mass can be used to differentiate placenta percreta.

Computed tomography enterography-based multiregional radiomics model for differential diagnosis of Crohn's disease from intestinal tuberculosis.

PURPOSE: To build computed tomography enterography (CTE)-based multiregional radiomics model for distinguishing Crohn's disease (CD) from intestinal tuberculosis (ITB). MATERIALS AND METHODS: A total of 105 patients with CD and ITB who underwent CTE were retrospectively enrolled. Volume of interest segmentation were performed on CTE and radiomic features were obtained separately from the intestinal wall of lesion, the largest lymph node (LN), and region surrounding the lesion in the ileocecal region. The most valuable radiomic features was selected by the selection operator and least absolute shrinkage. We established nomogram combining clinical factors, endoscopy results, CTE features, and radiomic score through multivariate logistic regression analysis. Receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were used to evaluate the prediction performance. DeLong test was applied to compare the performance of the models. RESULTS: The clinical-radiomic combined model comprised of four variables including one radiomic signature from intestinal wall, one radiomic signature from LN, involved bowel segments on CTE, and longitudinal ulcer on endoscopy. The combined model showed good diagnostic performance with an area under the ROC curve (AUC) of 0.975 (95% CI 0.953-0.998) in the training cohort and 0.958 (95% CI 0.925-0.991) in the validation cohort. The combined model showed higher AUC than that of the clinical model in cross-validation set (0.958 vs. 0.878, P = 0.004). The DCA showed the highest benefit for the combined model. CONCLUSION: Clinical-radiomic combined model constructed by combining CTE-based radiomics from the intestinal wall of lesion and LN, endoscopy results, and CTE features can accurately distinguish CD from ITB.

Association of MRI Features and Adverse Maternal Outcome in Patients With Placenta Accreta Spectrum Disorders After Abdominal Aortic Balloon Occlusion.

BACKGROUND: MRI features may be associated with adverse maternal outcome in patients with placenta accreta spectrum (PAS) disorders even with abdominal aortic balloon occlusion (AABO). PURPOSE: This study aimed to identify risk factors of MRI for association with adverse maternal outcome in patients with PAS disorders after AABO. STUDY TYPE: Retrospective. POPULATION: Clinical and MRI features of 80 patients were retrospectively reviewed from October 2016 to August 2021. A total of 40 patients had adverse maternal outcomes including intrapartum/peripartum bleeding >1000 mL and/or emergency hysterectomy after AABO. SEQUENCE: Half-Fourier acquisition single-shot turbo spin echo and gradient echo imaging True fast imaging with steady-state precession (True-FISP) at 1.5T MR scanner. ASSESSMENT: MRI features were evaluated by three radiologists and were tested for any association with adverse maternal outcome. STATISTICAL TESTS: Interobserver agreement was calculated with kappa (k) statistics. Association between MRI features and adverse maternal outcomes were evaluated by univariate and multivariate analyses. A nomogram was constructed based on the logistic regression. RESULTS: The interobserver agreement ranged from fair to substantial (k = 0.379-0.783). Multivariate analyses revealed that short cervical length (OR: 4.344), abnormal intraplacental vascularity (OR: 6.005), placental bulge (OR: 9.085), and myometrial interruption (OR: 9.550) were independent risk factors for adverse maternal outcomes. The combination of four risk factors together demonstrated the highest AUC of 0.851 (95% CI 0.769-0.933) with a sensitivity and specificity of 77.5% and 72.5%, respectively and then a nomogram composed of the above four risk factors was constructed to represent the probability of adverse maternal outcome. DATA CONCLUSION: The nomogram demonstrated the association between MRI features and patient's poor outcome after undergoing AABO and C-section delivery for PAS. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 3.

Diagnostic performance of mono-exponential DWI versus diffusion kurtosis imaging in breast lesions: A meta-analysis.

