Machine learning to predict lymph node metastasis in T1 esophageal squamous cell carcinoma：a multicenter study.

BACKGROUND: Existing models do poorly when it comes to quantifying the risk of Lymph node metastases (LNM). This study aimed to develop a machine learning model for LNM in patients with T1 esophageal squamous cell carcinoma (ESCC). METHODS AND RESULTS: The study is multicenter, and population based. Elastic net regression (ELR), random forest (RF), extreme gradient boosting (XGB), and a combined (ensemble) model of these was generated. The contribution to the model of each factor was calculated. The models all exhibited potent discriminating power. The Elastic net regression performed best with externally validated AUC of 0.803, whereas the NCCN guidelines identified patients with LNM with an AUC of 0.576 and logistic model with an AUC of 0. 670. The most important features were lymphatic and vascular invasion and depth of tumor invasion. CONCLUSIONS: Models created utilizing machine learning approaches had excellent performance estimating the likelihood of LNM in T1 ESCC.

Enhanced quantum secret sharing protocol for anonymous secure communication utilizing W states.

Quantum secret sharing (QSS) represents the fusion of quantum mechanics principles with secret information sharing, allowing a sender to distribute a secret among receivers for collective recovery. This paper introduces the concept of quantum anonymous secret sharing (QASS) to enhance the practicality of such protocols. We propose a QASS protocol leveraging W states, ensuring both recover-security and anonymity of shared secrets. Our protocol undergoes rigorous evaluation verifying their accuracy and fortifying their security against scenarios involving the active adversary. Additionally, acknowledging the imperfections inherent in real-world communication channels, we conduct a comprehensive analysis of protocol security and efficacy in noisy quantum networks. Our investigations reveal that W states exhibit good performance in mitigating noise interference, making them apt for practical applications.

Enhanced phase sensitivity in a Mach-Zehnder interferometer via photon recycling.

We propose an alternative scheme for phase estimation in a Mach-Zehnder interferometer (MZI) with photon recycling. It is demonstrated that with the same coherent-state input and homodyne detection, our proposal possesses a phase sensitivity beyond the traditional MZI. For instance, it can achieve an enhancement factor of ∼9.32 in the phase sensitivity compared with the conventional scheme even with a photon loss of 10% on the photon-recycled arm. From another point of view, the quantum Cramér-Rao bound (QCRB) is also investigated. It is found that our scheme is able to achieve a lower QCRB than the traditional one. Intriguingly, the QCRB of our scheme is dependent of the phase shift ϕ while the traditional scheme has a constant QCRB regardless of the phase shift. Finally, we present the underlying mechanisms behind the enhanced phase sensitivity. We believe that our results provide another angle from which to enhance the phase sensitivity in a MZI via photon recycling.

Correlation between Mean Platelet Volume and Gensini Score in Patients with Coronary Heart Disease in Different Diabetic States.

SUBJECT: To investigate the correlation between mean platelet volume (MPV) levels and Gensini scores in stable coronary heart disease (CHD) patients with or without diabetes. METHODS: A retrospective analysis was conducted on 2525 patients with stable CHD in Zhongshan Hospital, Fudan University. There were 1274 in the low MPV group and 1251 in the high MPV group, divided by a median MPV level of 10.9 fL. In the total population, 1605 patients were non-diabetic and 920 were diabetic. The severity of coronary artery disease was quantified using the Gensini score. RESULTS: The Gensini score was significantly higher in the high MPV group than in the low MPV group (p < 0.001). MPV levels increased significantly with the number of stenotic (>50%) coronary vessels (p < 0.001). The Spearman analysis showed a positive correlation between MPV and Gensini score (r = 0.189, p < 0.001), which was more significant in the diabetic subgroup (r = 0.232, p < 0.001). Receiver operating characteristic (ROC) curves were employed to assess the predictive value of MPV for high Gensini scores, using the median value of 32 points as the cutoff. MPV levels in the diabetes cohort exhibited a higher predictive value for high Gensini scores (area under the curve: 0.635 [0.614-0.657], p < 0.001). Multivariate linear regression analysis showed that diabetes and MPV were independently associated with Gensini scores. CONCLUSION: MPV levels in stable CHD patients can predict the severity of coronary artery stenosis. This correlation is more significant in the presence of diabetes.

Sensitivity and specificity of magnetic resonance imaging in routine diagnosis of pulmonary lesions: a comparison with computed tomography.

