Evaluation of the JF5-based Aspergillus galactomannoprotein lateral flow device for diagnosing invasive aspergillosis in cancer patients.

PURPOSE: Cancer patients are at heightened risk for invasive aspergillosis (IA), a condition associated with elevated mortality risk. The JF5-based Aspergillus Galactomannoprotein Lateral Flow Device (AspLFD) offers rapid point-of-care testing (POCT) for IA. This study evaluated the diagnostic performance of AspLFD in cancer populations. METHODS: This retrospective study examined cancer patient bronchoalveolar lavage fluid (BALF) and serum samples collected between September 2021 and January 2023. Both AspLFD and galactomannan (GM) assays were conducted, and the results were analysed by two independent researchers. RESULTS: This study included 242 samples from 218 cancer patients, with 58 BALF and 184 serum samples. The overall agreement between AspLFD and GM assay results was 92.1%, with a kappa value of 0.552. AspLFD diagnosed proven/probable IA with a sensitivity and specificity of 91.7% and 95.3%, respectively, whereas GM exhibited sensitivity and specificity values of 83.3% and 93.7%, respectively. There were no statistical differences in the sensitivity and specificity between the two methods (P > 0.05). For serum analyses, AspLFD and GM exhibited similar sensitivity (66.7% vs. 66.7%, P > 0.05) and specificity (98.6% vs. 96.6%, P > 0.05) values. However, the sensitivity of the AspLFD was superior to the GM assay (100% vs. 88.9%) in BALF analyses but the difference was not statistically significant (P > 0.05), with no difference in specificity (83.7% vs. 83.7%, P > 0.05). In the solid-tumour cohort, both the AspLFD and GM assay exhibited high sensitivity (100% for both) and specificity (94.2% vs. 92.8%, P > 0.05). CONCLUSION: The AspLFD demonstrated good performance in diagnosing IA in cancer patients, especially those with solid tumours. The AspLFD is thus an alternative POCT, particularly when GM evaluations are not readily available.

Prediction of lymph node metastasis in stage T1-2 rectal cancers with MRI-based deep learning.

OBJECTIVES: This study aimed to investigate whether a deep learning (DL) model based on preoperative MR images of primary tumors can predict lymph node metastasis (LNM) in patients with stage T1-2 rectal cancer. METHODS: In this retrospective study, patients with stage T1-2 rectal cancer who underwent preoperative MRI between October 2013 and March 2021 were included and assigned to the training, validation, and test sets. Four two-dimensional and three-dimensional (3D) residual networks (ResNet18, ResNet50, ResNet101, and ResNet152) were trained and tested on T2-weighted images to identify patients with LNM. Three radiologists independently assessed LN status on MRI, and diagnostic outcomes were compared with the DL model. Predictive performance was assessed with AUC and compared using the Delong method. RESULTS: In total, 611 patients were evaluated (444 training, 81 validation, and 86 test). The AUCs of the eight DL models ranged from 0.80 (95% confidence interval [CI]: 0.75, 0.85) to 0.89 (95% CI: 0.85, 0.92) in the training set and from 0.77 (95% CI: 0.62, 0.92) to 0.89 (95% CI: 0.76, 1.00) in the validation set. The ResNet101 model based on 3D network architecture achieved the best performance in predicting LNM in the test set, with an AUC of 0.79 (95% CI: 0.70, 0.89) that was significantly greater than that of the pooled readers (AUC, 0.54 [95% CI: 0.48, 0.60]; p < 0.001). CONCLUSION: The DL model based on preoperative MR images of primary tumors outperformed radiologists in predicting LNM in patients with stage T1-2 rectal cancer. KEY POINTS: • Deep learning (DL) models with different network frameworks showed different diagnostic performance for predicting lymph node metastasis (LNM) in patients with stage T1-2 rectal cancer. • The ResNet101 model based on 3D network architecture achieved the best performance in predicting LNM in the test set. • The DL model based on preoperative MR images outperformed radiologists in predicting LNM in patients with stage T1-2 rectal cancer.

Risk stratification for 1- to 2-cm gastric gastrointestinal stromal tumors: visual assessment of CT and EUS high-risk features versus CT radiomics analysis.

