Incidence rate of occult lymph node metastasis in clinical T1-2N0M0 small cell lung cancer patients and radiomic prediction based on contrast-enhanced CT imaging: a multicenter study : Original research.

BACKGROUND: This study aimed to explore the incidence of occult lymph node metastasis (OLM) in clinical T1 - 2N0M0 (cT1 - 2N0M0) small cell lung cancer (SCLC) patients and develop machine learning prediction models using preoperative intratumoral and peritumoral contrast-enhanced CT-based radiomic data. METHODS: By conducting a retrospective analysis involving 242 eligible patients from 4 centeres, we determined the incidence of OLM in cT1 - 2N0M0 SCLC patients. For each lesion, two ROIs were defined using the gross tumour volume (GTV) and peritumoral volume 15 mm around the tumour (PTV). By extracting a comprehensive set of 1595 enhanced CT-based radiomic features individually from the GTV and PTV, five models were constucted and we rigorously evaluated the model performance using various metrics, including the area under the curve (AUC), accuracy, sensitivity, specificity, calibration curve, and decision curve analysis (DCA). For enhanced clinical applicability, we formulated a nomogram that integrates clinical parameters and the rad_score (GTV and PTV). RESULTS: The initial investigation revealed a 33.9% OLM positivity rate in cT1 - 2N0M0 SCLC patients. Our combined model, which incorporates three radiomic features from the GTV and PTV, along with two clinical parameters (smoking status and shape), exhibited robust predictive capabilities. With a peak AUC value of 0.772 in the external validation cohort, the model outperformed the alternative models. The nomogram significantly enhanced diagnostic precision for radiologists and added substantial value to the clinical decision-making process for cT1 - 2N0M0 SCLC patients. CONCLUSIONS: The incidence of OLM in SCLC patients surpassed that in non-small cell lung cancer patients. The combined model demonstrated a notable generalization effect, effectively distinguishing between positive and negative OLMs in a noninvasive manner, thereby guiding individualized clinical decisions for patients with cT1 - 2N0M0 SCLC.

Design, synthesis, and antiviral activity of 1-aryl-4-arylmethylpiperazine derivatives as Zika virus inhibitors with broad antiviral spectrum.

Zika virus (ZIKV) disease has been given attention due to the risk of congenital microcephaly and neurodevelopmental disorders after ZIKV infection in pregnancy, but no vaccine or antiviral drug is available. Based on a previously reported ZIKV inhibitor ZK22, a series of novel 1-aryl-4-arylmethylpiperazine derivatives was designed, synthesized, and investigated for antiviral activity by quantify cellular ZIKV RNA amount using RT-qPCR method in ZIKV-infected human venous endothelial cells (HUVECs) assay. Structure-activity relationship (SAR) analysis demonstrated that anti-ZIKV activity of 1-aryl-4-arylmethylpiperazine derivatives is not correlated with molecular hydrophobicity, multiple new derivatives with pyridine group to replace the benzonitrile moiety of ZK22 showed stronger antiviral activity, higher ligand lipophilicity efficiency as well as lower cytotoxicity. Two active compounds 13 and 33 were further identified as novel ZIKV entry inhibitors with the potential of oral available. Moreover, both ZK22 and newly active derivatives also possess of obvious inhibition on the viral replication of coronavirus and influenza A virus at low micromolar level. In summary, this work provided better candidates of ZIKV inhibitor for preclinical study and revealed the promise of 1-aryl-4-arylmethylpiperazine chemotype in the development of broad-spectrum antiviral agents.

Imaging segmentation mechanism for rectal tumors using improved U-Net.

OBJECTIVE: In radiation therapy, cancerous region segmentation in magnetic resonance images (MRI) is a critical step. For rectal cancer, the automatic segmentation of rectal tumors from an MRI is a great challenge. There are two main shortcomings in existing deep learning-based methods that lead to incorrect segmentation: 1) there are many organs surrounding the rectum, and the shape of some organs is similar to that of rectal tumors; 2) high-level features extracted by conventional neural networks often do not contain enough high-resolution information. Therefore, an improved U-Net segmentation network based on attention mechanisms is proposed to replace the traditional U-Net network. METHODS: The overall framework of the proposed method is based on traditional U-Net. A ResNeSt module was added to extract the overall features, and a shape module was added after the encoder layer. We then combined the outputs of the shape module and the decoder to obtain the results. Moreover, the model used different types of attention mechanisms, so that the network learned information to improve segmentation accuracy. RESULTS: We validated the effectiveness of the proposed method using 3773 2D MRI datasets from 304 patients. The results showed that the proposed method achieved 0.987, 0.946, 0.897, and 0.899 for Dice, MPA, MioU, and FWIoU, respectively; these values are significantly better than those of other existing methods. CONCLUSION: Due to time savings, the proposed method can help radiologists segment rectal tumors effectively and enable them to focus on patients whose cancerous regions are difficult for the network to segment. SIGNIFICANCE: The proposed method can help doctors segment rectal tumors, thereby ensuring good diagnostic quality and accuracy.

