Predicting overall survival and prophylactic cranial irradiation benefit in small-cell lung cancer with CT-based deep learning: A retrospective multicenter study.

BACKGROUND AND PURPOSE: To develop a computed tomography (CT)-based deep learning model to predict overall survival (OS) among small-cell lung cancer (SCLC) patients and identify patients who could benefit from prophylactic cranial irradiation (PCI) based on OS signature risk stratification. MATERIALS AND METHODS: This study retrospectively included 556 SCLC patients from three medical centers. The training, internal validation, and external validation cohorts comprised 309, 133, and 114 patients, respectively. The OS signature was built using a unified fully connected neural network. A deep learning model was developed based on the OS signature. Clinical and combined models were developed and compared with a deep learning model. Additionally, the benefits of PCI were evaluated after stratification using an OS signature. RESULTS: Within the internal and external validation cohorts, the deep learning model (concordance index [C-index] 0.745, 0.733) was far superior to the clinical model (C-index: 0.635, 0.630) in predicting OS, but slightly worse than the combined model (C-index: 0.771, 0.770). Additionally, the deep learning model had excellent calibration, clinical usefulness, and improved accuracy in classifying survival outcomes. Remarkably, patients at high risk had a survival benefit from PCI in both the limited and extensive stages (all P < 0.05), whereas no significant association was observed in patients at low risk. CONCLUSIONS: The CT-based deep learning model exhibited promising performance in predicting the OS of SCLC patients. The OS signature may aid in individualized treatment planning to select patients who may benefit from PCI.

Improved overall image quality in low-dose dual-energy computed tomography enterography using deep-learning image reconstruction.

OBJECTIVE: To demonstrate the clinical advantages of a deep-learning image reconstruction (DLIR) in low-dose dual-energy computed tomography enterography (DECTE) by comparing images with standard-dose adaptive iterative reconstruction-Veo (ASIR-V) images. METHODS: In this Institutional review board approved prospective study, 86 participants who underwent DECTE were enrolled. The early-enteric phase scan was performed using standard-dose (noise index: 8) and images were reconstructed at 5 mm and 1.25 mm slice thickness with ASIR-V at a level of 40% (ASIR-V40%). The late-enteric phase scan used low-dose (noise index: 12) and images were reconstructed at 1.25 mm slice thickness with ASIR-V40%, and DLIR at medium (DLIR-M) and high (DLIR-H). The 70 keV monochromatic images were used for image comparison and analysis. For objective assessment, image noise, artifact index, SNR and CNR were measured. For subjective assessment, subjective noise, image contrast, bowel wall sharpness, mesenteric vessel clarity, and small structure visibility were scored by two radiologists blindly. Radiation dose was compared between the early- and late-enteric phases. RESULTS: Radiation dose was reduced by 50% in the late-enteric phase [(6.31 ± 1.67) mSv] compared with the early-enteric phase [(3.01 ± 1.09) mSv]. For the 1.25 mm images, DLIR-M and DLIR-H significantly improved both objective and subjective image quality compared to those with ASIR-V40%. The low-dose 1.25 mm DLIR-H images had similar image noise, SNR, CNR values as the standard-dose 5 mm ASIR-V40% images, but significantly higher scores in image contrast [5(5-5), P < 0.05], bowel wall sharpness [5(5-5), P < 0.05], mesenteric vessel clarity [5(5-5), P < 0.05] and small structure visibility [5(5-5), P < 0.05]. CONCLUSIONS: DLIR significantly reduces image noise at the same slice thickness, but significantly improves spatial resolution and lesion conspicuity with thinner slice thickness in DECTE, compared to conventional ASIR-V40% 5 mm images, all while providing 50% radiation dose reduction.

Automatic Segmentation and Radiomics for Identification and Activity Assessment of CTE Lesions in Crohn's Disease.