OBJECTIVES: To investigate the ability of CT and endoscopic sonography (EUS) in predicting the malignant risk of 1-2-cm gastric gastrointestinal stromal tumors (gGISTs) and to clarify whether radiomics could be applied for risk stratification. METHODS: A total of 151 pathologically confirmed 1-2-cm gGISTs from seven institutions were identified by contrast-enhanced CT scans between January 2010 and March 2021. A detailed description of EUS morphological features was available for 73 gGISTs. The association between EUS or CT high-risk features and pathological malignant potential was evaluated. gGISTs were randomly divided into three groups to build the radiomics model, including 74 in the training cohort, 37 in validation cohort, and 40 in testing cohort. The ROIs covering the whole tumor volume were delineated on the CT images of the portal venous phase. The Pearson test and least absolute shrinkage and selection operator (LASSO) algorithm were used for feature selection, and the ROC curves were used to evaluate the model performance. RESULTS: The presence of EUS- and CT-based morphological high-risk features, including calcification, necrosis, intratumoral heterogeneity, irregular border, or surface ulceration, did not differ between very-low and intermediate risk 1-2-cm gGISTs (p > 0.05). The radiomics model consisting of five radiomics features showed favorable performance in discrimination of malignant 1-2-cm gGISTs, with the AUC of the training, validation, and testing cohort as 0.866, 0.812, and 0.766, respectively. CONCLUSIONS: Instead of CT- and EUS-based morphological high-risk features, the CT radiomics model could potentially be applied for preoperative risk stratification of 1-2-cm gGISTs. KEY POINTS: • The presence of EUS- and CT-based morphological high-risk factors, including calcification, necrosis, intratumoral heterogeneity, irregular border, or surface ulceration, did not correlate with the pathological malignant potential of 1-2-cm gGISTs. • The CT radiomics model could potentially be applied for preoperative risk stratification of 1-2-cm gGISTs.

Selecting Candidates for Organ-Preserving Strategies After Neoadjuvant Chemoradiotherapy for Rectal Cancer: Development and Validation of a Model Integrating MRI Radiomics and Pathomics.

BACKGROUND: Histopathologic evaluation after surgery is the gold standard to evaluate treatment response to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC). However, it cannot be used to guide organ-preserving strategies due to poor timeliness. PURPOSE: To develop and validate a multiscale model incorporating radiomics and pathomics features for predicting pathological good response (pGR) of down-staging to stage ypT0-1N0 after nCRT. STUDY TYPE: Retrospective. POPULATION: A total of 153 patients (median age, 55 years; 109 men; 107 training group; 46 validation group) with clinicopathologically confirmed LARC. FIELD STRENGTH/SEQUENCE: A 3.0-T; fast spin echo T2 -weighted and single-shot EPI diffusion-weighted images. ASSESSMENT: The differences in clinicoradiological variables between pGR and non-pGR groups were assessed. Pretreatment and posttreatment radiomics signatures, and pathomics signature were constructed. A multiscale pGR prediction model was established. The predictive performance of the model was evaluated and compared to that of the clinicoradiological model. STATISTICAL TESTS: The χ2 test, Fisher's exact test, t-test, the minimum redundancy maximum relevance algorithm, the least absolute shrinkage and selection operator logistic regression algorithm, regression analysis, receiver operating characteristic curve (ROC) analysis, Delong method. P < 0.05 indicated a significant difference. RESULTS: Pretreatment radiomics signature (odds ratio [OR] = 2.53; 95% CI: 1.58-4.66), posttreatment radiomics signature (OR = 9.59; 95% CI: 3.04-41.46), and pathomics signature (OR = 3.14; 95% CI: 1.40-8.31) were independent factors for predicting pGR. The multiscale model presented good predictive performance with areas under the curve (AUC) of 0.93 (95% CI: 0.88-0.98) and 0.90 (95% CI: 0.78-1.00) in the training and validation groups, those were significantly higher than that of the clinicoradiological model with AUCs of 0.69 (95% CI: 0.55-0.82) and 0.68 (95% CI: 0.46-0.91) in both groups. DATA CONCLUSION: A model incorporating radiomics and pathomics features effectively predicted pGR after nCRT in patients with LARC. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 4.

Submucosal Enhancing Stripe as a Contrast Material-enhanced MRI-based Imaging Feature for the Differentiation of Stage T0-T1 from Early T2 Rectal Cancers.