BACKGROUND: This meta-analysis aimed to explore the diagnostic value of diffusion kurtosis imaging (DKI) compared to mono-exponential diffusion weighted imaging (DWI) in the diagnosis of breast cancer. METHODS: A systematic electronic literature search (up to September 2020) was conducted for published English-language studies comparing the diagnostic values of DKI and DWI for the detection of breast cancer. The data of mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC) were extracted to construct 2 × 2 contingency tables. The pooled sensitivities, specificities, and areas under the receiver operating characteristic curve (AUCs) were compared between DKI and DWI in the diagnosis of breast cancer. RESULTS: Eight studies were finally included, with a total of 771 patients in the same population. Pooled sensitivities were 82.0% [95% confidence interval (95% CI), 78.2-85.3%] for ADC, 87.3% (95% CI, 83.9-90.1%) for MK, and 83.9% (95% CI, 80.2-87.1%) for MD. Pooled specificities were 81.1% (95% CI, 76.7-84.9%) for ADC, 85.1% (95% CI, 81.1-88.5%) for MK, and 83.2% (95% CI, 79.0-86.8%) for MD. According to the summary receiver operator characteristic curve analyses, the AUCwas 0.901 for ADC, 0.930 for MK, and 0.918 for MD (ADC vs MK, P = .353; ADC vs MD, P = .611). No notable publication bias was found, while significant heterogeneity was observed. CONCLUSIONS: Although DKI is feasible for identifying breast cancer, MD and MK offer similar diagnostic performance to ADC values. Thus, we recommend that DKI should not be included in the routine evaluation of breast lesions now.

Radiomics for differentiating tumor deposits from lymph node metastasis in rectal cancer.

BACKGROUND: Tumor deposits (TDs) are not equivalent to lymph node (LN) metastasis (LNM) but have become independent adverse prognostic factors in patients with rectal cancer (RC). Although preoperatively differentiating TDs and LNMs is helpful in designing individualized treatment strategies and achieving improved prognoses, it is a challenging task. AIM: To establish a computed tomography (CT)-based radiomics model for preoperatively differentiating TDs from LNM in patients with RC. METHODS: This study retrospectively enrolled 219 patients with RC [TDs+LNM- (n = 89); LNM+ TDs- (n = 115); TDs+LNM+ (n = 15)] from a single center between September 2016 and September 2021. Single-positive patients (i.e., TDs+LNM- and LNM+TDs-) were classified into the training (n = 163) and validation (n = 41) sets. We extracted numerous features from the enhanced CT (region 1: The main tumor; region 2: The largest peritumoral nodule). After deleting redundant features, three feature selection methods and three machine learning methods were used to select the best-performing classifier as the radiomics model (Rad-score). After validating Rad-score, its performance was further evaluated in the field of diagnosing double-positive patients (i.e., TDs+LNM+) by outlining all peritumoral nodules with diameter (short-axis) > 3 mm. RESULTS: Rad-score 1 (radiomics signature of the main tumor) had an area under the curve (AUC) of 0.768 on the training dataset and 0.700 on the validation dataset. Rad-score 2 (radiomics signature of the largest peritumoral nodule) had a higher AUC (training set: 0.940; validation set: 0.918) than Rad-score 1. Clinical factors, including age, gender, location of RC, tumor markers, and radiological features of the largest peritumoral nodule, were excluded by logistic regression. Thus, the combined model was comprised of Rad-scores of 1 and 2. Considering that the combined model had similar AUCs with Rad-score 2 (P = 0.134 in the training set and 0.594 in the validation set), Rad-score 2 was used as the final model. For the diagnosis of double-positive patients in the mixed group [TDs+LNM+ (n = 15); single-positive (n = 15)], Rad-score 2 demonstrated moderate performance (sensitivity, 73.3%; specificity, 66.6%; and accuracy, 70.0%). CONCLUSION: Radiomics analysis based on the largest peritumoral nodule can be helpful in preoperatively differentiating between TDs and LNM.

Effect of Tempering Temperature on Microstructure and Sulfide Stress Cracking of 125 Ksi Grade Casing Steel.