BACKGROUND: The purpose of this article is to develop a deep learning automatic segmentation model for the segmentation of Crohn's disease (CD) lesions in computed tomography enterography (CTE) images. Additionally, the radiomics features extracted from the segmented CD lesions will be analyzed and multiple machine learning classifiers will be built to distinguish CD activity. METHODS: This was a retrospective study with 2 sets of CTE image data. Segmentation datasets were used to establish nnU-Net neural network's automatic segmentation model. The classification dataset was processed using the automatic segmentation model to obtain segmentation results and extract radiomics features. The most optimal features were then selected to build 5 machine learning classifiers to distinguish CD activity. The performance of the automatic segmentation model was evaluated using the Dice similarity coefficient, while the performance of the machine learning classifier was evaluated using the area under the curve, sensitivity, specificity, and accuracy. RESULTS: The segmentation dataset had 84 CTE examinations of CD patients (mean age 31 ±â€…13 years , 60 males), and the classification dataset had 193 (mean age 31 ±â€…12 years , 136 males). The deep learning segmentation model achieved a Dice similarity coefficient of 0.824 on the testing set. The logistic regression model showed the best performance among the 5 classifiers in the testing set, with an area under the curve, sensitivity, specificity, and accuracy of 0.862, 0.697, 0.840, and 0.759, respectively. CONCLUSION: The automated segmentation model accurately segments CD lesions, and machine learning classifier distinguishes CD activity well. This method can assist radiologists in promptly and precisely evaluating CD activity.

The automatic segmentation and radiomics of computed tomography enterography images can assist radiologists in accurately and quickly identifying Crohn's disease lesions and evaluating Crohn's disease activity.

Multiphase CT radiomics nomogram for preoperatively predicting the WHO/ISUP nuclear grade of small (< 4 cm) clear cell renal cell carcinoma.

BACKGROUND: Small (< 4 cm) clear cell renal cell carcinoma (ccRCC) is the most common type of small renal cancer and its prognosis is poor. However, conventional radiological characteristics obtained by computed tomography (CT) are not sufficient to predict the nuclear grade of small ccRCC before surgery. METHODS: A total of 113 patients with histologically confirmed ccRCC were randomly assigned to the training set (n = 67) and the testing set (n = 46). The baseline and CT imaging data of the patients were evaluated statistically to develop a clinical model. A radiomics model was created, and the radiomics score (Rad-score) was calculated by extracting radiomics features from the CT images. Then, a clinical radiomics nomogram was developed using multivariate logistic regression analysis by combining the Rad-score and critical clinical characteristics. The receiver operating characteristic (ROC) curve was used to evaluate the discrimination of small ccRCC in both the training and testing sets. RESULTS: The radiomics model was constructed using six features obtained from the CT images. The shape and relative enhancement value of the nephrographic phase (REV of the NP) were found to be independent risk factors in the clinical model. The area under the curve (AUC) values for the training and testing sets for the clinical radiomics nomogram were 0.940 and 0.902, respectively. Decision curve analysis (DCA) revealed that the radiomics nomogram model was a better predictor, with the highest degree of coincidence. CONCLUSION: The CT-based radiomics nomogram has the potential to be a noninvasive and preoperative method for predicting the WHO/ISUP grade of small ccRCC.

A CT-based radiomics nomogram for predicting the progression-free survival in small cell lung cancer: a multicenter cohort study.

PURPOSE: To develop a radiomics nomogram based on computed tomography (CT) to estimate progression-free survival (PFS) in patients with small cell lung cancer (SCLC) and assess its incremental value to the clinical risk factors for individual PFS estimation. METHODS: 558 patients with pathologically confirmed SCLC were retrospectively recruited from three medical centers. A radiomics signature was generated by using the Pearson correlation analysis, univariate Cox analysis, and multivariate Cox analysis. Association of the radiomics signature with PFS was evaluated. A radiomics nomogram was developed based on the radiomics signature, then its calibration, discrimination, reclassification, and clinical usefulness were evaluated. RESULTS: In total, 6 CT radiomics features were finally selected. The radiomics signature was significantly associated with PFS (hazard ratio [HR] 4.531, 95% confidence interval [CI] 3.524-5.825, p < 0.001). Incorporating the radiomics signature into the radiomics nomogram resulted in better performance for the estimation of PFS (concordance index [C-index] 0.799) than with the clinical nomogram (C-index 0.629), as well as high 6 months and 12 months area under the curves of 0.885 and 0.846, respectively. Furthermore, the radiomics nomogram also significantly improved the classification accuracy for PFS outcomes, based on the net reclassification improvement (33.7%, 95% CI 0.216-0.609, p < 0.05) and integrated discrimination improvement (22.7%, 95% CI 0.168-0.278, p < 0.05). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the clinical nomogram. CONCLUSION: A CT-based radiomics nomogram exhibited a promising performance for predicting PFS in patients with SCLC, which could provide valuable information for individualized treatment.