Background Accurate differentiation of stage T0-T1 rectal tumors from stage T2 rectal tumors facilitates the selection of appropriate surgical treatment. MRI is a recommended technique for local staging, but its ability to distinguish T1 from T2 tumors is poor. Purpose To explore the value of a submucosal enhancing stripe (SES), an uninterrupted enhancing band between the rectal tumor and the muscular layer on contrast material-enhanced T1-weighted images, as a potential imaging feature to differentiate T0-T1 from T2 rectal tumors. Materials and Methods This retrospective study included patients with pT0-T1 and pT2 rectal tumors who underwent pretreatment MRI and rectal tumor resection between January 2012 and November 2019. Two radiologists independently evaluated tumor characteristics (SES; status of muscularis propria [SMP]; and tumor shape, location, and size) at MRI. The associations of clinical and imaging characteristics with stage T0-T1 or T2 tumors were assessed, ß values were calculated, and predictive models were built. The diagnostic accuracies for the differentiation of T0-T1 tumors from T2 tumors with SES and SMP were compared. Results Data from 431 patients (mean age, 60 years ± 10 [standard deviation]; 261 men) were evaluated. SES (ß = 3.9; 95% CI: 3.1, 4.7; P < .001), SMP (ß = 1.3; 95% CI: 0.7, 1.9; P < .001), and carpetlike shape (ß = 1.6; 95% CI: 0.5, 2.8; P = .01) were independent factors distinguishing T0-T1 tumors from T2 tumors. The diagnostic accuracy was 87% (95% CI: 84, 90; 376 of 431) for SES and 67% (95% CI: 63, 72; 290 of 431) for SMP (P < .001). Conclusion Submucosal enhancing stripe (SES) at contrasted-enhanced MRI, status of muscularis propria (SMP) on T2-weighted images, and tumor shape can serve as independent imaging features to differentiate stage T0-T1 rectal tumors from stage T2 rectal tumors. Moreover, SES is a more accurate feature than is SMP. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Turkbey in this issue.

Prognostic risk factors and survival models for T3 locally advanced rectal cancer: what can we learn from the baseline MRI?

OBJECTIVES: To evaluate the baseline MRI characteristics for predicting survival outcomes and construct survival models for risk stratification to facilitate personalized treatment and follow-up strategies in patients with MRI-defined T3 (mrT3) locally advanced rectal cancer (LARC). METHODS: We retrospectively reviewed 256 mrT3 LARC patients evaluated between 2008 and 2012 in our institution, with an average follow-up period of 6.8 ± 1.2 years. The baseline MRI characteristics, clinical data, and follow-up information were evaluated. The patients were randomized into a training cohort (TC, 186 patients) and validation cohort (VC, 70 patients). The TC dataset was used to develop multivariate nomograms for disease-free survival (DFS) and overall survival (OS), while the VC dataset was used for independent validation of the models. Harrell concordance (C) indices and Hosmer-Lemeshow calibration were used to evaluate the performances of the models. RESULTS: Baseline mrT3 substage, extramural venous invasion (EMVI) grading, mucinous adenocarcinoma, mesorectal fascia involvement, elevated pretreatment carcinoembryonic antigen level, and neoadjuvant chemoradiotherapy (NCRT) were independent predictors of DFS. T3 substage, EMVI grading, and NCRT were also independent predictors of OS. The nomograms constructed permitted the individualized prediction of 3-year and 5-year DFS and 5-year OS with high discrimination (C-index range, 0.833-0.892) and good calibration in the TC and VC. CONCLUSIONS: We have identified baseline MRI characteristics that help independently predict survival outcomes in patients with mrT3 LARC. The survival models based on these characteristics allow for the individualized pretreatment risk stratification in patients with mrT3 LARC. KEY POINTS: • Baseline MRI characteristics can independently stratify risk and predict survival outcomes in patients with mrT3 LARC. • The nomograms built using selected baseline MRI characteristics facilitate the individualized pretreatment risk stratification and help with clinical decision-making in patients with mrT3 LARC. • MR-defined risk factors should, therefore, be carefully reported in the baseline MRI evaluation.

Encephalitis is mediated by ROP18 of Toxoplasma gondii, a severe pathogen in AIDS patients.

