The Pathogenicity of Fusobacterium nucleatum Modulated by Dietary Fibers-A Possible Missing Link between the Dietary Composition and the Risk of Colorectal Cancer.

The dietary composition has been approved to be strongly associated with the risk of colorectal cancer (CRC), one of the most serious malignancies worldwide, through regulating the gut microbiota structure, thereby influencing the homeostasis of colonic epithelial cells by producing carcinogens, i.e., ammonia or antitumor metabolites, like butyrate. Though butyrate-producing Fusobacterium nucleatum has been considered a potential tumor driver associated with chemotherapy resistance and poor prognosis in CRC, it was more frequently identified in the gut microbiota of healthy individuals rather than CRC tumor tissues. First, within the concentration range tested, the fermentation broth of F. nucleatum exhibited no significant effects on Caco-2 and NCM460 cells viability except for a notable up-regulation of the expression of TLR4 (30.70%, p < 0.0001) and Myc (47.67%, p = 0.021) and genes encoding proinflammatory cytokines including IL1B (197.57%, p < 0.0001), IL6 (1704.51%, p < 0.0001), and IL8 (897.05%, p < 0.0001) in Caco-2 cells exclusively. Although no marked effects of polydextrose or fibersol-2 on the growth of F. nucleatum, Caco-2 and NCM460 cells were observed, once culture media supplemented with polydextrose or fibersol-2, the corresponding fermentation broths of F. nucleatum significantly inhibited the growth of Caco-2 cells up to 48.90% (p = 0.0003, 72 h, 10%) and 52.96% (p = 0.0002, 72 h, 10%), respectively in a dose-dependent manner. These two kinds of fibers considerably promoted butyrate production of F. nucleatum up to 205.67% (p < 0.0001, 6% polydextrose at 24 h) and 153.46% (p = 0.0002, 6% fibersol-2 at 12 h), which explained why and how the fermentation broths of F. nucleatum cultured with fibers suppressing the growth of Caco-2 cells. Above findings indicated that dietary fiber determined F. nucleatum to be a carcinogenic or antitumor bacterium, and F. nucleatum played an important role in the association between the dietary composition, primarily the content of dietary fibers, and the risk of CRC.

Efficiency of CT radiomics model in assessing the microsatellite instability of colorectal cancer liver metastasis.

OBJECTIVE: This study aims to investigate the efficiency of a radiomics model in identifying high-frequency microsatellite instability (MSI-H) and microsatellite stability (MSS) of colorectal liver metastasis (CRLM) according to machine learning radiomics features of enhanced CT liver images. MATERIALS AND METHODS: A total of 12 patients with MSI-H CRLM and 96 patients with MSS CRLM were randomly divided into the training group and internal validation group according to the ratio of 7: 3 (training: 75 cases, validation: 33 cases). From the enhanced CT (portal phase) image data of patients, 788 radiomics features were extracted, and a random forest model was established with the optimal features selected. The receiver operating characteristics (ROC) curve analysis was performed to assess the model's diagnostic efficacy. RESULTS: The training group comprised 8 patients with MSI-H CRLM and 67 patients with MSS CRLM, and the internal validation group included 4 patients with MSI-H CRLM and 29 patients with MSS CRLM. After feature selection, 7 radiomics features good for distinguishing MSI-H CRLM and MSS CRLM were screened out. The ROC curve analysis demonstrated that the random forest model had the AUC (area under the ROC curve) value 0.88, accuracy 0.85, sensitivity 0.85, specificity 0.92, and F1 score 0.88 in the training group. The model had an AUC value of 0.75, accuracy of 0.74, sensitivity of 0.81, specificity of 0.85, and F1_score of 0.78 in the internal validation group in identifying the MSI-H from the MSS CRLM. In order to evaluate the robustness of the overall model, the 788 features obtained were all applied to the 5-fold cross-validation, with the model being built on the random forest and analyzed with the ROC curve analysis. The AUC value of the model was 0.86 (P＜0.05), accuracy value 0.91, sensitivity 0.60, and specificity 0.95. CONCLUSION: The random forest prediction model built on the radiometric features extracted from enhanced CT images can be used to identify the MSI-H from the MSS CRLM and may provide effective guidance for clinical immunotherapy of CRLM patients with unknown MSI status.