The influence of tempering temperature on the microstructure of 0.5Cr0.4W steels was investigated by scanning electron microscope, and the roles of grain boundary character, dislocation, and Taylor factor in sulfide stress cracking (SSC) resistance were interpreted using the election backscattered diffraction technique. The 0.5Cr0.4W steels tempered at 690 °C, 700 °C, and 715 °C all showed tempered martensites. The specimen tempered at 715 °C exhibited a higher critical stress intensity factor (KISSC) of 34.58 MPa·m0.5, but the yield strength of 800 MPa did not meet the criterion of 125 ksi (862 MPa) grade. When the specimen was tempered at 690 °C, the yield strength reached 960 MPa and the KISSC was only 21.36 MPa·m0.5, displaying poorer SSC resistance. The 0.5Cr0.4W steel tempered at 700 °C showed a good combination of yield strength (887 MPa) and SSC resistance (KISSC: 31.16 MPa·m0.5). When increasing the tempering temperature, the local average misorientation and Taylor factor of the 0.5Cr0.4W steels were decreased. The reduced dislocation density, and greater number of grains amenable to slippage, produced less hydrogen transport and a lower crack sensitivity. The SSC resistance was, thus, increased, owing to the minor damage to hydrogen aggregation. Therefore, 700 °C is a suitable tempering temperature for 0.5Cr0.4W casing steel.

Association of prostate zonal volume with location and aggressiveness of clinically significant prostate cancer: A multiparametric MRI study according to PI-RADS version 2.1.

BACKGROUND: The relationship between prostate cancer (PCa) and benign prostatic hyperplasia (BPH) remains unclear. This study aimed to determine whether prostate zonal volume was associated with the location and aggressiveness of PCa. METHODS: 412 PCa patients were retrospectively enrolled. The volumes of the whole prostate (V) and transition zone (Vtz) were calculated by the prolate ellipsoid formula, and volume of the peripheral zone (Vpz) was calculated by their subtraction. Locations of PCa were divided into three categories, including peripheral zone (PZ), transition zone (Vtz) and mixed zone group. The mixed zone group was further divided into PZ-dominated, TZ-dominated and equally distributed subgroups. RESULTS: The Vtz of Gleason score (GS) 3 + 4 group was the largest, while the Vpz of GS 9 group was the largest. Tumor diameters of the PZ and TZ groups were weakly correlated with their corresponding zonal volume (r = 0.261, p < 0.001; r = 0.311, p = 0.009, respectively). There was no significant difference in the zonal volumes and GS distribution among the location groups. The proportion of tumors with high-grade GS of 8 and 9 was higher in the PZ than that in the TZ group (38.5 vs 24.3%, p = 0.041). For tumors located in the TZ, the V and Vpz of tumors with high-grade GS were larger than those in the low-grade GS of 7 group (p = 0.033 and 0.039, respectively). Among the subgroups of mixed zone group, the Vtz of the TZ-dominated group was larger than those of PZ-dominated and equally distributed groups (p = 0.016 and 0.001, respectively). CONCLUSION: PCa with high-grade GS is more likely to have a relatively larger Vpz and involve PZ, while PCa with a larger Vtz is more likely to be with low-grade GS, which support the theoretical model that pressure exerted by increasing prostate size/mechanical deformation may inhibit PCa growth from the clinical point. However, the increase in zonal volume might not be the direct cause of tumorigenesis and aggressiveness.

Enhanced computed tomography features predict pancreatic neuroendocrine neoplasm with Ki-67 index less than 5.

BACKGROUND: Several studies have suggested that patients with pancreatic neuroendocrine neoplasm (pNEN) with the Ki-67 index of < 5% are more likely to show better prognosis after clinical intervention. Moreover, the Ki-67 index at 5% has also been suggested as a potential threshold by the 2016 European Neuroendocrine Tumor Society guidelines. OBJECTIVE: Based on preoperative enhanced computed tomography (CT), this study aimed to investigate imaging characteristics eligible to discriminate the ≤ 5% Ki-67 group from the > 5% Ki-67 group of patients with nonmetastatic pNEN. METHODS: Patients with pathologically diagnosed pNEN and preoperative multiphase CT were enrolled. Their Ki-67 index was calculated and grouped according to the 5% cutoff value. The following CT imaging characteristics and some serum biomarkers were assessed between the two groups: the diameter, location, tumor margin, calcification, pancreatic atrophy, distal pancreatic duct dilation, vessel involvement, and enhancement pattern characteristics of both arterial phase (AP) and portal vein phase (PVP). RESULTS: A total of 142 patients with pNEN were enrolled in this study, comprising 104 in the low (Ki-67, 1%-5%) and 38 in the high index group (Ki-67, >5%). Alpha fetoprotein and cancer antigen 125 were significantly different between the two groups (P-values, 0.030 and 0.049, respectively). The diameter (P < 0.0001), margin (P = 0.003), distal main ductal dilation (P = 0.021), vessel involvement (P = 0.002), AP hypoenhancement (P < 0.0001), PVP hypoenhancement (P = 0.003), AP ratio (P = 0.0001), and PVP ratio (P = 0.0003) were significantly different between the low and high index groups. The area under the curve of the multivariate logistic regression model was 0.853. CONCLUSION: Nonmetastatic pNENs with larger diameter, ill-defined margin, distal main ductal dilation, and tumor hypoenhancement in AP in preoperative enhanced CT tend to have a Ki-67 index of > 5%.The results of this study provide an alternative method to clinicians to decide whether surgery is appropriate.