Unveiling Anti-Diabetic Potential of Baicalin and Baicalein from Baikal Skullcap: LC-MS, In Silico, and In Vitro Studies.

Type 2 diabetes mellitus (T2DM) is marked by persistent hyperglycemia, insulin resistance, and pancreatic ß-cell dysfunction, imposing substantial health burdens and elevating the risk of systemic complications and cardiovascular diseases. While the pathogenesis of diabetes remains elusive, a cyclical relationship between insulin resistance and inflammation is acknowledged, wherein inflammation exacerbates insulin resistance, perpetuating a deleterious cycle. Consequently, anti-inflammatory interventions offer a therapeutic avenue for T2DM management. In this study, a herb called Baikal skullcap, renowned for its repertoire of bioactive compounds with anti-inflammatory potential, is posited as a promising source for novel T2DM therapeutic strategies. Our study probed the anti-diabetic properties of compounds from Baikal skullcap via network pharmacology, molecular docking, and cellular assays, concentrating on their dual modulatory effects on diabetes through Protein Tyrosine Phosphatase 1B (PTP1B) enzyme inhibition and anti-inflammatory actions. We identified the major compounds in Baikal skullcap using liquid chromatography-mass spectrometry (LC-MS), highlighting six flavonoids, including the well-studied baicalein, as potent inhibitors of PTP1B. Furthermore, cellular experiments revealed that baicalin and baicalein exhibited enhanced anti-inflammatory responses compared to the active constituents of licorice, a known anti-inflammatory agent in TCM. Our findings confirmed that baicalin and baicalein mitigate diabetes via two distinct pathways: PTP1B inhibition and anti-inflammatory effects. Additionally, we have identified six flavonoid molecules with substantial potential for drug development, thereby augmenting the T2DM pharmacotherapeutic arsenal and promoting the integration of herb-derived treatments into modern pharmacology.

Mulberry Leaf Compounds and Gut Microbiota in Alzheimer's Disease and Diabetes: A Study Using Network Pharmacology, Molecular Dynamics Simulation, and Cellular Assays.

Recently, studies have reported a correlation that individuals with diabetes show an increased risk of developing Alzheimer's disease (AD). Mulberry leaves, serving as both a traditional medicinal herb and a food source, exhibit significant hypoglycemic and antioxidative properties. The flavonoid compounds in mulberry leaf offer therapeutic effects for relieving diabetic symptoms and providing neuroprotection. However, the mechanisms of this effect have not been fully elucidated. This investigation aimed to investigate the combined effects of specific mulberry leaf flavonoids (kaempferol, quercetin, rhamnocitrin, tetramethoxyluteolin, and norartocarpetin) on both type 2 diabetes mellitus (T2DM) and AD. Additionally, the role of the gut microbiota in these two diseases' treatment was studied. Using network pharmacology, we investigated the potential mechanisms of flavonoids in mulberry leaves, combined with gut microbiota, in combating AD and T2DM. In addition, we identified protein tyrosine phosphatase 1B (PTP1B) as a key target for kaempferol in these two diseases. Molecular docking and molecular dynamics simulations showed that kaempferol has the potential to inhibit PTP1B for indirect treatment of AD, which was proven by measuring the IC50 of kaempferol (279.23 µM). The cell experiment also confirmed the dose-dependent effect of kaempferol on the phosphorylation of total cellular protein in HepG2 cells. This research supports the concept of food-medicine homology and broadens the range of medical treatments for diabetes and AD, highlighting the prospect of integrating traditional herbal remedies with modern medical research.

Dual parallel net: A novel deep learning model for rectal tumor segmentation via CNN and transformer with Gaussian Mixture prior.