The neurotropic parasite Toxoplasma gondii is a globally distributed parasitic protozoan among mammalian hosts, including humans. During the course of infection, the CNS is the most commonly damaged organ among invaded tissues. The polymorphic rhoptry protein 18 (ROP18) is a key serine (Ser)/threonine (Thr) kinase that phosphorylates host proteins to modulate acute virulence. However, the basis of neurotropism and the specific substrates through which ROP18 exerts neuropathogenesis remain unknown. Using mass spectrometry, we performed proteomic analysis of proteins that selectively bind to active ROP18 and identified RTN1-C, an endoplasmic reticulum (ER) protein that is preferentially expressed in the CNS. We demonstrated that ROP18 is associated with the N-terminal portion of RTN1-C and specifically phosphorylates RTN1-C at Ser7/134 and Thr4/8/118. ROP18 phosphorylation of RTN1-C triggers ER stress-mediated apoptosis in neural cells. Remarkably, ROP18 phosphorylation of RTN1-C enhances glucose-regulated protein 78 (GRP78) acetylation by attenuating the activity of histone deacetylase (HDAC), and this event is associated with an increase of neural apoptosis. These results clearly demonstrate that both RTN1-C and HDACs are involved in T. gondii ROP18-mediated pathogenesis of encephalitis during Toxoplasma infection.

Associations of Cervicovaginal Lactobacilli With High-Risk Human Papillomavirus Infection, Cervical Intraepithelial Neoplasia, and Cancer: A Systematic Review and Meta-Analysis.

BACKGROUND: Vaginal dysbiosis characterized by depleted lactobacilli is usually correlated with human papillomavirus (HPV) infection and cervical carcinogenesis, but the effect of the Lactobacillus genus and represented species on this process remains unclear. METHODS: PubMed, EMBASE, and CENTRAL databases were searched up to February 15, 2019. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using a fixed-effect model and Review Manager (version 5.3) for Mac. RESULTS: Eleven studies comprising 1230 cases were included. Lactobacillus spp. was associated with the decreased detection of high-risk subtype (hr)HPV infection (OR = 0.64, 95% CI = 0.48-0.87, I2 = 6%), cervical intraepithelial neoplasia (CIN) (OR = 0.53, 95% CI = 0.34-0.83, I2 = 0%), and cervical cancer (CC) (OR = 0.12, 95% CI = 0.04-0.36, I2 = 0%). At the level of Lactobacillus species, Lactobacillus crispatus, but not Lactobacillus iners, was correlated with the decreased detection of hrHPV infection (OR = 0.49, 95% CI = 0.31-0.79, I2 = 10%) and CIN (OR = 0.50, 95% CI = 0.29-0.88, I2 = 0%). CONCLUSIONS: Cervicovaginal Lactobacillus spp. is associated with the decreased detection of hrHPV infection, CIN, and CC; L. crispatus may be the critical protective factor.

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.

Ctenopharyngodon idella PERK (EIF2AK3) decreases cell viability by phosphorylating eIF2α under ER stress.

As an upstream kinase of eIF2α, protein kinase RNA-like ER (endoplasmic reticulum) kinase (PERK) is a type I transmembrane protein located in ER in eukaryotic cells. PERK is mainly composed of two domains, the intracavitary domain for BIP protein combination and the dissociative C-terminal region containing a typical serine/threonine kinase domain which promotes the phosphorylation of eIF2α. In this study, we cloned a PERK (also known as EIF2AK3) gene from grass carp (Ctenopharyngodon idella). The full-length cDNA of grass carp PERK (CiPERK) is 5192 bp including a 176 bp of 5' untranslated region, a 1719 bp of 3' untranslated region and a 3297 bp of the longest open reading frame (ORF) encoding 1098 amino acids. Phylogenetic analysis exhibits that CiPERK shares a high degree of sequence homology to the counterparts in other teleosts. RT-PCR indicated that CiPERK expression was significantly up-regulated following the stimulation with TM (tunicamycin). To study the function of CiPERK, the N-terminal sequence of CiPERK and CiGRP78 sequence were separately subcloned into the expression vectors pCMV-HA and pCMV-Flag for co-immunoprecipitation and GST-Pulldown assays. The assays indicated that CiPERK and CiGRP78 can combine with each other in normal conditions. However, under ER stress (TM stimulation) CiPERK can improve the eIF2α phosphorylation level. In addition, CCK assay showed the overexpression of CiPERK in CIK cells decreases the cell viability.