Computed tomography-based radiomics for predicting lymphovascular invasion in rectal cancer.

PURPOSE: To develop and externally validate a computed tomography (CT)-based radiomics model for predicting lymphovascular invasion (LVI) before treatment in patients with rectal cancer (RC). METHOD: This retrospective study enrolled 351 patients with RC from three hospitals between March 2018 and March 2021. These patients were assigned to one of the following three groups: training set (n = 239, from hospital 1), internal validation set (n = 60, from hospital 1), and external validation set (n = 52, from hospitals 2 and 3). Large amounts of radiomics features were extracted from the intratumoral and peritumoral regions in the portal venous phase contrast-enhanced CT images. The score of radiomics features (Rad-score) was calculated by performing logistic regression analysis following the L1-based method. A combined model (Rad-score + clinical factors) was developed in the training cohort and validated internally and externally. The models were compared using the area under the receiver operating characteristic curve (AUC). RESULTS: Of the 351 patients, 106 (30.2%) had an LVI + tumor. Rad-score (comprised of 22 features) was significantly higher in the LVI + group than in the LVI- group (0.60 ± 0.17 vs. 0.42 ± 0.19, P = 0.001). The combined model obtained good predictive performance in the training cohort (AUC = 0.813 [95% CI: 0.758-0.861]), with robust results in internal and external validations (AUC = 0.843 [95% CI: 0.726-0.924] and 0.807 [95% CI: 0.674-0.903]). CONCLUSIONS: The proposed combined model demonstrated the potential to predict LVI preoperatively in patients with RC.

Preparation of CD52-targeted chimeric antigen receptor-modified T cells and their anti-leukemia effects / 中华血液学杂志

Objective: To construct chimeric antigen receptor (CAR) T cells targeting CD52 (CD52 CAR-T) and validate the effect of CD52 CAR-T cells on CD52-positive leukemia. Methods: A second-generation CD52-targeting CAR bearing 4-1BB costimulatory domain was ligated into a lentiviral vector through molecular cloning. Lentivirus was prepared and packaged by 293 T cells with a four-plasmid system. Fluorescein was used to label cell surface antigens to evaluate the phenotype of CD52 CAR-T cells after infection. Flow cytometry and ELISA were used to evaluate the specific cytotoxicity of CD52 CAR-T cells to CD52-positive cell lines in vitro. Results: ①A pCDH-CD52scFv-CD8α-4-1BB-CD3ζ-GFP expressing plasmid was successfully constructed and used to transduce T cells expressing a novel CD52-targeting CAR. ②On day 6, CD52-positive T cells were almost killed by CD52-targeted CAR-T post lentivirus transduction [CD52 CAR-T (4.48 ± 4.99) %, vs Vector-T (56.58±19.8) %, P=0.011]. ③T cells transduced with the CAR targeting CD52 showed low levels of apoptosis and could be expanded long-term ex vivo. ④The CD52 CAR could promote T cell differentiation into central and effector memory T cells, whereas the proportion of T cells with a CD45RA(+) effector memory phenotype were reduced. ⑤CD52 CAR-T cells could specifically kill CD52-positive HuT78-19t cells but had no killing effect on CD52-negative MOLT4-19t cells. For CD52 CAR-T cells, the percentage of residual of HuT78-19t cells was (2.66±1.60) % at an the E:T ratio of 1∶1 for 24 h, while (56.66±5.74) % of MOLT4-19t cells survived (P<0.001) . ⑥The results of a degranulation experiment confirmed that HuT78-19t cells significantly activated CD52 CAR-T cells but not MOLT4-19t cells[ (57.34±11.25) % vs (13.06± 4.23) %, P<0.001]. ⑦CD52 CAR-T cells released more cytokines when co-cultured with HuT78-19t cells than that of vector-T cells [IFN-γ: (3706±226) pg/ml, P<0.001; TNF-α: (1732±560) pg/ml, P<0.01]. Conclusions: We successfully prepared CD52 CAR-T cells with anti-leukemia effects, which might provide the foundation for further immunotherapy.