Prediction of histologic types in solid lung lesions using preoperative contrast-enhanced CT.

OBJECTIVES: This study aimed to develop and validate a predicting model for the histologic classification of solid lung lesions based on preoperative contrast-enhanced CT. METHODS: A primary dataset of 1012 patients from Tianjin Medical University Cancer Institute and Hospital (TMUCIH) was randomly divided into a development cohort (708) and an internal validation cohort (304). Patients from the Second Hospital of Shanxi Medical University (SHSMU) were set as an external validation cohort (212). Two clinical factors (age, gender) and twenty-one characteristics on contrast-enhanced CT were used to construct a multinomial multivariable logistic regression model for the classification of seven common histologic types of solid lung lesions. The area under the receiver operating characteristic curve was used to assess the diagnostic performance of the model in the development and validation cohorts, separately. RESULTS: Multivariable analysis showed that two clinical factors and twenty-one characteristics on contrast-enhanced CT were predictive in lung lesion histologic classification. The mean AUC of the proposed model for histologic classification was 0.95, 0.94, and 0.92 in the development, internal validation, and external validation cohort, respectively. When determining the malignancy of lung lesions based on histologic types, the mean AUC of the model was 0.88, 0.86, and 0.90 in three cohorts. CONCLUSIONS: We demonstrated that by utilizing both clinical and CT characteristics on contrast-enhanced CT images, the proposed model could not only effectively stratify histologic types of solid lung lesions, but also enabled accurate assessment of lung lesion malignancy. Such a model has the potential to avoid unnecessary surgery for patients and to guide clinical decision-making for preoperative treatment. KEY POINTS: • Clinical and CT characteristics on contrast-enhanced CT could be used to differentiate histologic types of solid lung lesions. • Predicting models using preoperative contrast-enhanced CT could accurately assessment of tumor malignancy based on predicted histologic types.

Mining of the Novel Virulent ATP-Binding Cassette Importers in Mycobacterium abscessus by Comparative Genomic Strategy.

Background: The virulent ATP-binding cassette (ABC) importers from Mycobacterium abscessus, the most common native multidrug resistant and emerging opportunistic pathogen in rapidly growing NTM, were explored by comparative genomic study, in view of the fact that the ABC importers of Mycobacterium tuberculosis, responsible for uptaking metals, anions, amino acids, peptides, sugars, and other crucial substances from the host, had been proved to be closely related with the bacillus's virulence, survival in the host macrophages, antibiotic resistance, modulation of host immune system, and so on, although detailed mechanism was unclear. Methods: For virulent ABC importers from M. abscessus predicted by orthology and phylogeny analysis of nucleotide-binding domains (NBDs) of Mycobacterium smegmatis, M. abscessus, and M. tuberculosis, the antibiotic susceptibility of overexpression transformant and knockout mutant was assayed after confirmation by in vitro experiment. Results: Three-domain importers were dominant ones in M. abscessus (60.0%), four-domain ones dominant in M. tuberculosis (87.5%), whereas both types were same in M. smegmatis (41.9%). In the phylogenetic tree, the importers of M. abscessus (53.3%) and M. tuberculosis (62.5%) were mainly distributed in clay A, whereas the clay E was exclusively composed of M. smegmatis NBDs, which hinted possible reprogramming of the transporter system during the pathogen evolution. In clay A, MAB_2178 and others were predicted virulence-associated because of high sequence similarity to M. tuberculosis virulence importers. Conclusions: The importance and complexity of antibiotics resistance mechanisms of MAB_2176-2177-2178 were pointed out by its overexpression enhancing bacterial resistance to ciprofloxacin, clarithromycin, cefoxitin, and sensitivity to amikacin, and knockout having opposite phenotypes.