Erratum to "Development and validation of a clinical radiomics nomogram to predict secondary loss of response to infliximab in Crohn's disease patients" [Heliyon 9(4) (April 2023) e14594].

[This corrects the article DOI: 10.1016/j.heliyon.2023.e14594.].

Radiomics Analysis of Breast Lesions in Combination with Coronal Plane of ABVS and Strain Elastography.

Background: Breast cancer is the most common tumor globally. Automated Breast Volume Scanner (ABVS) and strain elastography (SE) can provide more useful breast information. The use of radiomics combined with ABVS and SE images to predict breast cancer has become a new focus. Therefore, this study developed and validated a radiomics analysis of breast lesions in combination with coronal plane of ABVS and SE to improve the differential diagnosis of benign and malignant breast diseases. Patients and Methods: 620 pathologically confirmed breast lesions from January 2017 to August 2021 were retrospectively analyzed and randomly divided into a training set (n=434) and a validation set (n=186). Radiomic features of the lesions were extracted from ABVS, B-ultrasound, and strain elastography (SE) images, respectively. These were then filtered by Gradient Boosted Decision Tree (GBDT) and multiple logistic regression. The ABVS model is based on coronal plane features for the breast, B+SE model is based on features of B-ultrasound and SE, and the multimodal model is based on features of three examinations. The evaluation of the predicted performance of the three models used the receiver operating characteristic (ROC) and decision curve analysis (DCA). Results: The area under the curve, accuracy, specificity, and sensitivity of the multimodal model in the training set are 0.975 (95% CI:0.959-0.991),93.78%, 92.02%, and 96.49%, respectively, and 0.946 (95% CI:0.913 -0.978), 87.63%, 83.93%, and 93.24% in the validation set, respectively. The multimodal model outperformed the ABVS model and B+SE model in both the training (P < 0.001, P = 0.002, respectively) and validation sets (P < 0.001, P = 0.034, respectively). Conclusion: Radiomics from the coronal plane of the breast lesion provide valuable information for identification. A multimodal model combination with radiomics from ABVS, B-ultrasound, and SE could improve the diagnostic efficacy of breast masses.

Development and validation of a clinical radiomics nomogram to predict secondary loss of response to infliximab in Crohn's disease patients.

Background: Infliximab (IFX) is the first-line treatment for Crohn's disease (CD). However, the secondary loss of response (LOR) is common in IFX therapy. Therefore, non-invasive assessment of LOR in CD patients is the goal pursued by clinicians. Methods: A multicenter study involving 181 CD patients was conducted, with patients being split into a training cohort (n = 102), testing cohort (n = 45), and validation cohort (n = 34). The study evaluated various clinical factors to establish a clinical model, and a radiomics signature was constructed based on reproducible features from computed tomography enterography (CTE). Logistic regression modeling was used to create models based on the radiomics signature and significant clinical factors, with the receiver operating characteristic curve (ROC) used to compare their performance. Results: The study found that 64 of the 181 CD patients included experienced secondary LOR. The radiomics signature performed well in predicting secondary LOR, showing good discrimination in the training cohort (AUC [area under the curve], 0.947; 95% confidence interval [CI], 0.910-0.976), the testing cohort (AUC, 0.860; 95% CI, 0.768-0.941), and the validation cohort (AUC, 0.921; 95% CI: 0.831-1.000). Decision curve analysis (DCA) demonstrated the clinical value of the radiomics nomogram. Conclusions: The CTE-based radiomics model showed good performance in predicting secondary LOR in CD patients. The nomogram can help clinicians choose alternative biologics early for CD patients.