Hyperglycemia attenuates remifentanil postconditioning-induced cardioprotection against hypoxia/reoxygenation injury in H9c2 cardiomyoblasts.

BACKGROUND: Hyperglycemia is proposed to be an independent risk factor for cardiovascular morbidity and mortality. Preclinical studies suggest that diabetes mellitus exacerbates myocardial ischemia/reperfusion injury and attenuates the effects of cardioprotective strategies. The cardioprotective effects of postconditioning with the opioid analgesic remifentanil against ischemia/reperfusion injury under the hyperglycemic condition remain contradictory. Therefore, the aim of this study was to investigate the mechanisms by which hyperglycemia affects cardioprotection induced by remifentanil postconditioning. MATERIALS AND METHODS: H9c2 cardiomyoblasts were cultured under the normoglycemic or hyperglycemic condition. Cells were exposed to hypoxia/reoxygenation (H/R) injury followed by hypoxia postconditioning (HPC group) or remifentanil postconditioning (RPC group). Cell viability, injury, and apoptosis were measured after each postconditioning treatment. Activation of endoplasmic reticulum stress (ERS) was analyzed by examining the protein levels of GRP78, CHOP, cleaved caspase-12 and cleaved caspase-3. RESULTS: RPC significantly increased cell viability and reduced apoptosis in normoglycemic cardiomyoblasts, but not in hyperglycemic cardiomyoblasts. HPC and RPC markedly decreased the upregulation of GRP78, CHOP, cleaved caspase 12, and cleaved caspase 3 in response to H/R injury under the normoglycemic condition. Hyperglycemia significantly increased these ERS-associated biomarkers and apoptosis, which could not be reduced by HPC or RPC. CONCLUSIONS: Remifentanil postconditioning protected cardiomyoblasts from H/R injury under normoglycemia, at least in part, through inhibiting ERS-induced apoptosis. Hyperglycemia attenuated the cardioprotection conferred by remifentanil postconditioning, likely as a result of the exacerbated ERS. Inhibiting the ERS response may be an attractive strategy to enhance the cardioprotective effects of postconditioning in diabetic patients.

Toxoplasma gondii virulence factor ROP18 inhibits the host NF-κB pathway by promoting p65 degradation.

The obligate intracellular parasite Toxoplasma gondii secretes effector molecules into the host cell to modulate host immunity. Previous studies have shown that T. gondii could interfere with host NF-κB signaling to promote their survival, but the effectors of type I strains remain unclear. The polymorphic rhoptry protein ROP18 is a key serine/threonine kinase that phosphorylates host proteins to modulate acute virulence. Our data demonstrated that the N-terminal portion of ROP18 is associated with the dimerization domain of p65. ROP18 phosphorylates p65 at Ser-468 and targets this protein to the ubiquitin-dependent degradation pathway. The kinase activity of ROP18 is required for p65 degradation and suppresses NF-κB activation. Consistently, compared with wild-type ROP18 strain, ROP18 kinase-deficient type I parasites displayed a severe inability to inhibit NF-κB, culminating in the enhanced production of IL-6, IL-12, and TNF-α in infected macrophages. In addition, studies have shown that transgenic parasites carrying kinase-deficient ROP18 induce M1-biased activation. These results demonstrate for the first time that the virulence factor ROP18 in T. gondii type I strains is responsible for inhibiting the host NF-κB pathway and for suppressing proinflammatory cytokine expression, thus providing a survival advantage to the infectious agent.

Identification of MANF as a protein interacting with RTN1-C.

Reticulons (RTNs) constitute a family of endoplasmic reticulum (ER)-associated proteins with a reticular distribution. Recently, evidence has shown that they exert a cancer-specific proapoptotic function via interaction or modulation of specific proteins. Such evidence is particularly associated with the RTN1-C family members. In order to explore proteins that interact with RTN1-C, the yeast two-hybrid system and regular molecular biological techniques were used to screen the human fetal brain cDNA library. As a result, seven RTN1-C interacting proteins including Homo sapiens mesencephalic astrocyte-derived neurotrophic factor (MANF) were obtained. The interactions between RTN1-C and its interacting proteins were confirmed by ß-galactosidase assay and growth test in selective media. Moreover, the MANF/RTN1-C interaction was verified in vitro by glutathione S-transferase pull-down assay and in vivo by immunoprecipitation assay. By immunofluorescence assay, it was found that MANF co-localized with RTN1-C in the ER. Knockdown of RTN1-C reduced the localization of MANF in the ER. These results provide clues to further explore the function of RTN1-C and MANF in neurodegenerative diseases and cancer.