AI-Radiomics Can Improve Inclusion Criteria and Clinical Trial Performance.

Purpose: Success of clinical trials increasingly relies on effective selection of the target patient populations. We hypothesize that computational analysis of pre-accrual imaging data can be used for patient enrichment to better identify patients who can potentially benefit from investigational agents. Methods: This was tested retrospectively in soft-tissue sarcoma (STS) patients accrued into a randomized clinical trial (SARC021) that evaluated the efficacy of evofosfamide (Evo), a hypoxia activated prodrug, in combination with doxorubicin (Dox). Notably, SARC021 failed to meet its overall survival (OS) objective. We tested whether a radiomic biomarker-driven inclusion/exclusion criterion could have been used to improve the difference between the two arms (Evo + Dox vs. Dox) of the study. 164 radiomics features were extracted from 296 SARC021 patients with lung metastases, divided into training and test sets. Results: A single radiomics feature, Short Run Emphasis (SRE), was representative of a group of correlated features that were the most informative. The SRE feature value was combined into a model along with histological classification and smoking history. This model as able to identify an enriched subset (52%) of patients who had a significantly longer OS in Evo + Dox vs. Dox groups [p = 0.036, Hazard Ratio (HR) = 0.64 (0.42-0.97)]. Applying the same model and threshold value in an independent test set confirmed the significant survival difference [p = 0.016, HR = 0.42 (0.20-0.85)]. Notably, this model was best at identifying exclusion criteria for patients most likely to benefit from doxorubicin alone. Conclusions: The study presents a first of its kind clinical-radiomic approach for patient enrichment in clinical trials. We show that, had an appropriate model been used for selective patient inclusion, SARC021 trial could have met its primary survival objective for patients with metastatic STS.

Performance of a deep learning-based lung nodule detection system as an alternative reader in a Chinese lung cancer screening program.

OBJECTIVE: To evaluate the performance of a deep learning-based computer-aided detection (DL-CAD) system in a Chinese low-dose CT (LDCT) lung cancer screening program. MATERIALS AND METHODS: One-hundred-and-eighty individuals with a lung nodule on their baseline LDCT lung cancer screening scan were randomly mixed with screenees without nodules in a 1:1 ratio (total: 360 individuals). All scans were assessed by double reading and subsequently processed by an academic DL-CAD system. The findings of double reading and the DL-CAD system were then evaluated by two senior radiologists to derive the reference standard. The detection performance was evaluated by the Free Response Operating Characteristic curve, sensitivity and false-positive (FP) rate. The senior radiologists categorized nodules according to nodule diameter, type (solid, part-solid, non-solid) and Lung-RADS. RESULTS: The reference standard consisted of 262 nodules ≥ 4 mm in 196 individuals; 359 findings were considered false positives. The DL-CAD system achieved a sensitivity of 90.1% with 1.0 FP/scan for detection of lung nodules regardless of size or type, whereas double reading had a sensitivity of 76.0% with 0.04 FP/scan (P = 0.001). The sensitivity for detection of nodules ≥ 4 - ≤ 6 mm was significantly higher with DL-CAD than with double reading (86.3% vs. 58.9% respectively; P = 0.001). Sixty-three nodules were only identified by the DL-CAD system, and 27 nodules only found by double reading. The DL-CAD system reached similar performance compared to double reading in Lung-RADS 3 (94.3% vs. 90.0%, P = 0.549) and Lung-RADS 4 nodules (100.0% vs. 97.0%, P = 1.000), but showed a higher sensitivity in Lung-RADS 2 (86.2% vs. 65.4%, P < 0.001). CONCLUSIONS: The DL-CAD system can accurately detect pulmonary nodules on LDCT, with an acceptable false-positive rate of 1 nodule per scan and has higher detection performance than double reading. This DL-CAD system may assist radiologists in nodule detection in LDCT lung cancer screening.