CTE-Based Radiomics Models Can Identify Mucosal Healing in Patients with Crohn's Disease.

RATIONALE AND OBJECTIVES: To develop computed tomography enterography (CTE)-based radiomics models to assess mucosal healing (MH) in patients with Crohn's disease (CD). MATERIALS AND METHODS: CTE images were retrospectively collected from 92 confirmed cases of CD at the post-treatment review. Patients were randomly divided into developing (n = 73) and testing (n = 19) groups. Radiomics features were extracted from the enteric phase images, and the least absolute shrinkage and selection operator (LASSO) logistic regression was applied for feature selection using 5-fold cross-validation on the developing group. The selected features were further identified from the top-ranked features and used to create improved radiomics models. Machine learning models were constructed to compare radiomics models with different radiomics features. The area under the ROC curve (AUC) was calculated to assess the predictive performance for identifying MH in CD. RESULTS: Among the 92 CD patients included in our study, 36 patients achieved MH. The AUC of the radiomics model 1, which was based on the 26 selected radiomics features, was 0.976 for evaluating MH in the testing cohort. The AUCs of radiomics models 2 and 4, based on the top 10 and top 5 positive and negative radiomics features, were 0.974 and 0.952 in the testing cohort, respectively. The AUC of the radiomics model 3, built by removing features with r > 0.5, was 0.956 in the testing cohort. The clinical utility of the clinical radiomics nomogram was confirmed by the decision curve analysis (DCA). CONCLUSION: The CTE-based radiomics models have demonstrated favorable performance in assessing MH in patients with CD. Radiomics features can be used as a promising imaging biomarker for MH.

The Role of Fascin-1 in Human Urologic Cancers: A Promising Biomarker or Therapeutic Target?

Human cancer statistics show that an increased incidence of urologic cancers such as bladder cancer, prostate cancer, and renal cell carcinoma. Due to the lack of early markers and effective therapeutic targets, their prognosis is poor. Fascin-1 is an actin-binding protein, which functions in the formation of cell protrusions by cross-linking with actin filaments. Studies have found that fascin-1 expression is elevated in most human cancers and is related to outcomes such as neoplasm metastasis, reduced survival, and increased aggressiveness. Fascin-1 has been considered as a potential therapeutic target for urologic cancers, but there is no comprehensive review to evaluate these studies. This review aimed to provide an enhanced literature review, outline, and summarize the mechanism of fascin-1 in urologic cancers and discuss the therapeutic potential of fascin-1 and the possibility of its use as a potential marker. We also focused on the correlation between the overexpression of fascin-1 and clinicopathological parameters. Mechanistically, fascin-1 is regulated by several regulators and signaling pathways (such as long noncoding RNA, microRNA, c-Jun N-terminal kinase, and extracellular regulated protein kinases). The overexpression of fascin-1 is related to clinicopathologic parameters such as pathological stage, bone or lymph node metastasis, and reduced disease-free survival. Several fascin-1 inhibitors (G2, NP-G2-044) have been evaluated in vitro and in preclinical models. The study proved the promising potential of fascin-1 as a newly developing biomarker and a potential therapeutic target that needs further investigation. The data also highlight the inadequacy of fascin-1 to serve as a novel biomarker for prostate cancer.

Mucosal healing assessment in Crohn's disease with normalized iodine concentration from dual-energy CT enterography: comparison with endoscopy.