[Construction and Identification of Transgenic Strain of Toxoplasma gondii High-expressing Virulence Factor ROP18].

OBJECTIVE: To construct a transgenic strain of Toxoplasma gondii high-expressing ROP18. METHODS: The gene sequence of encoding ROP18 was amplified by RT-PCR with RNA of T. gondii RH strain. The purified PCR product was subcloned into pCR-Blunt II-Top vector to construct pROP18. The gene sequence of encoding ROP18 was amplified from pROP18, and subcloned into pTUB8-mycGFPPftail-Ty1. The recombinant plasmid pTUB8-ROP18-Ty1 was electroporated into T. gondii RH strain. Stable transgenic cells were selected in the presence of 25 µg/ml mycophenolic acid and 50 µg/ml xanthine, and parasite clones were isolated by limiting dilution after drug selection. The expression of ROP18 in transgenic parasites was detected by immunofluorescence analysis and Western blotting. Giemsa assay was used to detect the proliferation rate of RH strain and the transgenic strain of T. gondii high-expressing ROP18. Twenty mice were divided into two groups. Each mouse in RH strain group was intraperitoneally injected with 1 x 10(3) RH strain, and that of transgenic strain group received 1 x 10(3) transgenic high-expressing ROP18 strain. RESULTS: The full-length sequence of ROP18 gene (1,665 bp) was amplified by RT-PCR. The recombinant plasmid pTUB8-ROP18-Ty1 was identified by restriction enzyme digestion and sequencing methods. Western blotting analysis showed that the transgenic high-expressing ROP18 strain expressed ROP18-Ty1 (Mr 56,000). Immunofluorescence assay showed that ROP18-Ty1 was localized in the rhoptry of T. gondii. Giemsa assay confirmed that ROP18 protein enhanced the proliferation of T. gondii. On the 6th, 12th, and 24th hour after HFF cells infected with T. gondii, the number of tachyzoites in transgenic strain group was 100.0 ± 16.9, 476.0 ± 31.1, and 860.0 ± 52.3, respectively, higher than that of RH strain group (88.0 ± 16.9, 300.0 ± 11.3, 675.0 ± 35.4) (P < 0.05). In RH strain group, all the mice were survival on the 8th day post-infection, the survival rate on the 14th day was 30% (3/10), and all died on the 16th day. In transgenic strain group, all the mice were survival on the 5th day post-infection, the survival rate on the 8th day was 30% (3/10), and all died on the 9th day. CONCLUSION: The transgenic high-expressing ROP18 strain of T. gondii is constructed.

Preoperative MRI features predicting very early recurrence of intrahepatic mass-forming cholangiocarcinoma after R0 resection: a comparison with the AJCC 8th edition staging system.

PURPOSE: This study aimed to establish a nomogram based on preoperative magnetic resonance imaging (MRI) features to predict the very early recurrence (VER, less than 6 months) of intrahepatic mass-forming cholangiocarcinoma (IMCC) after R0 resection. METHODS: This study enrolled a group of 193 IMCC patients from our institution between March 2010 and January 2022. Patients were allocated into the development cohort (n = 137) and the validation cohort (n = 56), randomly, and the preoperative clinical and MRI features were collected. Univariate and multivariate stepwise logistic regression assessments were adopted to assess predictors of VER. Nomogram was constructed and certificated in the validation cohort. The performance of the prediction nomogram was evaluated by its discrimination, calibration, and clinical utility. The performance of the nomogram was compared with the T stage of the American Joint Committee on Cancer (AJCC) 8th edition staging system. RESULTS: Fifty-three patients (27.5%) experienced VER of the tumor and 140 patients (72.5%) with non-VER, during the follow-up period. After multivariate stepwise logistic regression, number of lesions, diffuse hypoenhancement on arterial phase, necorsis and suspicious lymph nodes were independently associated with VER. The nomogram demonstrated significantly higher area under the curve (AUC) of 0.813 than T stage (AUC = 0.666, P = 0.006) in the development cohort, whereas in the validation cohort, the nomogram showed better discrimination performance, with an AUC of 0.808 than T stage (0.705) with no significantly difference (P = 0.230). Decision curve analysis reflected the clinical net benefit of the nomogram. CONCLUSION: The nomogram based on preoperative MRI features is a reliable tool to predict VER for patients with IMCC after R0 resection. This nomogram will be helpful to improve survival prediction and individualized treatment.