Prognostic value of desmoplastic stromal reaction, tumor budding and tumor-stroma ratio in stage II colorectal cancer.

Background: Existing high-risk factors are insufficient to accurately predict the postoperative recurrence risk of stage II colorectal cancer (CRC). The discovery of additional prognostic markers may be the key to improving the current status of stage II CRC treatment. The present study aimed to evaluate the relationship among desmoplastic reaction (DR), tumor budding (TBd), the tumor-stroma ratio (TSR) and their prognostic value for relapse-free survival (RFS). Methods: In this study, 207 patients with histologically confirmed stage II CRC from January 2012 to August 2018 were retrospectively reviewed from a single center; the cohort was divided into subgroups based on low or high TSR, and low, intermediate or high DR and TBd. Kaplan-Meier curve analysis and log-rank test were applied to examine RFS among subgroups. Univariate and multivariate Cox proportional hazards analyses were used to identify independent factors associated with RFS, and a nomogram was subsequently developed. Results: Abnormal CA242, CEA, T4 stage, presence of hypertension, internal obstruction or perforation (IOP), lymphovascular or/and perineural invasion (PNI), number of nodes examined less than 12, low-frequency microsatellite instability (MSI-L), higher Ki-67 and immature DR were associated with a lower RFS. In multivariable analysis, DR (HR =2.111; 95% CI: 1.184-3.766; P=0.011), LVI (HR =1.919; 95% CI: 1.004-3.669; P=0.049) and PNI (HR =2.724; 95% CI: 1.362-5.448; P=0.005) were prognostic factors for RFS. On this basis, a nomogram that integrated DR and clinicopathologic predictors for predicting RFS passed the calibration and had an area under the curve of 0.826. Conclusions: The prognostic significance of DR outperformed TBd and TSR, therefore, we recommend adding DR as a biomarker in routine pathological reports. The novel nomogram combining these factors may be used as a reliable and effective tool for the prediction of RFS in stage II CRC, thus helping optimize therapeutic regimens under cooperation of oncologists and surgeons. Further multicentric studies are required for validation of this novel, simple and cost-effective prognostic model.

Community-based lung cancer screening by low-dose computed tomography in China: First round results and a meta-analysis.

OBJECTIVE: To evaluate the efficiency of low-dose computed tomography (LDCT) screening for lung cancer in China by analyzing the baseline results of a community-based screening study accompanied with a meta-analysis. METHODS: A first round of community-based lung cancer screening with LDCT was conducted in Tianjin, China, and a systematic literature search was performed to identify LDCT screening and registry-based clinical studies for lung cancer in China. Baseline results in the community-based screening study were described by participant risk level and the lung cancer detection rate was compared with the pooled rate among the screening studies. The percentage of patients per stage was compared between the community-based study and screening and clinical studies. RESULTS: In the community-based study, 5523 participants (43.6% men) underwent LDCT. The lung cancer detection rate was 0.5% (high-risk, 1.2%; low-risk, 0.4%), with stage I disease present in 70.0% (high-risk, 50.0%; low-risk, 83.3%), and the adenocarcinoma present in 84.4% (high-risk, 61.5%; low-risk, 100%). Among all screen-detected lung cancer, women accounted for 8.3% and 66.7% in the high- and low-risk group, respectively. In the screening studies from mainland China, the lung cancer detection rate 0.6% (95 %CI: 0.3%-0.9%) for high-risk populations. The proportions with carcinoma in situ and stage I disease in the screening and clinical studies were 76.4% (95 %CI: 66.3%-85.3%) and 15.2% (95 %CI: 11.8%-18.9%), respectively. CONCLUSIONS: The stage shift of lung cancer due to screening suggests a potential effectiveness of LDCT screening in China. Nearly 70% of screen-detected lung cancers in low-risk populations are identified in women.