Segmentation of rectal cancerous regions from Magnetic Resonance (MR) images can help doctor define the extent of the rectal cancer and judge the severity of rectal cancer, so rectal tumor segmentation is crucial to improve the accuracy of rectal cancer diagnosis. However, accurate segmentation of rectal cancerous regions remains a challenging task due to the shape of rectal tumor has significant variations and the tumor and surrounding tissue are indistinguishable. In addition, in the early research on rectal tumor segmentation, most deep learning methods were based on convolutional neural networks (CNNs), and traditional CNN have small receptive field, which can only capture local information while ignoring the global information of the image. Nevertheless, the global information plays a crucial role in rectal tumor segmentation, so traditional CNN-based methods usually cannot achieve satisfactory segmentation results. In this paper, we propose an encoder-decoder network named Dual Parallel Net (DuPNet), which fuses transformer and classical CNN for capturing both global and local information. Meanwhile, as for capture features at different scales as well as to avoid accuracy loss and parameters reduction, we design a feature adaptive block (FAB) in skip connection between encoder and decoder. Furthermore, in order to utilize the apriori information of rectal tumor shape effectively, we design a Gaussian Mixture prior and embed it in self-attention mechanism of the transformer, leading to robust feature representation and accurate segmentation results. We have performed extensive ablation experiments to verify the effectiveness of our proposed dual parallel encoder, FAB and Gaussian Mixture prior on the dataset from the Shanxi Cancer Hospital. In the experimental comparison with the state-of-the-art methods, our method achieved a Mean Intersection over Union (MIoU) of 89.34% on the test set. In addition to that, we evaluated the generalizability of our method on the dataset from Xinhua Hospital, the promising results verify the superiority of our method.

Design, synthesis and biological evaluation of 2-((4-sulfamoylphenyl)amino)-pyrrolo[2,3-d]pyrimidine derivatives as CDK inhibitors.

To explore the potential use of CDK inhibitors in pancreatic ductal adenocarcinoma (PDAC) therapy, a series of novel 2-((4-sulfamoylphenyl)amino)-pyrrolo[2,3-d]pyrimidine derivatives was designed, synthesised, and investigated for inhibition on both CDK kinase activity and cellular proliferation of pancreatic cancer. Most of new sulphonamide-containing derivatives demonstrated strong inhibitory activity on CDK9 and obvious anti-proliferative activity in cell culture. Moreover, two new compounds suppressed cell proliferation of multiple human pancreatic cancer cell lines. The most potent compound 2g inhibited cancer cell proliferation by blocking Rb phosphorylation and induced apoptosis via downregulation of CDK9 downstream proteins Mcl-1 and c-Myc in MIA PaCa-2 cells. CDK9 knockdown experiment suggests its anti-proliferative activity is mainly mediated by CDK9. Additionally, 2g displayed moderate tumour inhibition effect in AsPC-1 derived xenograft mice model. Altogether, this study provided a new start for further optimisation to develop potential CDK inhibitor candidates for PDAC treatment by alone or combination use.

Molecular Dynamics Simulations Combined with Markov Model to Explore the Effect of Allosteric Inhibitor Binding on Bromodomain-Containing Protein 4.

Bromodomain-Containing Protein 4 (BRD4) can play an important role in gene transcriptional regulation of tumor development and survival by participating in histone modification epigenetic mechanism. Although it has been reported that novel allosteric inhibitors such as ZL0590 have a high affinity with target protein BRD4 and good efficacy, their inhibitory mechanism has not been studied further. The aim of this study was to reveal the inhibition mechanism of allosteric inhibitor ZL0590 on Free-BRD4 and BRD4 binding MS436 (orthosteric inhibitor) by molecular dynamics simulation combined with a Markov model. Our results showed that BRD4-ZL0590 led to α-helices formation of 100-105 compared with Free-BRD4; the combination of MS436 caused residues 30-40 and 95-105 to form α-helices, while the combination of allosteric inhibitors untangled the α-helices formed by the MS436. The results of Markov flux analysis showed that the binding process of inhibitors mainly involved changes in the degree of α-helices at ZA loop. The binding of ZL0590 reduced the distance between ZA loop and BC loop, blocked the conformation at the active site, and inhibited the binding of MS436. After the allosteric inhibitor binding, the MS436 that could normally penetrate into the interior of the pocket was floating on the edge of the active pocket and did not continue to penetrate into the active pocket as expected. In summary, we provide a theoretical basis for the inhibition mechanism of ZL0590 against BRD4, which can be used as a reference for improving the development of drug targets for cancer therapy.

Network Pharmacology Combined with Machine Learning to Reveal the Action Mechanism of Licochalcone Intervention in Liver Cancer.