Nomogram based on preoperative clinical and MRI features to estimate the microvascular invasion status and the prognosis of solitary intrahepatic mass-forming cholangiocarcinoma.

PURPOSE: To develop a nomogram based on preoperative clinical and magnetic resonance imaging (MRI) features for the microvascular invasion (MVI) status in solitary intrahepatic mass-forming cholangiocarcinoma (sIMCC) and to evaluate whether it could predict recurrence-free survival (RFS). METHODS: We included 115 cases who experienced MRI examinations for sIMCC with R0 resection. The preoperative clinical and MRI features were extracted. Independent predictors related to MVI+ were evaluated by stepwise multivariate logistic regression, and a nomogram was constructed. A receiver operating characteristic (ROC) curve was used to assess the predictive ability. All patients were classified into high- and low-risk groups of MVI. Then, the correlations of the nomogram with RFS in patents with sIMCC were analyzed by Kaplan-Meier method. RESULTS: The occurrence rate of MVI+ was 38.3% (44/115). The preoperative independent predictors of MVI+ were carbohydrate antigen 19-9 > 37 U/ml, tumor size > 5 cm, and an ill-defined tumor boundary. Integrating these predictors, the nomogram exerted a favorable diagnostic performance with areas under the ROC curve of 0.767 (95% confidence interval [CI] 0.654-0.881) in the development cohort, and 0.760 (95% CI 0.591-0.929) in the validation cohort. In the RFS analysis, significant differences were observed between the high- and low-risk MVI groups (6-month RFS rates: 64.5% vs. 78.8% and 46.7% vs. 82.4% in the development and validation cohorts, respectively) (P < 0.05). CONCLUSIONS: A nomogram based on clinical and MRI features is a potential biomarker of MVI and may be a potent method to classify the risk of recurrence in patients with sIMCC.

MRI-based Multiregional Radiomics for Pretreatment Prediction of Distant Metastasis After Neoadjuvant Chemoradiotherapy in Patients with Locally Advanced Rectal Cancer.

RATIONALE AND OBJECTIVES: To develop and validate a nomogram based on intratumoral and peritumoral radiomics signatures for pretreatment prediction of distant metastasis-free survival (DMFS) in patients after neoadjuvant chemoradiotherapy (NCRT) with locally advanced rectal cancer (LARC). MATERIALS AND METHODS: This retrospective study included 230 patients (161 training cohort; 69 validation cohort) with LARC who underwent NCRT and surgery. Radiomics features were extracted on T2-weighted images from gross tumor volume (GTV) and volumes of 4-mm, 6-mm, and 8-mm peritumoral regions (PTV4, PTV6, and PTV8). The least absolute shrinkage and selection operator (LASSO)-Cox analysis were used for features selection and models construction. The performance of each model in predicting DMFS was evaluated by the Concordance index (C-index) and time-independent receiver operating characteristic curve (ROC). RESULTS: The PTV4 radiomics model demonstrated superior performance compared to the PTV6 and PTV8 radiomics models, with C-indexes of 0.750 and 0.703 in the training and validation cohorts, respectively. The nomogram was constructed by integrating the GTV radiomics signature, PTV4 radiomics signature, and relevant clinical characteristics, including CA19-9 level, clinical T stage, and clinical N stage. The nomogram achieved C-indexes of 0.831 and 0.748, with corresponding AUCs of 0.872 and 0.808 for 5-year DMFS in the training and validation cohorts, respectively. Kaplan-Meier analysis revealed that a cut-off value of 1.653 effectively stratified patients into high- and low-risk groups for DM (P < 0.001). CONCLUSION: The intra-peritumoral radiomics nomogram is a favorable tool for clinicians to develop personalized systemic treatment and intensive follow-up strategies to improve patient prognosis.

Novel biomarkers based on dual-energy computed tomography for risk stratification of very early distant metastasis in colorectal cancer after surgery.