Background: State-of-the-art thoracic magnetic resonance imaging (MRI) plays a complementary role in the assessment of pulmonary nodules/masses which potentially indicate to cancer. We aimed to evaluate the sensitivity and specificity of MRI in diagnosis of pulmonary nodules/masses. Methods: Sixty-eight patients with computed tomography (CT)-detected pulmonary nodules/masses underwent 3T MRI (T1-VIBE, T1-starVIBE, T2-fBLADE turbo spin-echo, and T2-SPACE). The detection rate was calculated for each of the different subgroups of pulmonary nodules according to lung imaging reporting and data system (Lung-RADS). The four MRI sequences were compared in terms of detection rate and image quality-signal to noise ratio (SNR), contrast to noise ratio (CNR) and 5-point scoring scale. Agreement of lesion size measurement between CT and MRI was assessed by intraclass correlation coefficient (ICC). The picture-SNR, lesion-SNR and CNR of each sequence were analyzed by Mann-Whitney U test. Results: In total, 232 pulmonary lesions were detected by CT. The CT showed 86 solid nodules (SNs) <6 mm, 15 SNs between 6-8 mm, 35 SNs between 8-15 mm, and 52 SNs between 15-30 mm. The T1-VIBE, T1-starVIBE, T2-fBLADE TSE and T2-SPACE sequences accurately detected 141 SNs (141/188, 75%/83.3%), 150 SNs (150/188, 79.8%/100%), 166 SNs (166/188, 88.3%/66.7%) and 169 SNs (169/188, 89.9%/53.3%), respectively. Four ground glass nodules (GGNs) (4/6) were detected by T2-fBLADE TSE. Twelve part-solid nodules (PSNs) (12/22) were detected by T1-VIBE and 20 PSNs (20/22) by T2-SPACE. A total of 100 lesions (2.2±1.4 cm, 0.8-7.3 cm) were accurately detected and measured by the four MRI sequences with ICC >0.96. The picture-SNR, lesion-SNR and CNR by T1-starVIBE were higher than those by T1-VIBE (P<0.001). The lesion-SNR and CNR by T2-fBLADE TSE were higher than those by T2-SPACE (P=0.006, 0.038). 86% of images by T1-starVIBE, 92% by T2-fBLADE TSE, 90% by T2-SPACE and 93% by T1-VIBE were scored 3 or more. Conclusions: MRI achieves high sensitivity and specificity for different type of pulmonary nodules detection and is an effective alternative to CT as a diagnostic tool for pulmonary nodules.

Establishment and validation of a radiological-radiomics model for predicting high-grade patterns of lung adenocarcinoma less than or equal to 3 cm.

Objective: We aimed to develop a Radiological-Radiomics (R-R) based model for predicting the high-grade pattern (HGP) of lung adenocarcinoma and evaluate its predictive performance. Methods: The clinical, pathological, and imaging data of 374 patients pathologically confirmed with lung adenocarcinoma (374 lesions in total) were retrospectively analyzed. The 374 lesions were assigned to HGP (n = 81) and non-high-grade pattern (n-HGP, n = 293) groups depending on the presence or absence of high-grade components in pathological findings. The least absolute shrinkage and selection operator (LASSO) method was utilized to screen features on the United Imaging artificial intelligence scientific research platform, and logistic regression models for predicting HGP were constructed, namely, Radiological model, Radiomics model, and R-R model. Also, receiver operating curve (ROC) curves were plotted on the platform, generating corresponding area under the curve (AUC), sensitivity, specificity, and accuracy. Using the platform, nomograms for R-R models were also provided, and calibration curves and decision curves were drawn to evaluate the performance and clinical utility of the model. The statistical differences in the performance of the models were compared by the DeLong test. Results: The R-R model for HGP prediction achieved an AUC value of 0.923 (95% CI: 0.891-0.948), a sensitivity of 87.0%, a specificity of 83.4%, and an accuracy of 84.2% in the training set. In the validation set, this model exhibited an AUC value of 0.920 (95% CI: 0.887-0.945), a sensitivity of 87.5%, a specificity of 83.3%, and an accuracy of 84.2%. The DeLong test demonstrated optimal performance of the R-R model among the three models, and decision curves validated the clinical utility of the R-R model. Conclusion: In this study, we developed a fusion model using radiomic features combined with radiological features to predict the high-grade pattern of lung adenocarcinoma, and this model shows excellent diagnostic performance. The R-R model can provide certain guidance for clinical diagnosis and surgical treatment plans, contributing to improving the prognosis of patients.

Applying a CT texture analysis model trained with deep-learning reconstruction images to iterative reconstruction images in pulmonary nodule diagnosis.