Computed Tomography-Based Radiomics for Preoperative Prediction of Tumor Deposits in Rectal Cancer.

OBJECTIVE: To develop and validate a computed tomography (CT)-based radiomics model for predicting tumor deposits (TDs) preoperatively in patients with rectal cancer (RC). METHODS: This retrospective study enrolled 254 patients with pathologically confirmed RC between December 2017 and December 2019. Patients were divided into a training set (n = 203) and a validation set (n = 51). A large number of radiomics features were extracted from the portal venous phase images of CT. After selecting features with L1-based method, we established Rad-score by using the logistic regression analysis. Furthermore, a combined model incorporating Rad-score and clinical factors was developed and visualized as the nomogram. The models were evaluated by the receiver operating characteristic curve (ROC) analysis and area under the ROC curve (AUC). RESULTS: One hundred and seventeen of 254 patients were eventually found to be TDs+. Rad-score and clinical factors including carbohydrate antigen (CA) 19-9, CT-reported T stage (cT), and CT-reported peritumoral nodules (+/-) were significantly different between the TDs+ and TDs- groups (all P < 0.001). These factors were all included in the combined model by the logistic regression analysis (odds ratio = 2.378 for Rad-score, 2.253 for CA19-9, 2.281 for cT, and 4.485 for peritumoral nodules). This model showed good performance to predict TDs in the training and validation cohorts (AUC = 0.830 and 0.832, respectively). Furthermore, the combined model outperformed the clinical model incorporating CA19-9, cT, and peritumoral nodules (+/-) in both training and validation cohorts for predicting TDs preoperatively (AUC = 0.773 and 0.718, P = 0.008 and 0.039). CONCLUSIONS: The combined model incorporating Rad-score and clinical factors could provide a preoperative prediction of TDs and help clinicians guide individualized treatment for RC patients.

Radiomics for predicting perineural invasion status in rectal cancer.

BACKGROUND: Perineural invasion (PNI), as a key pathological feature of tumor spread, has emerged as an independent prognostic factor in patients with rectal cancer (RC). The preoperative stratification of RC patients according to PNI status is beneficial for individualized treatment and improved prognosis. However, the preoperative evaluation of PNI status is still challenging. AIM: To establish a radiomics model for evaluating PNI status preoperatively in RC patients. METHODS: This retrospective study enrolled 303 RC patients in a single institution from March 2018 to October 2019. These patients were classified as the training cohort (n = 242) and validation cohort (n = 61) at a ratio of 8:2. A large number of intra- and peritumoral radiomics features were extracted from portal venous phase images of computed tomography (CT). After deleting redundant features, we tested different feature selection (n = 6) and machine-learning (n = 14) methods to form 84 classifiers. The best performing classifier was then selected to establish Rad-score. Finally, the clinicoradiological model (combined model) was developed by combining Rad-score with clinical factors. These models for predicting PNI were compared using receiver operating characteristic curve (ROC) analysis and area under the ROC curve (AUC). RESULTS: One hundred and forty-four of the 303 patients were eventually found to be PNI-positive. Clinical factors including CT-reported T stage (cT), N stage (cN), and carcinoembryonic antigen (CEA) level were independent risk factors for predicting PNI preoperatively. We established Rad-score by logistic regression analysis after selecting features with the L1-based method. The combined model was developed by combining Rad-score with cT, cN, and CEA. The combined model showed good performance to predict PNI status, with an AUC of 0.828 [95% confidence interval (CI): 0.774-0.873] in the training cohort and 0.801 (95%CI: 0.679-0.892) in the validation cohort. For comparison of the models, the combined model achieved a higher AUC than the clinical model (cT + cN + CEA) achieved (P < 0.001 in the training cohort, and P = 0.045 in the validation cohort). CONCLUSION: The combined model incorporating Rad-score and clinical factors can provide an individualized evaluation of PNI status and help clinicians guide individualized treatment of RC patients.