There are reports indicating that licochalcones can inhibit the proliferation, migration, and invasion of cancer cells by promoting the expression of autophagy-related proteins, inhibiting the expression of cell cycle proteins and angiogenic factors, and regulating autophagy and apoptosis. This study aims to reveal the potential mechanisms of licochalcone A (LCA), licochalcone B (LCB), licochalcone C (LCC), licochalcone D (LCD), licochalcone E (LCE), licochalcone F (LCF), and licochalcone G (LCG) inhibition in liver cancer through computer-aided screening strategies. By using machine learning clustering analysis to search for other structurally similar components in licorice, quantitative calculations were conducted to collect the structural commonalities of these components related to liver cancer and to identify key residues involved in the interactions between small molecules and key target proteins. Our research results show that the seven licochalcones molecules interfere with the cancer signaling pathway via the NF-κB signaling pathway, PDL1 expression and PD1 checkpoint pathway in cancer, and others. Glypallichalcone, Echinatin, and 3,4,3',4'-Tetrahydroxy-2-methoxychalcone in licorice also have similar structures to the seven licochalcones, which may indicate their similar effects. We also identified the key residues (including ASN364, GLY365, TRP366, and TYR485) involved in the interactions between ten flavonoids and the key target protein (nitric oxide synthase 2). In summary, we provide valuable insights into the molecular mechanisms of the anticancer effects of licorice flavonoids, providing new ideas for the design of small molecules for liver cancer drugs.

An integrated deep learning model for the prediction of pathological complete response to neoadjuvant chemotherapy with serial ultrasonography in breast cancer patients: a multicentre, retrospective study.

BACKGROUND: The biological phenotype of tumours evolves during neoadjuvant chemotherapy (NAC). Accurate prediction of pathological complete response (pCR) to NAC in the early-stage or posttreatment can optimize treatment strategies or improve the breast-conserving rate. This study aimed to develop and validate an autosegmentation-based serial ultrasonography assessment system (SUAS) that incorporated serial ultrasonographic features throughout the NAC of breast cancer to predict pCR. METHODS: A total of 801 patients with biopsy-proven breast cancer were retrospectively enrolled from three institutions and were split into a training cohort (242 patients), an internal validation cohort (197 patients), and two external test cohorts (212 and 150 patients). Three imaging signatures were constructed from the serial ultrasonographic features before (pretreatment signature), during the first-second cycle of (early-stage treatment signature), and after (posttreatment signature) NAC based on autosegmentation by U-net. The SUAS was constructed by subsequently integrating the pre, early-stage, and posttreatment signatures, and the incremental performance was analysed. RESULTS: The SUAS yielded a favourable performance in predicting pCR, with areas under the receiver operating characteristic curve (AUCs) of 0.927 [95% confidence interval (CI) 0.891-0.963] and 0.914 (95% CI 0.853-0.976), compared with those of the clinicopathological prediction model [0.734 (95% CI 0.665-0.804) and 0.610 (95% CI 0.504-0.716)], and radiologist interpretation [0.632 (95% CI 0.570-0.693) and 0.724 (95% CI 0.644-0.804)] in the external test cohorts. Furthermore, similar results were also observed in the early-stage treatment of NAC [AUC 0.874 (0.793-0.955)-0.897 (0.851-0.943) in the external test cohorts]. CONCLUSIONS: We demonstrate that autosegmentation-based SAUS integrating serial ultrasonographic features throughout NAC can predict pCR with favourable performance, which can facilitate individualized treatment strategies.

Multiparametric MRI-based radiomics nomogram for preoperative prediction of lymphovascular invasion and clinical outcomes in patients with breast invasive ductal carcinoma.

OBJECTIVE: To develop a multiparametric MRI-based radiomics nomogram for predicting lymphovascular invasion (LVI) status and clinical outcomes in patients with breast invasive ductal carcinoma (IDC). METHODS: A total of 160 patients with pathologically confirmed breast IDC (training cohort: n = 112; validation cohort: n = 48) who underwent preoperative breast MRI were included. Imaging features were extracted from T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC) maps, and contrast-enhanced T1-weighted imaging (cT1WI) sequences. A four-step procedure was applied for feature selection and radiomics signature building. Univariate and multivariate logistic regression analyses were conducted to identify the features associated with LVI, which were then incorporated into the radiomics nomogram. The performance of the nomogram was evaluated by its discrimination, calibration, and clinical usefulness. Kaplan-Meier survival curves based on the two radiomics models were used to estimate disease-free survival (DFS). RESULTS: The fusion radiomics signature of the T2WI, cT1WI, and ADC maps achieved a better predictive efficacy for LVI than either of them alone. The proposed radiomics nomogram, incorporating the fusion radiomics signature and MRI-reported peritumoral edema, showed satisfactory capabilities of calibration and discrimination in both training and validation datasets, with AUCs of 0.919 (95% CI: 0.871-0.967) and 0.863 (95% CI: 0.726-0.999), respectively. The radiomics signature and nomogram-defined high-risk groups had a shorter DFS than those in the low-risk groups (both p < 0.05). Higher Rad-scores were independently associated with a worse DFS in the whole cohort (p < 0.05). CONCLUSIONS: The proposed nomogram, incorporating multiparametric MRI-based radiomics signature and MRI-reported peritumoral edema, achieved a satisfactory preoperative prediction of LVI and clinical outcomes in IDC patients. KEY POINTS: • The fusion radiomics signature of the T2WI, cT1WI, and ADC maps achieved a better predictive efficacy for LVI than either of them alone. • The proposed nomogram achieved a favorable prediction of LVI in IDC patients with AUCs of 0.919 and 0.863 in the training and validation datasets, respectively. • The radiomics model could classify patients into high- and low-risk groups with significant differences in DFS.