OBJECTIVE: To investigate the feasibility and accuracy of applying a computed tomography (CT) texture analysis model trained with deep-learning reconstruction images to iterative reconstruction images for classifying pulmonary nodules. MATERIALS AND METHODS: CT images of 102 patients, with a total of 118 pulmonary nodules (52 benign, 66 malignant) were retrospectively reconstructed with a deep-learning reconstruction (artificial intelligence iterative reconstruction [AIIR]) and a hybrid iterative reconstruction (HIR) technique. The AIIR data were divided into a training (n = 96) and a validation set (n = 22), and the HIR data were set as the test set (n = 118). Extracted texture features were compared using the Mann-Whitney U test and t-test. The diagnostic performance of the classification model was analyzed with the receiver operating characteristic curve (ROC), the area under ROC (AUC), sensitivity, specificity, and accuracy. RESULTS: Among the obtained 68 texture features, 51 (75.0%) were not influenced by the change of reconstruction algorithm (p > 0.05). Forty-four features were significantly different between benign and malignant nodules (p < 0.05) for the AIIR dataset, which were selected to build the classification model. The accuracy and AUC of the classification model were 92.3% and 0.91 (95% confidence interval [CI], 0.74-0.90) with the validation set, which were 80.0% and 0.80 (95% CI, 0.68-0.86) with the test set. CONCLUSION: With the CT texture analysis model trained with deep-learning reconstruction (AIIR) images showing favorable diagnostic accuracy in discriminating benign and malignant pulmonary nodules, it also has certain applicability to the iterative reconstruction (HIR) images.

Radiomics nomogram analysis of T2-fBLADE-TSE in pulmonary nodules evaluation.

OBJECTIVES: To develop and validate a radiomics nomogram for differentiating between malignant pulmonary nodules and benign nodules. METHODS: 56 benign and 51 malignant nodules from 96 patients were analyzed using manual segmentation of the T2-fBLADE-TSE, while the nodules signal intensity (SIlesion), lesion muscle ratio (LMR) and nodule size were all measured and recorded. The maximum relevance and minimum redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) were used to select nonzero coefficients and develop the model in pulmonary nodules diagnosis. The radiomics nomogram was also developed. The clinical prediction value was determined by the decision curve analysis (DCA). RESULTS: The nodule size, SIlesion and LMR of the benign group were 1.78 ± 0.57 cm, 227.50 ± 81.39 and 2.40 ± 1.27 respectively, in contrast to the 2.00 ± 0.64 cm, 232.87 ± 82.21 and 2.17 ± 0.91, respectively, in the malignant group (P = 0.09, 0.60 and 0.579). A total of 13 radiomics features were retained. The Rad-score of the benign nodules group was lower than that of the malignant nodules group (P < 0.001 & 0.049, training & test set). The AUC of radiomics signature for nodules diagnosis was 0.82 (95% CI, 0.73-0.91) in the training set and 0.71 (95% CI, 0.51-0.90) in the test set. A nomogram, consisting of 13 radiomics features and nodule size, produced good prediction in the training set (AUC, 0.82; 95% CI, 0.73-0.91), which was significantly better than that of T2-based quantitative parameters (AUC, 0.62; 95% CI, 0.50-0.75, P = 0.003). In the test set, the performance of radiomics nomogram (AUC, 0.70; 95% CI, 0.51-0.90) was also better than that of T2-based quantitative parameters (AUC, 0.46; 95% CI, 0.25-0.67) (P = 0.145). The DCA showed that radiomics nomogram and T2-based quantitative parameter had overall net benefits, while the performance of nomogram was better. CONCLUSION: We constructed and validated a T2-fBLADE-TSE-based radiomics nomogram that can help to differentiate between malignant pulmonary nodules and benign nodules.

Intra-observer and inter-observer agreements for the measurement of dual-input whole tumor computed tomography perfusion in patients with lung cancer: Influences of the size and inner-air density of tumors.

BACKGROUND: This study was conducted to assess intra-observer and inter-observer agreements for the measurement of dual-input whole tumor computed tomography perfusion (DCTP) in patients with lung cancer. METHODS: A total of 88 patients who had undergone DCTP, which had proved a diagnosis of primary lung cancer, were divided into two groups: (i) nodules (diameter ≤3 cm) and masses (diameter >3 cm) by size, and (ii) tumors with and without air density. Pulmonary flow, bronchial flow, and pulmonary index were measured in each group. Intra-observer and inter-observer agreements for measurement were assessed using intraclass correlation coefficient, within-subject coefficient of variation, and Bland-Altman analysis. RESULTS: In all lung cancers, the reproducibility coefficient for intra-observer agreement (range 26.1-38.3%) was superior to inter-observer agreement (range 38.1-81.2%). Further analysis revealed lower agreements for nodules compared to masses. Additionally, inner-air density reduced both agreements for lung cancer. CONCLUSION: The intra-observer agreement for measuring lung cancer DCTP was satisfied, while the inter-observer agreement was limited. The effects of tumoral size and inner-air density to agreements, especially between two observers, should be emphasized. In future, an automatic computer-aided segment of perfusion value of the tumor should be developed.

