Establishing subdivisions of M1 stage nasopharyngeal carcinoma based on decision tree classification: A multicenter retrospective study.

OBJECTIVES: To meet the demand for personalized treatment, effective stratification of patients with metastatic nasopharyngeal carcinoma (mNPC) is essential. Hence, our study aimed to establish an M1 subdivision for prognostic prediction and treatment planning in patients with mNPC. MATERIALS AND METHODS: This study included 1239 patients with mNPC from three medical centers divided into the synchronous mNPC cohort (smNPC, n = 556) to establish an M1 stage subdivision and the metachronous mNPC cohort (mmNPC, n = 683) to validate this subdivision. The primary endpoint was overall survival. Univariate and multivariate Cox analyses identified covariates for the decision-tree model, proposing an M1 subdivision. Model performance was evaluated using time-dependent receiver operating characteristic curves, Harrell's concordance index, calibration plots, and decision curve analyses. RESULTS: The proposed M1 subdivisions were M1a (≤5 metastatic lesions), M1b (>5 metastatic lesions + absent liver metastases), and M1c (>5 metastatic lesions + existing liver metastases) with median OS of 34, 22, and 13 months, respectively (p < 0.001). This M1 subdivision demonstrated superior discrimination (C-index = 0.698; 3-year AUC = 0.707) and clinical utility over those of existing staging systems. Calibration curves exhibited satisfactory agreement between predictions and actual observations. Internal and mmNPC cohort validation confirmed the robustness. Survival benefits from local metastatic treatment were observed in M1a, while immunotherapy improved survival in patients with M1b and M1c disease. CONCLUSION: This novel M1 staging strategy provides a refined approach for prognostic prediction and treatment planning in patients with mNPC, emphasizing the potential benefits of local and immunotherapeutic interventions based on individualized risk stratification.

Quantification of Intratumoral Heterogeneity: Distinguishing Histological Subtypes in Clinical T1 Stage Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodules on Computed Tomography.

RATIONALE AND OBJECTIVES: To quantify intratumor heterogeneity (ITH) in clinical T1 stage lung adenocarcinoma presenting as pure ground-glass nodules (pGGN) on computed tomography, assessing its value in distinguishing histological subtypes. MATERIALS AND METHODS: An ITH score was developed for quantitative measurement by integrating local radiomics features and global pixel distribution patterns. Diagnostic efficacy in distinguishing histological subtypes was evaluated using receiver operating characteristic curve analysis and area under the curve (AUC) values. The ITH score's performance was compared to those of conventional radiomics (C-radiomics), and radiological assessments conducted by experienced radiologists. RESULTS: The ITH score demonstrated excellent performance in distinguishing lepidic-predominant adenocarcinoma (LPA) from other histological subtypes of clinical T1 stage lung adenocarcinoma presenting as pGGN. It outperformed both C-radiomics and radiological findings, exhibiting higher AUCs of 0.784 (95% confidence interval [CI]: 0.742-0.826) and 0.801 (95% CI: 0.739-0.863) in the training and validation cohorts, respectively. The AUCs of C-radiomics were 0.764 (95% CI: 0.718-0.810, DeLong test, p = 0.025) and 0.760 (95% CI: 0.692-0.829, p = 0.023) and those of radiological findings were 0.722 (95% CI: 0.673-0.771, p = 0.003) and 0.754 (95% CI: 0.684-0.823, p = 0.016) in the training and validation cohorts, respectively. Subgroup analysis revealed varying diagnostic efficacy across clinical T1 stages, with the highest efficacy in the T1a stage, followed by the T1b stage, and lowest in the T1c stage. CONCLUSION: The ITH score presents a superior method for evaluating histological subtypes and distinguishing LPA from other subtypes in clinical T1 stage lung adenocarcinoma presenting as pGGN.

Efficacy of metastatic lesion radiotherapy in patients with metastatic nasopharyngeal carcinoma: A multicenter retrospective study.

OBJECTIVE: We investigated the efficacy of metastatic lesion radiotherapy (MLRT) in patients with metastatic nasopharyngeal carcinoma (mNPC). MATERIALS AND METHODS: Patients with mNPC from three institutions were included in this study. Propensity score matching (PSM) was employed to ensure comparability between patient groups. Overall survival (OS) rates were assessed using the Kaplan-Meier method and compared using the log-rank test. Prognostic factors were identified using univariate and multivariate Cox hazard analyses. Subgroup analyses were conducted to assess the effects of MLRT on specific patient populations. RESULTS: We analyzed data from 1157 patients with mNPC. Patients who received MLRT had significantly better OS than those who did not, both in the original (28 vs. 21 months) and PSM cohorts (26 vs. 23 months). MLRT was identified as an independent favorable predictor of OS in multivariate analyses, with hazard ratios of 0.67. The subgroup analysis results indicated that radiotherapy effectively treated liver, lung, and bone metastatic lesions, particularly in patients with a limited tumor burden. Higher total radiation doses of MLRT (biologically effective dose (BED) ≥ 56 Gy) were associated with improved OS, while neither radiation technique nor dose fractionation independently influenced prognosis. CONCLUSIONS: MLRT offers survival advantages to patients diagnosed with mNPC. Patients with limited metastatic burden derive the most benefit from MLRT, and the recommended regimen for MLRT is a minimum BED of 56 Gy for optimal outcomes.