OBJECTIVES: Mucosal healing (MH) is an important goal in the treatment of patients with Crohn's disease (CD). Noninvasive assessment of MH with normalized iodine concentration (NIC) is unknown. METHODS: In this retrospective study, 94 patients with diagnosed CD underwent endoscopy and dual-energy CT enterography (DECTE) at the post-infliximab treatment review. Two radiologists reviewed DECTE images by consensus for assessing diseased bowel segments of the colon or terminal ileum, and the NIC was measured. Patients were divided into transmural healing (TH), MH and non-MH groups. The diagnostic performance of the MH and non-MH groups with clinical factors and NIC was assessed utilizing receiver operating characteristic (ROC) curve analysis. RESULTS: Of the 94 patients included in our study, 8 patients achieved TH, 34 patients achieved MH, and 52 patients did not achieve MH at the post-IFX treatment review. The area under the ROC curve (AUC), sensitivity, specificity, and accuracy values were 0.929 (95% confidence interval [CI] 0.883-0.967), 0.853, 0.827, and 0.837, respectively, for differentiating MHs from non-MHs, and the optimal NIC threshold was 0.448. The AUC of the combined model for distinguishing MHs from non-MHs in CD patients, which was based on the NIC and calprotectin, was 0.964 (95% CI 0.935-0.987). CONCLUSIONS: The normalized iodine concentration measurement in DECTE has good performance in assessment MH in patients with CD. Iodine concentration from DECTE can be used as a radiologic marker for MH.

Characterizing e-Cigarette-Related Videos on TikTok: Observational Study.

BACKGROUND: As a popular social networking platform for sharing short videos, TikTok has been widely used for sharing e-cigarettes or vaping-related videos, especially among the youth. OBJECTIVE: This study aims to characterize e-cigarette or vaping-related videos and their user engagement on TikTok through descriptive analysis. METHODS: From TikTok, a total of 417 short videos, posted between October 4, 2018, and February 27, 2021, were collected using e-cigarette or vaping-related hashtags. Two human coders independently hand-coded the video category and the attitude toward vaping (provaping or antivaping) for each vaping-related video. The social media user engagement measures (eg, the comment count, like count, and share count) for each video category were compared within provaping and antivaping groups. The user accounts posting these videos were also characterized. RESULTS: Among 417 vaping-related TikTok videos, 387 (92.8%) were provaping, and 30 (7.2%) were antivaping videos. Among provaping TikTok videos, the most popular category is vaping tricks (n=107, 27.65%), followed by advertisement (n=85, 21.95%), customization (n=75, 19.38%), TikTok trend (n=70, 18.09%), others (n=44, 11.37%), and education (n=6, 1.55%). By comparison, videos showing the TikTok trend had significantly higher user engagement (like count per video) than other provaping videos. Antivaping videos included 15 (50%) videos with the TikTok trend, 10 (33.33%) videos on education, and 5 (16.67%) videos about others. Videos with education have a significantly lower number of likes than other antivaping videos. Most TikTok users posting vaping-related videos are personal accounts (119/203, 58.62%). CONCLUSIONS: Vaping-related TikTok videos are dominated by provaping videos focusing on vaping tricks, advertisement, customization, and TikTok trend. Videos with the TikTok trend have higher user engagement than other video categories. Our findings provide important information on vaping-related videos shared on TikTok and their user engagement levels, which might provide valuable guidance on future policy making, such as possible restrictions on provaping videos posted on TikTok, as well as how to effectively communicate with the public about the potential health risks of vaping.

Automatic Breast Volume Scanner and B-Ultrasound-Based Radiomics Nomogram for Clinician Management of BI-RADS 4A Lesions.

RATIONALE AND OBJECTIVES: To develop and validate a nomogram for predicting the risk of malignancy of breast imaging reporting and data system (BI-RADS) 4A lesions to reduce unnecessary invasive examinations. MATERIALS AND METHODS: From January 2017 to July 2021, 190 cases of 4A lesions included in this study were divided into training and validation sets in a ratio of 8:2. Radiomics features were extracted from sonograms by Automatic Breast Volume Scanner (ABVS) and B-ultrasound. We constructed the radiomics model and calculated the rad-scores. Univariate and multivariate logistic regressions were used to assess demographics and lesion elastography values (virtual touch tissue image, shear wave velocity) and to develop clinical model. A clinical radiomics model was developed using rad-score and independent clinical factors, and a nomogram was plotted. Nomogram performance was evaluated using discrimination, calibration, and clinical utility. RESULTS: The nomogram included rad-score, age, and elastography, and showed good calibration. In the training set, the area under the receiver operating characteristic curve (AUC) of the clinical radiomics model (0.900, 95% confidence interval (CI): 0.843-0.958) was superior to that of the radiomics model (0.860, 95% CI: 0.799-0.921) and clinical model (0.816, 95% CI: 0.735-0.958) (p = 0.024 and 0.008, respectively). The decision curve analysis showed that the clinical radiomics model had the highest net benefit in most threshold probability ranges. CONCLUSION: ABVS and B-ultrasound-based radiomics nomograms have satisfactory performance in differentiating benign and malignant 4A lesions. This can help clinicians make an accurate diagnosis of 4A lesions and reduce unnecessary biopsy.