Deep learning predicts resistance to neoadjuvant chemotherapy for locally advanced gastric cancer: a multicenter study.

BACKGROUND: Accurate pre-treatment prediction of neoadjuvant chemotherapy (NACT) resistance in patients with locally advanced gastric cancer (LAGC) is essential for timely surgeries and optimized treatments. We aim to evaluate the effectiveness of deep learning (DL) on computed tomography (CT) images in predicting NACT resistance in LAGC patients. METHODS: A total of 633 LAGC patients receiving NACT from three hospitals were included in this retrospective study. The training and internal validation cohorts were randomly selected from center 1, comprising 242 and 104 patients, respectively. The external validation cohort 1 comprised 128 patients from center 2, and the external validation cohort 2 comprised 159 patients from center 3. First, a DL model was developed using ResNet-50 to predict NACT resistance in LAGC patients, and the gradient-weighted class activation mapping (Grad-CAM) was assessed for visualization. Then, an integrated model was constructed by combing the DL signature and clinical characteristics. Finally, the performance was tested in internal and external validation cohorts using area under the receiver operating characteristic (ROC) curves (AUC). RESULTS: The DL model achieved AUCs of 0.808 (95% CI 0.724-0.893), 0.755 (95% CI 0.660-0.850), and 0.752 (95% CI 0.678-0.825) in validation cohorts, respectively, which were higher than those of the clinical model. Furthermore, the integrated model performed significantly better than the clinical model (P < 0.05). CONCLUSIONS: A CT-based model using DL showed promising performance for predicting NACT resistance in LAGC patients, which could provide valuable information in terms of individualized treatment.

Contrast-enhanced CT-based radiomics model for differentiating risk subgroups of thymic epithelial tumors.

BACKGROUND: To validate a contrast-enhanced CT (CECT)-based radiomics model (RM) for differentiating various risk subgroups of thymic epithelial tumors (TETs). METHODS: A retrospective study was performed on 164 patients with TETs who underwent CECT scans before treatment. A total of 130 patients (approximately 79%, from 2012 to 2018) were designated as the training set, and 34 patients (approximately 21%, from 2019 to 2021) were designated as the testing set. The analysis of variance and least absolute shrinkage and selection operator algorithm methods were used to select the radiomics features. A logistic regression classifier was constructed to identify various subgroups of TETs. The predictive performance of RMs was evaluated based on receiver operating characteristic (ROC) curve analyses. RESULTS: Two RMs included 16 and 13 radiomics features to identify three risk subgroups of traditional risk grouping [low-risk thymomas (LRT: Types A, AB and B1), high-risk thymomas (HRT: Types B2 and B3), thymic carcinoma (TC)] and improved risk grouping [LRT* (Types A and AB), HRT* (Types B1, B2 and B3), TC], respectively. For traditional risk grouping, the areas under the ROC curves (AUCs) of LRT, HRT, and TC were 0.795, 0.851, and 0.860, respectively, the accuracy was 0.65 in the training set, the AUCs were 0.621, 0.754, and 0.500, respectively, and the accuracy was 0.47 in the testing set. For improved risk grouping, the AUCs of LRT*, HRT*, and TC were 0.855, 0.862, and 0.869, respectively, and the accuracy was 0.72 in the training set; the AUCs were 0.778, 0.716, and 0.879, respectively, and the accuracy was 0.62 in the testing set. CONCLUSIONS: CECT-based RMs help to differentiate three risk subgroups of TETs, and RM established according to improved risk grouping performed better than traditional risk grouping.