A contrast-enhanced-CT-based classification tree model for classifying malignancy of solid lung tumors in a Chinese clinical population.

BACKGROUND: To develop and validate a contrast-enhanced CT based classification tree model for classifying solid lung tumors in clinical patients into malignant or benign. METHODS: Between January 2015 and October 2017, 827 pathologically confirmed solid lung tumors (487 malignant, 340 benign; median size, 27.0 mm, IQR 18.0-39.0 mm) from 827 patients from a dedicated Chinese cancer hospital were identified. Nodules were divided randomly into two groups, a training group (575 cases) and a testing group (252 cases). CT characteristics were collected by two radiologists, and analyzed using a classification and regression tree (CART) model. For validation, we used the decision analysis threshold to evaluate the classification performance of the CART model and radiologist's diagnosis (benign; malignant) in the testing group. RESULTS: Three out of 19 characteristics [margin (smooth; slightly lobulated/lobulated/spiculated), and shape (round/oval; irregular), subjective enhancement (no/uniform enhancement; heterogeneous enhancement)] were automatically generated by the CART model for classifying solid lung tumors. The sensitivity, specificity, PPV, NPV, and diagnostic accuracy of the CART model is 98.5%, 58.1%, 80.6%, 98.6%, 79.8%, and 90.4%, 54.7%, 82.4% 98.5%, 74.2% for the radiologist's diagnosis by using three-threshold decision analysis. CONCLUSIONS: Tumor margin and shape, and subjective tumor enhancement were the most important CT characteristics in the CART model for classifying solid lung tumors as malignant. The CART model had higher discriminatory power than radiologist's diagnosis. The CART model could help radiologists making recommendations regarding follow-up or surgery in clinical patients with a solid lung tumor.

CT-Based Radiomics Signature: A Potential Biomarker for Predicting Postoperative Recurrence Risk in Stage II Colorectal Cancer.

Objective: To evaluate whether a radiomics signature could improve stratification of postoperative risk and prediction of chemotherapy benefit in stage II colorectal cancer (CRC) patients. Material and Methods: This retrospective study enrolled 299 stage II CRC patients from January 2010 to December 2015. Based on preoperative portal venous-phase CT scans, radiomics features were generated and selected to build a radiomics score (Rad-score) using the Least Absolute Shrinkage and Selection Operator (LASSO) method. The minority group was balanced by the synthetic minority over-sampling technique (SMOTE). Predictive models were built with the Rad-score and clinicopathological factors, and the area under the curve (AUC) was used to evaluate their performance. A nomogram was also constructed for predicting 3-year disease-free survival (DFS). The performance of the nomogram was assessed with a concordance index (C-index) and calibration plots. Results: Overall, 114 features were selected to construct the Rad-score, which was significantly associated with the 3-year DFS. Multivariate analysis demonstrated that the Rad-score, CA724 level, mismatch repair status, and perineural invasion were independent predictors of recurrence. Results showed that the Rad-score can classify patients into high-risk and low-risk groups in the training cohort (AUC 0.886) and the validation cohort (AUC 0.874). On this basis, a nomogram that integrated the Rad-score and clinical variables demonstrated superior performance (AUC 0.954, 0.906) than the clinical model alone (AUC 0.765, 0.705) in the training and validation cohorts, respectively. The C-index of the nomogram was 0.872, and the performance was acceptable. Conclusion: Our radiomics-based model can reliably predict recurrence risk in stage II CRC patients and potentially provide complementary prognostic value to the traditional clinicopathological risk factors for better identification of patients who are most likely to benefit from adjuvant therapy. The proposed nomogram promises to be an effective tool for personalized postoperative surveillance for stage II CRC patients.

Optimization of CT windowing for diagnosing invasiveness of adenocarcinoma presenting as sub-solid nodules.