Predictive Value of Metabolic Parameters Derived From 18F-FDG PET/CT for Microsatellite Instability in Patients With Colorectal Carcinoma.

Background: Microsatellite instability (MSI) is one of the important factors that determine the effectiveness of immunotherapy in colorectal cancer (CRC) and serves as a prognostic biomarker for its clinical outcomes. Purpose: To investigate whether the metabolic parameters derived from18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) can predict MSI status in patients with CRC. Materials and Methods: A retrospective analysis was performed on CRC patients who underwent 18F-FDG PET/CT examination before surgery between January 2015 and April 2021. The metabolic 18F-FDG PET/CT parameters of the primary CRC lesion were calculated and recorded with different thresholds, including the maximum, peak, and mean standardized uptake value (SUVmax, SUVpeak, and SUVmean), as well as the metabolic tumor volume (MTV) and the total lesion glycolysis (TLG). The status of MSI was determined by immunohistochemical assessment. The difference of quantitative parameters between MSI and microsatellite stability (MSS) groups was assessed, and the receiver operating characteristic (ROC) analyses with area under ROC curves (AUC) was used to evaluate the predictive performance of metabolic parameters. Results: A total of 44 patients (24 men and 20 women; mean ± standard deviation age: 71.1 ± 14.2 years) were included. There were 14 patients in the MSI group while there were 30 in the MSS group. MTV30%, MTV40%, MTV50%, and MTV60%, as well as TLG50% and TLG60% showed significant difference between two groups (all p-values <0.05), among which MTV50% demonstrated the highest performance in the prediction of MSI, with an AUC of 0.805 [95% confidence interval (CI): 0.657-0.909], a sensitivity of 92.9% (95% CI: 0.661-0.998), and a specificity of 66.7% (95% CI: 0.472-0.827). Patients' age and MTV50% were significant predictive indicators of MSI in multivariate logistic regression. Conclusion: The metabolic parameters derived from18F-FDG PET/CT were able to preoperatively predict the MSI status in CRC, with MTV50% demonstrating the highest predictive performance. PET/CT imaging could serve as a noninvasive tool in the guidance of immunotherapy and individualized treatment in CRC patients.

Development and validation of MRI-based deep learning models for prediction of microsatellite instability in rectal cancer.

BACKGROUND: Microsatellite instability (MSI) predetermines responses to adjuvant 5-fluorouracil and immunotherapy in rectal cancer and serves as a prognostic biomarker for clinical outcomes. Our objective was to develop and validate a deep learning model that could preoperatively predict the MSI status of rectal cancer based on magnetic resonance images. METHODS: This single-center retrospective study included 491 rectal cancer patients with pathologically proven microsatellite status. Patients were randomly divided into the training/validation cohort (n = 395) and the testing cohort (n = 96). A clinical model using logistic regression was constructed to discriminate MSI status using only clinical factors. Based on a modified MobileNetV2 architecture, deep learning models were tested for the predictive ability of MSI status from magnetic resonance images, with or without integrating clinical factors. RESULTS: The clinical model correctly classified 37.5% of MSI status in the testing cohort, with an AUC value of 0.573 (95% confidence interval [CI], 0.468 ~ 0.674). The pure imaging-based model and the combined model correctly classified 75.0% and 85.4% of MSI status in the testing cohort, with AUC values of 0.820 (95% CI, 0.718 ~ 0.884) and 0.868 (95% CI, 0.784 ~ 0.929), respectively. Both deep learning models performed better than the clinical model (p < 0.05). There was no statistically significant difference between the deep learning models with or without integrating clinical factors. CONCLUSIONS: Deep learning based on high-resolution T2-weighted magnetic resonance images showed a good predictive performance for MSI status in rectal cancer patients. The proposed model may help to identify patients who would benefit from chemotherapy or immunotherapy and determine individualized therapeutic strategies for these patients.