Three-Dimensional Computed Tomography Bronchography and Angiography-Guided Thoracoscopic Segmentectomy for Pulmonary Nodules.

BACKGROUND: Three-dimensional computed tomography bronchography and angiography (3D-CTBA) provides detailed imaging information for pulmonary segmentectomy. This study was performed to verify the feasibility of 3D-CTBA-guided thoracoscopic segmentectomy for the treatment of pulmonary nodules. METHODS: A retrospective analysis was performed on all patients who underwent 3D-CTBA-guided uniport thoracoscopic segmentectomies or subsegmentectomies for pulmonary nodules in the period from May 2019 to May 2020. All of the information related to perioperative management and surgical operations was retrieved from the medical records and operating notes for detailed analysis. RESULTS: A total of 104 eligible operations involving the resection of 110 nodules with diameters in the range of 5-20 mm were included. Under 3D-CTBA guidance, the pulmonary nodules were located with an accuracy of 100% (110/110) and the median resection margin was 24.3 mm (17-33 mm). Additionally, the segmental (subsegmental) bronchi, arteries, and veins were identified with accuracy rates of 100% (104/104), 96.2% (100/104), and 94.2% (98/104), respectively. The postoperative complications consisted of 3 cases of pulmonary infection (2.9%), 6 cases of arrhythmia (5.8%), 2 cases of hemoptysis (1.9%), 4 cases of air leak (3.8%), and 2 cases of subcutaneous emphysema (1.9%). No perioperative death occurred. CONCLUSION: 3D-CTBA-guided thoracoscopic segmentectomy is an effective surgical approach for the management of pulmonary nodules.

Development and validation of a MRI-based radiomics signature for prediction of KRAS mutation in rectal cancer.

OBJECTIVE: To develop a T2-weighted (T2W) image-based radiomics signature for the individual prediction of KRAS mutation status in patients with rectal cancer. METHODS: Three hundred four consecutive patients from center I with pathologically diagnosed rectal adenocarcinoma (training dataset, n = 213; internal validation dataset, n = 91) were enrolled in our retrospective study. The patients from center II (n = 86) were selected as an external validation dataset. A total of 960 imaging features were extracted from high-resolution T2W images for each patient. Five steps, mainly univariate statistical tests, were applied for feature selection. Subsequently, three classification methods, i.e., logistic regression (LR), decision tree (DT), and support vector machine (SVM) algorithm, were applied to develop the radiomics signature for KRAS prediction in the training dataset. The predictive performance was evaluated by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). RESULTS: Seven radiomics features were screened as a KRAS-associated radiomics signature of rectal cancer. Our best prediction model was obtained with SVM classifiers with AUC of 0.722 (95%CI, 0.654-0.790) in the training dataset. This was validated in the internal and external validation datasets with good calibration, and the corresponding AUCs were 0.682 (95% CI, 0.569-0.794) and 0.714 (95% CI, 0.602-0.827), respectively. DCA confirmed its clinical usefulness. CONCLUSIONS: The proposed T2WI-based radiomics signature has a moderate performance to predict KRAS status, and may be useful for supplementing genomic analysis to determine KRAS expression in rectal cancer patients. KEY POINTS: • T2WI-based radiomics showed a moderate diagnostic significance for KRAS status. • The best prediction model was obtained with SVM classifier. • The baseline clinical and histopathological characteristics were not associated with KRAS mutation.

Diffusion kurtosis imaging-derived histogram metrics for prediction of KRAS mutation in rectal adenocarcinoma: Preliminary findings.

BACKGROUND: Although histological examination is the standard method for assessing genetic status, the development of a noninvasive method, which can display the heterogeneity of the whole tumor to supplement genotype analysis, might be important for personalized treatment strategies. PURPOSE: To evaluate the potential role of diffusion kurtosis imaging (DKI)-derived parameters using histogram analysis derived from whole-tumor volumes for prediction of the status of KRAS mutations in patients with rectal adenocarcinoma. STUDY TYPE: Retrospective. SUBJECTS: In all, 148 consecutive patients with histologically confirmed rectal adenocarcinoma who were treated at our institution. SEQUENCE: DKI was performed with a 3.0 T MRI system using a single-shot echo-planar imaging sequence with b values of 0, 700, 1400, and 2100 sec/mm2 . ASSESSMENT: D, K, and apparent diffusion coefficient (ADC) values were measured using whole-tumor volume histogram analysis and were compared between different KRAS mutations status. STATISTICAL TESTS: Student's t-test or Mann-Whitney U-test, and receiver operating characteristic (ROC) curves were used for statistical analysis. RESULTS: All the percentile metrics of ADC and D values were significantly lower in the mutated group than those in the wildtype group (all P < 0.05), except for the minimum value of ADC and D (both P > 0.05), while K-related percentile metrics were higher in the mutated group compared with those in the wildtype group (all P < 0.05). Regarding the comparison of the diagnostic performance of all the histogram metrics, K75th showed the highest AUC value of 0.871, and the corresponding values for sensitivity, specificity, positive predictive value, and negative predictive value were 81.43%, 78.21%, 77.03%, and 82.43%, respectively. DATA CONCLUSION: DKI metrics with whole-tumor volume histogram analysis is associated with KRAS mutations, and thus may be useful for predicting the KRAS status of rectal cancers for guiding targeted therapy. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:930-939.

Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer.

OBJECTIVES: To develop and validate a radiomics predictive model based on pre-treatment multiparameter magnetic resonance imaging (MRI) features and clinical features to predict a pathological complete response (pCR) in patients with locally advanced rectal cancer (LARC) after receiving neoadjuvant chemoradiotherapy (CRT). METHODS: One hundred and eighty-six consecutive patients with LARC (training dataset, n = 131; validation dataset, n = 55) were enrolled in our retrospective study. A total of 1,188 imaging features were extracted from pre-CRT T2-weighted (T2-w), contrast-enhanced T1-weighted (cT1-w) and ADC images for each patient. Three steps including least absolute shrinkage and selection operator (LASSO) regression were performed to select key features and build a radiomics signature. Combining clinical risk factors, a radiomics nomogram was constructed. The predictive performance was evaluated by receiver operator characteristic (ROC) curve analysis, and then assessed with respect to its calibration, discrimination and clinical usefulness. RESULTS: Thirty-one of 186 patients (16.7%) achieved pCR. The radiomics signature derived from joint T2-w, ADC, and cT1-w images, comprising 12 selected features, was significantly associated with pCR status and showed better predictive performance than signatures derived from either of them alone in both datasets. The radiomics nomogram, incorporating the radiomics signature and MR-reported T-stages, also showed good discrimination, with areas under the ROC curves (AUCs) of 0.948 (95% CI, 0.907-0.989) and 0.966 (95% CI, 0.924-1.000), as well as good calibration in both datasets. Decision curve analysis confirmed its clinical usefulness. CONCLUSIONS: This study demonstrated that the pre-treatment radiomics nomogram can predict pCR in patients with LARC and potentially guide treatments to select patients for a "wait-and-see" policy. KEY POINTS: • Radiomics analysis of pre-CRT multiparameter MR images could predict pCR in patients with LARC. • Proposed radiomics signature from joint T2-w, ADC and cT1-w images showed better predictive performance than individual signatures. • Most of the clinical characteristics were unable to predict pCR.

Whole-tumour diffusion kurtosis MR imaging histogram analysis of rectal adenocarcinoma: Correlation with clinical pathologic prognostic factors.

OBJECTIVE: To investigate potential relationships between diffusion kurtosis imaging (DKI)-derived parameters using whole-tumour volume histogram analysis and clinicopathological prognostic factors in patients with rectal adenocarcinoma. MATERIAL AND METHODS: 79 consecutive patients who underwent MRI examination with rectal adenocarcinoma were retrospectively evaluated. Parameters D, K and conventional ADC were measured using whole-tumour volume histogram analysis. Student's t-test or Mann-Whitney U-test, receiver operating characteristic curves and Spearman's correlation were used for statistical analysis. RESULTS: Almost all the percentile metrics of K were correlated positively with nodal involvement, higher histological grades, the presence of lymphangiovascular invasion (LVI) and circumferential margin (CRM) (p<0.05), with the exception of between K10th, K90th and histological grades. In contrast, significant negative correlations were observed between 25th, 50th percentiles and mean values of ADC and D, as well as ADC10th, with tumour T stages (p< 0.05). Meanwhile, lower 75th and 90th percentiles of ADC and D values were also correlated inversely with nodal involvement (p< 0.05). Kmean showed a relatively higher area under the curve (AUC) and higher specificity than other percentiles for differentiation of lesions with nodal involvement. CONCLUSION: DKI metrics with whole-tumour volume histogram analysis, especially K parameters, were associated with important prognostic factors of rectal cancer. KEY POINTS: • K correlated positively with some important prognostic factors of rectal cancer. • K mean showed higher AUC and specificity for differentiation of nodal involvement. • DKI metrics with whole-tumour volume histogram analysis depicted tumour heterogeneity.