PURPOSE: To evaluate the optimal window setting to diagnose the invasiveness of lung adenocarcinoma in sub-solid nodules (SSNs). METHODS: We retrospectively included 437 SSNs and randomly divided them 3:1 into a training group (327) and a testing group (110). The presence of a solid component was regarded as indicator of invasiveness. At fixed window level (WL) of 35 Hounsfield Units (HU), two readers adjusted the window width (WW) in the training group and recorded once a solid component appeared or disappeared on CT images acquired at 120 kVp. The optimal WW cut-off value to differentiate between invasive and pre-invasive lesions, based on the receiver operating characteristic (ROC) curve, was defined as "core" WW. The diagnostic performances of the mediastinal window setting (WW/WL, 350/35 HU) and core window setting were then compared in the testing group. RESULTS: Of the 437 SSNs, 88 were pre-invasive [17 atypical adenomatous hyperplasia (AAH) and 71 adenocarcinoma in situ (AIS)], 349 were invasive [233 minimally invasive adenocarcinoma (MIA), 116 invasive adenocarcinoma (IA)]. In training group, the core WW of 1175 HU was the optimal cut-off to detect solid components of SSNs (AUC:0.79). In testing group, the sensitivity, specificity, positive, negative predictive value, and diagnostic accuracy for SSN invasiveness were 49.4%, 90.5%, 95.7%, 29.7%, and 57.3% for mediastinal window setting, and 87.6%, 76.2%, 91.6%, 76.2%, and 85.5% for core window setting. CONCLUSION: At 120 kVp, core window setting (WW/WL, 1175/35 HU) outperformed the traditional mediastinal window setting to diagnose the invasiveness of SSNs.

Repeatability of Quantitative Imaging Features in Prostate Magnetic Resonance Imaging.

Background: Multiparametric magnetic resonance imaging (mpMRI) has emerged as a non-invasive modality to diagnose and monitor prostate cancer. Quantitative metrics on the regions of abnormality have shown to be useful descriptors to discriminate clinically significant cancers. In this study, we evaluate the reproducibility of quantitative imaging features using repeated mpMRI on the same patients. Methods: We retrospectively obtained the deidentified records of patients, who underwent two mpMRI scans within 2 weeks of the first baseline scan. The patient records were obtained as deidentified data (including imaging), obtained through the TCIA (The Cancer Imaging Archive) repository and analyzed in our institution with an institutional review board-approved Health Insurance Portability and Accountability Act-compliant retrospective study protocol. Indicated biopsied regions were used as a marker for our study radiologists to delineate the regions of interest. We extracted 307 quantitative features in each mpMRI modality [T2-weighted MR sequence image (T2w) and apparent diffusion coefficient (ADC) with b values of 0 and 1,400 mm/s2] across the two sequential scans. Concordance correlation coefficients (CCCs) were computed on the features extracted from sequential scans. Redundant features were removed by computing the coefficient of determination (R 2) among them and replaced with a feature that had the highest dynamic range within intercorrelated groups. Results: We have assessed the reproducibility of quantitative imaging features among sequential scans and found that habitat region characterization improves repeatability in ADC maps. There were 19 T2w features and two ADC features in radiologist drawn regions (native raw image), compared to 18 T2w and 15 ADC features in habitat regions (sphere), which were reproducible (CCC ≥0.65) and non-redundant (R 2 ≥ 0.99). We also found that z-transformation of the images prior to feature extraction reduced the number of reproducible features with no detrimental effect. Conclusion: We have shown that there are quantitative imaging features that are reproducible across sequential prostate mpMRI acquisition at a preset level of filters. We also found that a habitat approach improves feature repeatability in ADC. A validated set of reproducible image features in mpMRI will allow us to develop clinically useful disease risk stratification, enabling the possibility of using imaging as a surrogate to invasive biopsies.

A Subsolid Nodules Imaging Reporting System (SSN-IRS) for Classifying 3 Subtypes of Pulmonary Adenocarcinoma.