Bedside chest radiography of novel influenza A (H7N9) virus infections and follow-up findings after short-time treatment.

BACKGROUND: Influenza A (H7N9) virus infections were first observed in China in March 2013. This type virus can cause severe illness and deaths, the situation raises many urgent questions and global public health concerns. Our purpose was to investigate bedside chest radiography findings for patients with novel influenza A (H7N9) virus infections and the followup appearances after short-time treatment. METHODS: Eight hospitalized patients infected with the novel influenza A (H7N9) virus were included in our study. All of the patients underwent bedside chest radiography after admission, and all had follow-up bedside chest radiography during their first ten days, using AXIOM Aristos MX and/or AMX-IV portable X-ray units. The exposure dose was generally 90 kV and 5 mAs, and was slightly adjusted according to the weight of the patients. The initial radiography data were evaluated for radiological patterns (ground glass opacity, consolidation, and reticulation), distribution type (focal, multifocal, and diffuse), lung zones involved, and appearance at follow-up while the patients underwent therapy. RESULTS: All patients presented with bilateral multiple lung involvement. Two patients had bilateral diffuse lesions, three patients had unilateral diffuse lesions of the right lobe with multifocal lesions of the left lobe, and the remaining three had bilateral multifocal lung lesions. The lesions were present throughout bilateral lung zones in three patients, the whole right lung zone in three patients with additional involvement in the left middle and/or lower lung zone(s), both lower and middle lung zones in one patient, and the right middle and lower in combination with the left lower lung zones in one patient. The most common abnormal radiographic patterns were ground glass opacity (8/8), and consolidation (8/8). In three cases examined by CT we also found the pattern of reticulation in combination with CT images. Four patients had bilateral and four had unilateral pleural effusion. After a short period of treatment the pneumonia in one patient had significantly improved and three cases demonstrated disease progression. In four cases the severity of the pneumonia fluctuated. CONCLUSIONS: In patients with influenza A (H7N9) virus infection, the distribution of the lung lesions are extensive, and the disease usually involves both lung zones. The most common imaging findings are a mixture of ground glass opacity and consolidation. Pleural effusion is common. Most cases have a poor short-time treatment response, and seem to have either rapid progressive radiographic deterioration or fluctuating radiographic changes. Chest radiography is helpful for evaluating patients with severe clinical symptoms and for follow-up evaluation.

Emerging H7N9 influenza A (novel reassortant avian-origin) pneumonia: radiologic findings.

PURPOSE: To determine the radiologic findings of human infection with a novel reassortant avian-origin influenza A H7N9 virus in March 2013, the first outbreak in humans. MATERIALS AND METHODS: The institutional review board approved this retrospective study. Twelve patients (nine men and three women) with novel avian-origin influenza A H7N9 virus infection were enrolled. All patients underwent chest radiography and thin-section computed tomography (CT). Lesion patterns, distributions, and changes at follow-up CT were investigated. Two chest radiologists reviewed the images and clinical data together and reached decisions concerning findings by consensus. RESULTS: At presentation, all patients had progressing infection of the lower respiratory tract, with fever, cough, and shortness of breath, which rapidly progressed to acute respiratory distress syndrome. The imaging findings included ground-glass opacities (GGOs) (in 12 of 12 patients), consolidations (in 11 patients), air bronchograms (in 11 patients), interlobular septal thickening (in 11 patients), centrilobular nodules (in seven patients), reticulations (in seven patients), cystic changes (in four patients), bronchial dilatation (in three patients), and subpleural linear opacities (in three patients). The lung lesions involved three or more lobes in all cases and were mostly detected in the right lower lobe (in 11 patients). Follow-up CT in 10 patients showed interval improvement (in three patients) or worsening (in seven patients) of the lesions. Imaging findings closely mirrored the overall clinical severity of the disease. CONCLUSION: Rapidly progressive GGOs and consolidations with air bronchograms and interlobular septal thickening, with right lower lobe predominance, are the main imaging findings in H7N9 pneumonia. The severity of these findings is associated with the severity of the clinical presentation.

Clinical findings for early human cases of influenza A(H7N9) virus infection, Shanghai, China.

A novel strain of influenza A(H7N9) virus has emerged in China and is causing mild to severe clinical symptoms in infected humans. Some case-patients have died. To further knowledge of this virus, we report the characteristics and clinical histories of 4 early case-patients.