Spectral CT imaging in cervical computed tomography angiography: comparison of spectral CT monochromatic imaging and conventional CT polychromatic imaging.

OBJECTIVE: To compare the image quality of spectral CT monochromatic imaging and conventional CT polychromatic imaging for analysing CTAs in patients with cervical cancer. METHODS: In this IRB approved prospective study, 60 patients who had been diagnosed with cervical cancer underwent pelvic arterial CTA between May 2013 and July 2013. They were randomly divided into two groups; one group (30 patients) received 120 kVp polychromatic imaging (conventional CT group) and the other group (30 patients) received spectral CT imaging (spectral CT group), while all patients in both the groups received injections of 1 ml/kg of contrast agent. A total of 101 sets of monochromatic images (40-140 keV) were obtained via data reconstruction in the spectral CT group, and the monochromatic images with the best contrast-to-noise ratio (CNR) between the common iliac artery and pelvic fat (i.e. the best monochromatic energy) were selected. The best monochromatic images for the spectral CT group and the polychromatic images for the conventional CT group were postprocessed and visualised in MIP, VR and CPR mode. The CT attenuation value, noise and CNR of bilateral common iliac arteries were measured with the best monochromatic energy, as well as with 70 keV, in the spectral CT group and in the conventional CT group. The quality of the CT images was evaluated with a 5-point scale. The CTDIvol and the dose-length product (DLP) of the two groups were measured, and the results were statistically analysed. RESULTS: When images were at 50±1 keV, the common iliac artery and pelvic fat had the highest CNR, which was 72.00% higher than the images at 70 keV (P=.001) in the spectral group, and thus, the images at 50±1 keV were considered to have the best monochromatic energy. The average CT value of the internal iliac artery, which had the best monochromatic energy from the spectral CT group, was higher than that of the images from the conventional CT group (603.96±62.68 vs 251.24±28.77; P<.001), and the differences in the CNR (73.97±11.83 vs 45.21±16.63) and the subjective score (3.10±1.73 vs 2.80±1.63) were statistically significant (both P<.05). There were no significant differences in the CTDIvol (10.48±2.86 vs 12.38±1.88 mGy) or the DLP (317.76±95.50 vs 332.25±21.25 mGy cm) between the spectral and the conventional CT groups. CONCLUSION: Monochromatic spectral CT imaging has excellent soft tissue contrast and good spatial resolution and can visualise the arteries and branches supplying the tumours more clearly in patients with cervical cancer. Compared with polychromatic images, monochromatic spectral CT images are higher quality, which helps the treatment of patients with cervical cancer.

The initial experience of the upper abdominal CT angiography using low-concentration contrast medium on dual energy spectral CT.

OBJECTIVE: To investigate the feasibility of using Spectral CT imaging with low contrast medium in abdominal CT angiography (CTA). SUBJECTS AND METHODS: 70 consecutive patients (40 men, 42.6 ± 20.4 years; 30 women, 46.7 ± 18.8 years) with suspected abdominal focal lesions were referred to CTA exam. They were randomly assigned into two groups. Group A: 35 patients underwent conventional CT scan of Tube voltage 120 kVp, automatic current modulation with a Noise Index of 12, ASIR 30%, and injected with Iohexol (350 mgI/ml). Group B: 35 patients underwent Spectral CT Imaging, with Tube current of 600 mA, injected with Iodixanol (270 mgI/ml). The optimal mono-energy keV was achieved using the optimal contrast noise ratio in abdominal aorta at the renal artery level relative to the erector spine muscle. Both groups were injected with an injection rate of 3.5 ml/s, and a contrast volume of 1.5 ml/kg body weight. The Hounsfield units (HU) and noise of the bilateral renal arteries and muscle of both groups, as well as the optimal monochromatic image set of Group B were measured. Two radiologists assessed all images with a 5-points scale. CTDIvol and DLP were recorded. Data were analyzed using student t test. RESULTS: The total iodine intake of Group B was 28% lower than that of Group A. The CNR of abdomen artery, celiac trunk, superior mesenteric artery, and renal artery in spectral group (at the best mono-energy of 53.0 keV) were higher than those in conventional CTA group (p < 0.001). The subjective image quality score of spectral CTA group was also rated higher than conventional CTA group (p < 0.001). CTDIvol, DLP, and effective dose of spectral group were all lower than conventional group, but there were no significant differences (p > 0.05). CONCLUSION: With 28% contrast medium reduction and reduced radiation dose, CT angiography using spectral imaging and lower concentration contrast agent provided better image quality than conventional CTA.