A Clinical Radiomics Nomogram Was Developed by Integrating Radiomics Signatures and Clinical Variables to Distinguish High-Grade ccRCC from Type 2 pRCC.

Purpose: A nomogram was constructed by combining clinical factors and a CT-based radiomics signature to discriminate between high-grade clear cell renal cell carcinoma (ccRCC) and type 2 papillary renal cell carcinoma (pRCC). Methods: A total of 142 patients with 71 in high-grade ccRCC and seventy-one in type 2 pRCC were enrolled and split into a training cohort (n = 98) and a testing cohort (n = 44). A clinical factor model containing patient demographics and CT imaging characteristics was designed. By extracting the radiomics features from the precontrast phase, corticomedullary phase (CMP), and nephrographic phase (NP) CT images, a radiomics signature was established, and a Rad-score was computed. By combining the Rad-score and significant clinical factors using multivariate logistic regression analysis, a clinical radiomics nomogram was subsequently developed. The diagnostic performance of these three models was evaluated by using data from both the training and testing groups using a receiver operating characteristic (ROC) curve analysis. Results: The radiomics signature contained eight validated features from the CT images. The relative enhancement value of CMP (REV1) was an independent risk factor in the clinical factor model. The area under the curve (AUC) value of the clinical radiomics nomogram was 0.974 and 0.952 in the training and testing cohorts, respectively. In the training cohort, the decision curves of the nomogram demonstrated an added overall net advantage compared to the clinical factor model. Conclusion: A noninvasive prediction tool termed radiomics nomogram, combining clinical criteria and the radiomics signature, may accurately predict high-grade ccRCC and type 2 pRCC before surgery. It also has some importance in assisting clinicians in determining future treatment strategies.

The role of fascin-1 in the pathogenesis, diagnosis and management of respiratory related cancers.

Human cancer statistics report that respiratory related cancers such as lung, laryngeal, oral and nasopharyngeal cancers account for a large proportion of tumors, and tumor metastasis remains the major reason for patient death. The metastasis of tumor cells requires actin cytoskeleton remodeling, in which fascin-1 plays an important role. Fascin-1 can cross-link F-actin microfilaments into bundles and form finger-like cell protrusions. Some studies have shown that fascin-1 is overexpressed in human tumors and is associated with tumor growth, migration and invasion. The role of fascin-1 in respiratory related cancers is not very clear. The main purpose of this study was to provide an updated literature review on the role of fascin-1 in the pathogenesis, diagnosis and management of respiratory related cancers. These studies suggested that fascin-1 can serve as an emerging biomarker and potential therapeutic target, and has attracted widespread attention.

Fabrication of multifunctional metal-organic frameworks nanoparticles via layer-by-layer self-assembly to efficiently discover PSD95-nNOS uncouplers for stroke treatment.

BACKGROUND: Disruption of the postsynaptic density protein-95 (PSD95)-neuronal nitric oxide synthase (nNOS) coupling is an effective way to treat ischemic stroke, however, it still faces some challenges, especially lack of satisfactory PSD95-nNOS uncouplers and the efficient high throughput screening model to discover them. RESULTS: Herein, the multifunctional metal-organic framework (MMOF) nanoparticles as a new screening system were innovatively fabricated via layer-by-layer self-assembly in which His-tagged nNOS was selectively immobilized on the surface of magnetic MOF, and then PSD95 with green fluorescent protein (GFP-PSD95) was specifically bound on it. It was found that MMOF nanoparticles not only exhibited the superior performances including the high loading efficiency, reusability, and anti-interference ability, but also possessed the good fluorescent sensitivity to detect the coupled GFP-PSD95. After MMOF nanoparticles interacted with the uncouplers, they would be rapidly separated from uncoupled GFP-PSD95 by magnet, and the fluorescent intensities could be determined to assay the uncoupling efficiency at high throughput level. CONCLUSIONS: In conclusion, MMOF nanoparticles were successfully fabricated and applied to screen the natural actives as potential PSD95-nNOS uncouplers. Taken together, our newly developed method provided a new material as a platform for efficiently discovering PSD95-nNOS uncouplers for stoke treatment.