Background: Very early distant metastasis (VEDM) for patients with colorectal cancer (CRC) following surgery suggests failure of local treatment strategy and few biomarkers are available for its effective risk stratification. This study aimed to explore the potential of quantitative dual-energy computed tomography (DECT) spectral parameters and build models to predict VEDM. Methods: Consecutive patients suspected of having CRC and with a clinical indication for enhanced CT from April 2021 to July 2022 at a single institution were prospectively enrolled to undertake spectral CT scanning. The spectral features were extracted by two reviewers and intraclass correlation coefficient (ICC) was used for interobserver agreement evaluation. A total of 16 spectral parameters, including unenhanced effective atomic number, triphasic iodine concentrations (ICs)/normalized ICs (NICs)-A/V/E/1/NIC-A/V/E/spectral curve slopes (λ-A/V/E), two arterial enhancement fractions (AEFs), and venous enhancement fraction (VEF), were determined for analysis. Patients with and without VEDM after surgery were matched using propensity score matching (PSM). The diagnostic performance was assessed using the area under the curve (AUC). Models of multiple modalities were generated. Results: In total, 222 patients were included (141 males, age range, 32-83 years) and 13 patients developed VEDM. Interobserver agreement ranged from good to excellent (ICC, 0.773-0.964). A total of three spectral parameters (VEF, λ-V, and 1/NIC-V) exhibited significant discriminatory ability (P<0.05) in predicting VEDM, with AUCs of 0.822 [95% confidence interval (CI): 0.667-0.926], 0.738 (95% CI: 0.573-0.866), and 0.713 (95% CI: 0.546-0.846) and optimal cutoff points of 67.16%, 2.46, and 2.44, respectively. The performance of these spectral parameters was validated in the entire cohort; the combined spectral model showed comparable efficiency to the combined clinical model [AUC, 0.771 (95% CI: 0.622-0.919) vs. 0.779 (95% CI: 0.663-0.894), P>0.05]; the clinical-spectral model achieved further improved AUC of 0.887 (95% CI: 0.812-0.962), which was significantly higher than the combined clinical model (P=0.015), yet not superior to the combined spectral model (P=0.078). Conclusions: Novel spectral parameters showed potential in predicting VEDM in CRC following surgery in this preliminary study, which were closely related with spectral perfusion in the venous phase. However, further studies with larger samples are warranted.

A multiple-time-scale comparative study for the added value of magnetic resonance imaging-based radiomics in predicting pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer.

Objective: Radiomics based on magnetic resonance imaging (MRI) shows potential for prediction of therapeutic effect to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC); however, thorough comparison between radiomics and traditional models is deficient. We aimed to construct multiple-time-scale (pretreatment, posttreatment, and combined) radiomic models to predict pathological complete response (pCR) and compare their utility to those of traditional clinical models. Methods: In this research, 165 LARC patients undergoing nCRT followed by surgery were enrolled retrospectively, which were divided into training and testing sets in the ratio of 7:3. Morphological features on pre- and posttreatment MRI, coupled with clinical data, were evaluated by univariable and multivariable logistic regression analysis for constructing clinical models. Radiomic parameters were derived from pre- and posttreatment T2- and diffusion-weighted images to develop the radiomic signatures. The clinical-radiomics models were then generated. All the models were developed in the training set and then tested in the testing set, the performance of which was assessed using the area under the receiver operating characteristic curve (AUC). Radiomic models were compared with the clinical models with the DeLong test. Results: One hundred and sixty-five patients (median age, 55 years; age interquartile range, 47-62 years; 116 males) were enrolled in the study. The pretreatment maximum tumor length, posttreatment maximum tumor length, and magnetic resonance tumor regression grade were selected as independent predictors for pCR in the clinical models. In the testing set, the pre- and posttreatment and combined clinical models generated AUCs of 0.625, 0.842, and 0.842 for predicting pCR, respectively. The MRI-based radiomic models performed reasonably well in predicting pCR, but neither the pure radiomic signatures (AUCs, 0.734, 0.817, and 0.801 for the pre- and posttreatment and combined radiomic signatures, respectively) nor the clinical-radiomics models (AUCs, 0.734, 0.860, and 0.801 for the pre- and posttreatment and combined clinical-radiomics models, respectively) showed significant added value compared with the clinical models (all P > 0.05). Conclusion: The MRI-based radiomic models exhibited no definite added value compared with the clinical models for predicting pCR in LARC. Radiomic models can serve as ancillary tools for tailoring adequate treatment strategies.