OBJECTIVES: To develop an imaging reporting system for the classification of 3 adenocarcinoma subtypes of computed tomography (CT)-detected subsolid pulmonary nodules (SSNs) in clinical patients. METHODS: Between November 2011 and October 2017, 437 pathologically confirmed SSNs were retrospectively identified. SSNs were randomly divided 2:1 into a training group (291 cases) and a testing group (146 cases). CT-imaging characteristics were analyzed using multinomial univariable and multivariable logistic regression analysis to identify discriminating factors for the 3 adenocarcinoma subtypes (pre-invasive lesions, minimally invasive adenocarcinoma, and invasive adenocarcinoma). These factors were used to develop a classification and regression tree model. Finally, an SSN Imaging Reporting System (SSN-IRS) was constructed based on the optimized classification model. For validation, the classification performance was evaluated in the testing group. RESULTS: Of the CT-derived characteristics of SSNs, qualitative density (nonsolid or part-solid), core (non-core or core), semantic features (pleural indentation, vacuole sign, vascular invasion), and diameter of solid component (≤6 mm or >6 mm), were the most important factors for the SSN-IRS. The total sensitivity, specificity, and diagnostic accuracy of the SSN-IRS was 89.0% (95% confidence interval [CI], 84.8%-92.4%), 74.6% (95% CI, 70.8%-78.1%), and 79.4% (95% CI, 76.5%-82.0%) in the training group and 84.9% (95% CI, 78.1%-90.3%), 68.5% (95% CI, 62.8%-73.8%), and 74.0% (95% CI, 69.6%-78.0%) in the testing group, respectively. CONCLUSIONS: The SSN-IRS can classify 3 adenocarcinoma subtypes using CT-based characteristics of subsolid pulmonary nodules. This classification tool can help clinicians to make follow-up recommendations or decisions for surgery in clinical patients with SSNs.

Comparison of Veterans Affairs, Mayo, Brock classification models and radiologist diagnosis for classifying the malignancy of pulmonary nodules in Chinese clinical population.

BACKGROUND: Several classification models based on Western population have been developed to help clinicians to classify the malignancy probability of pulmonary nodules. However, the diagnostic performance of these Western models in Chinese population is unknown. This paper aimed to compare the diagnostic performance of radiologist evaluation of malignancy probability and three classification models (Mayo Clinic, Veterans Affairs, and Brock University) in Chinese clinical pulmonology patients. METHODS: This single-center retrospective study included clinical patients from Tianjin Medical University Cancer Institute and Hospital with new, CT-detected pulmonary nodules in 2013. Patients with a nodule with diameter of 4-25 mm, and histological diagnosis or 2-year follow-up were included. Analysis of area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA) and threshold of decision analysis was used to evaluate the diagnostic performance of radiologist diagnosis and the three classification models, with histological diagnosis or 2-year follow-up as the reference. RESULTS: In total, 277 patients (286 nodules) were included. Two hundred and seven of 286 nodules (72.4%) in 203 patients were malignant. AUC of the Mayo model (0.77; 95% CI: 0.72-0.82) and Brock model (0.77; 95% CI: 0.72-0.82) were similar to radiologist diagnosis (0.78; 95% CI: 0.73-0.83; P=0.68, P=0.71, respectively). The diagnostic performance of the VA model (AUC: 0.66) was significantly lower than that of radiologist diagnosis (P=0.003). A three-class classifying threshold analysis and DCA showed that the radiologist evaluation had higher discriminatory power for malignancy than the three classification models. CONCLUSIONS: In a cohort of Chinese clinical pulmonology patients, radiologist evaluation of lung nodule malignancy probability demonstrated higher diagnostic performance than Mayo, Brock, and VA classification models. To optimize nodule diagnosis and management, a new model with more radiological characteristics could be valuable.

Computed Tomography-Based Radiomic Features Could Potentially Predict Microsatellite Instability Status in Stage II Colorectal Cancer: A Preliminary Study.