Investigating the Impact of the New York State Flavor Ban on e-Cigarette-Related Discussions on Twitter: Observational Study.

BACKGROUND: On May 18, 2020, the New York State Department of Health implemented a statewide flavor ban to prohibit the sales of all flavored vapor products, except for tobacco or any other authorized flavor. OBJECTIVE: This study aims to investigate the discussion changes in e-cigarette-related tweets over time with the implementation of the New York State flavor ban. METHODS: Through the Twitter streaming application programming interface, 59,883 e-cigarette-related tweets were collected within the New York State from February 6, 2020, to May 17, 2020 (period 1, before the implementation of the flavor ban), May 18, 2020-June 30, 2020 (period 2, between the implementation of the flavor ban and the online sales ban), July 1, 2020-September 15, 2020 (period 3, the short term after the online sales ban), and September 16, 2020-November 30, 2020 (period 4, the long term after the online sales ban). Sentiment analysis and topic modeling were conducted to investigate the changes in public attitudes and discussions in e-cigarette-related tweets. The popularity of different e-cigarette flavor categories was compared before and after the implementation of the New York State flavor ban. RESULTS: Our results showed that the proportion of e-cigarette-related tweets with negative sentiment significantly decreased (4305/13,246, 32.5% vs 3855/14,455, 26.67%, P<.001), and tweets with positive sentiment significantly increased (5246/13,246, 39.6% vs 7038/14,455, 48.69%, P<.001) in period 4 compared to period 3. "Teens and nicotine products" was the most frequently discussed e-cigarette-related topic in the negative tweets. In contrast, "nicotine products and quitting" was more prevalent in positive tweets. The proportion of tweets mentioning mint and menthol flavors significantly increased right after the flavor ban and decreased to lower levels over time. The proportions of fruit and sweet flavors were most frequently mentioned in period 1, decreased in period 2, and dominated again in period 4. CONCLUSIONS: The proportion of e-cigarette-related tweets with different attitudes and frequently discussed flavor categories changed over time after the implementation of the New York State ban of flavored vaping products. This change indicated a potential impact of the flavor ban on public discussions of flavored e-cigarettes.

A novel clinical radiomics nomogram at baseline to predict mucosal healing in Crohn's disease patients treated with infliximab.

OBJECTIVES: Mucosal healing (MH) is currently the gold standard in Crohn's disease (CD) management. Noninvasive assessment of MH in CD patients is increasingly a concern of clinicians. METHODS: This retrospective study included 106 patients with confirmed CD who were divided into a training cohort (n = 73) and a testing cohort (n = 33). Patient demographics were evaluated to establish a clinical model. Radiomics features were extracted from computed tomography enterography (CTE) images. A radiomics signature was constructed, and a radiomics score (Rad-score) was calculated by using the radiomics signature-based formula. A clinical radiomics nomogram was then built by incorporating the Rad-score and significant clinical features. The diagnostic performance of the three models was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: Of the 106 patients with CD, 37 exhibited MH after 26 weeks of infliximab (IFX) treatment. The area under the ROC curve (AUC) of the clinical radiomics nomogram for distinguishing MH from non-MH, which was based on the disease duration and Rad-score, was 0.880 (95% confidence interval [CI]: 0.809-0.943) in the training cohort and 0.877 (95% CI: 0.745-0.983) in the testing cohort. Decision curve analysis (DCA) confirmed the clinical utility of the clinical radiomics nomogram. CONCLUSIONS: This is a preliminary study suggesting that this CTE-based radiomics model has potential value for predicting MH in CD patients. A nomogram constructed by combining radiomics signatures and clinical features can help clinicians select appropriate therapeutic strategies for CD patients. KEY POINTS: • The disease duration (odds ratio (OR) = 0.969, 95% confidence interval (CI) = 0.943-0.995, p = 0.021) was an independent predictor of MH in the clinical model. • The AUC of the radiomics model constructed by the five radiomics features was 0.846 (95% CI: 0.759-0.921) in the training cohort and 0.817 (95% CI: 0.665-0.945) in the testing cohort for differentiating MH from non-MH. • The AUC of the clinical radiomics nomogram was 0.880 (95% CI: 0.809-0.943) in the training cohort and 0.877 (95% CI: 0.745-0.983) in the testing cohort for distinguishing MH from non-MH.