RATIONALE AND OBJECTIVES: To investigate whether quantitative radiomics features extracted from computed tomography (CT) can predict microsatellite instability (MSI) status in an Asian cohort of patients with stage Ⅱ colorectal cancer (CRC). MATERIALS AND METHODS: This retrospective study was approved by our institutional review board, and the informed consent requirement was waived. From March 2016 to March 2018, 119 Chinese patients with pathologically confirmed stage Ⅱ CRC, available MSI status, and preoperative contrast-enhanced CT images were included in this study. Clinical and pathological information was obtained from the institutional database. The radiomics features were extracted from portal venous-phase CT images of segmented volumes of each entire primary tumor by using Matrix Laboratory (MATLAB), and radiomics signatures were generated using the least absolute shrinkage and selection operator logistic regression model. The minority group was balanced via synthetic minority over-sampling technique method. The association between the clinicopathologic characteristics and MSI status was assessed using Student's t test, Chi-square, or Fisher's exact test. The predictive efficacy of MSI status using radiomics features, clinical factors (including age, gender, CT-reported tumor location, differentiation degree of tumor, smoking history, hypertension history, family history of cancer, diabetes history, level of the Ki-67 expression, and laboratory analysis) and the combined models were evaluated, respectively. Predictive performance was evaluated by the area under receiver operating characteristic curve, accuracy, sensitivity, and specificity. RESULTS: MSI status was significantly associated with tumor location (p = 0.043); differentiation degree of tumor (p < 0.0001), hypertension history (p = 0.012), and the level of the Ki-67 expression (p = 0.015). Six radiomics features and 11 clinical characteristics were selected for predicting MSI status. The model that used the combination of clinical factors and radiomics features achieved the overall best performance than using either of the two features alone, yielding the area under the curve, sensitivity, and specificity of 0.752, 0.663, 0.841 for the combined model, 0.598, 0.371, 0.825 for clinical factors alone, and 0.688, 0.517, 0.858 for radiomics features alone, respectively. CONCLUSION: CT-based radiomic features of stage Ⅱ CRC are associated with MSI status. Combining analysis of clinical features and CT features could improve predictive efficacy and could potentially select the patients for individualized therapy noninvasively.

Correlations between the iodine concentrations from dual energy computed tomography and molecular markers Ki-67 and HIF-1α in rectal cancer: A preliminary study.

PURPOSE: To investigate whether dual energy computed tomography (CT) with iodine quantification is correlated with molecular markers Ki-67and hypoxia-inducible factor 1α (HIF-1α)in rectal cancer (RC). MATERIALS AND METHODS: Eighty patients (43 males and 37 females) diagnosed with rectal cancer got pelvic contrast-enhanced CT scan with dual energy computed tomography before any anticancer treatment. Analyse the normalized iodine concentration (NIC) values and CT values at each energy level (40-140 keV) from the virtual monochromatic image of the primary lesions. The postoperative specimens of all 80 patients underwent Ki-67 and HIF-1α immunohistochemistry staining. By SPSS17.0 software package, we analyzed the correlations of NIC values and CT values at each energy level (40-140 keV) with Ki-67 and HIF-1α expression. The receiver operating characteristic (ROC) curves of these dual energy computed tomography parameters were calculated and the diagnostic value were assessed. RESULTS: There was a weak positive correlation between NIC values and carcinoembryonic antigen level (r=0.246, P=0.028) in RC. Both the value and the level of Ki-67 expression were correlated positively with the NIC values (r=0.344, P=0.002 and r=0.248, P=0.026). HIF-1α expression was correlated positively with the NIC values of the RC (r=0.598, P<0.001). The best threshold values of NIC values in diagnosing the expression of HIF-1α was 0.5839. The sensitivity, 78%; specificity, 87%; PPV, 86%; NPV,79%;accuracy, 83%. CONCLUSION: The NIC values on dual energy computed tomography may be used as a measurement of hypoxia in RC and determining the ability of tumor invasion noninvasively.