Nomograms of Combining MRI Multisequences Radiomics and Clinical Factors for Differentiating High-Grade From Low-Grade Serous Ovarian Carcinoma.

Objective: To compare the performance of clinical factors, FS-T2WI, DWI, T1WI+C based radiomics and a combined clinic-radiomics model in predicting the type of serous ovarian carcinomas (SOCs). Methods: In this retrospective analysis, 138 SOC patients were confirmed by histology. Significant clinical factors (P < 0.05, and with the area under the curve (AUC) > 0.7) was retained to establish a clinical model. The radiomics model included FS-T2WI, DWI, and T1WI+C, and also, a multisequence model was established. A total of 1,316 radiomics features of each sequence were extracted; the univariate and multivariate logistic regressions, cross-validations were performed to reduce valueless features and then radiomics signatures were developed. Nomogram models using clinical factors, combined with radiomics features, were developed in the training cohort. The predictive performance was validated by receiver operating characteristic curve (ROC) analysis and decision curve analysis (DCA). A stratified analysis was conducted to compare the differences between the combined radiomics model and the clinical model in identifying low- and high-grade SOC. Results: The AUC of the clinical model and multisequence radiomics model in the training and validation cohorts was 0.90 and 0.89, 0.91 and 0.86, respectively. By incorporating clinical factors and multi-radiomics signature, the AUC of the radiomic-clinical nomogram in the training and validation cohorts was 0.98 and 0.95. The model comparison results show that the AUC of the combined model is higher than that of the uncombined models (P= 0.05, 0.002). Conclusion: The nomogram models of clinical factors combined with MRI multisequence radiomics signatures can help identifying low- and high-grade SOCs and a provide a more comprehensive, effective method to evaluate preoperative risk stratification for SOCs.

Differential Diagnosis of Type 1 and Type 2 Papillary Renal Cell Carcinoma Based on Enhanced CT Radiomics Nomogram.

Objectives: To construct a contrast-enhanced CT-based radiomics nomogram that combines clinical factors and a radiomics signature to distinguish papillary renal cell carcinoma (pRCC) type 1 from pRCC type 2 tumours. Methods: A total of 131 patients with 60 in pRCC type 1 and 71 in pRCC type 2 were enrolled and divided into training set (n=91) and testing set (n=40). Patient demographics and enhanced CT imaging characteristics were evaluated to set up a clinical factors model. A radiomics signature was constructed and radiomics score (Rad-score) was calculated by extracting radiomics features from contrast-enhanced CT images in corticomedullary phase (CMP) and nephrographic phase (NP). A radiomics nomogram was then built by incorporating the Rad-score and significant clinical factors according to multivariate logistic regression analysis. The diagnostic performance of the clinical factors model, radiomics signature and radiomics nomogram was evaluated on both the training and testing sets. Results: Three validated features were extracted from the CT images and used to construct the radiomics signature. Boundary blurring as an independent risk factor for tumours was used to build clinical factors model. The AUC value of the radiomics nomogram, which was based on the selected clinical factors and Rad-score, were 0.855 and 0.831 in the training and testing sets, respectively. The decision curves of the radiomics nomogram and radiomics signature in the training set indicated an overall net benefit over the clinical factors model. Conclusion: Radiomics nomogram combining clinical factors and radiomics signature is a non-invasive prediction method with a good prediction for pRCC type 1 tumours and type 2 tumours preoperatively and has some significance in guiding clinicians selecting subsequent treatment plans.