Leveraging multimodal MRI-based radiomics analysis with diverse machine learning models to evaluate lymphovascular invasion in clinically node-negative breast cancer.

Objective: This study aimed to investigate and validate the effectiveness of diverse radiomics models for preoperatively differentiating lymphovascular invasion (LVI) in clinically node-negative breast cancer (BC). Methods: This study included 198 patients diagnosed with clinically node-negative bc and pathologically confirmed LVI status from January 2018-July 2023. The training dataset consisted of 138 patients, while the validation dataset included 60. Radiomics features were extracted from multimodal magnetic resonance imaging obtained from T1WI, T2WI, DCE, DWI, and ADC sequences. Dimensionality reduction and feature selection techniques were applied to the extracted features. Subsequently, machine learning approaches, including logistic regression, support vector machine, classification and regression trees, k-nearest neighbors, and gradient boosting machine models (GBM), were constructed using the radiomics features. The best-performing radiomic model was selected based on its performance using the confusion matrix. Univariate and multivariable logistic regression analyses were conducted to identify variables for developing a clinical-radiological (Clin-Rad) model. Finally, a combined model incorporating both radiomics and clinical-radiological model features was created. Results: A total of 6195 radiomic features were extracted from multimodal magnetic resonance imaging. After applying dimensionality reduction and feature selection, seven valuable radiomics features were identified. Among the radiomics models, the GBM model demonstrated superior predictive efficiency and robustness, achieving area under the curve values (AUC) of 0.881 (0.823,0.940) and 0.820 (0.693,0.947) in the training and validation datasets, respectively. The Clin-Rad model was developed based on the peritumoral edema and DWI rim sign. In the training dataset, it achieved an AUC of 0.767 (0.681, 0.854), while in the validation dataset, it achieved an AUC of 0.734 (0.555-0.913). The combined model, which incorporated radiomics and the Clin-Rad model, showed the highest discriminatory capability. In the training dataset, it had an AUC value of 0.936 (0.892, 0.981), and in the validation dataset, it had an AUC value of 0.876 (0.757, 0.995). Additionally, decision curve analysis of the combined model revealed its optimal clinical efficacy. Conclusion: The combined model, integrating radiomics and clinical-radiological features, exhibited excellent performance in distinguishing LVI status. This non-invasive and efficient approach holds promise for aiding clinical decision-making in the context of clinically node-negative BC.

Assessment of Lymphovascular Invasion in Breast Cancer Using a Combined MRI Morphological Features, Radiomics, and Deep Learning Approach Based on Dynamic Contrast-Enhanced MRI.

BACKGROUND: Assessment of lymphovascular invasion (LVI) in breast cancer (BC) primarily relies on preoperative needle biopsy. There is an urgent need to develop a non-invasive assessment method. PURPOSE: To develop an effective model to assess the LVI status in patients with BC using magnetic resonance imaging morphological features (MRI-MF), Radiomics, and deep learning (DL) approaches based on dynamic contrast-enhanced MRI (DCE-MRI). STUDY TYPE: Cross-sectional retrospective cohort study. POPULATION: The study included 206 BC patients, with 136 in the training set [97 LVI(-) and 39 LVI(+) cases; median age: 51.5 years] and 70 in the test set [52 LVI(-) and 18 LVI(+) cases; median age: 48 years]. FIELD STRENGTH/SEQUENCE: 1.5 T/T1-weighted images, fat-suppressed T2-weighted images, diffusion-weighted imaging (DWI), and DCE-MRI. ASSESSMENT: The MRI-MF model was developed with conventional MR features using logistic analyses. The Radiomic feature extraction process involved collecting data from categorized DCE-MRI datasets, specifically the first and second post-contrast images (A1 and A2). Next, a DL model was implemented to determine LVI. Finally, we established a joint diagnosis model by combining the MRI-MF, Radiomics, and DL approaches. STATISTICAL TESTS: Diagnostic performance was compared using receiver operating characteristic curve analysis, confusion matrix, and decision curve analysis. RESULTS: Rim sign and peritumoral edema features were used to develop the MRI-MF model, while six Radiomics signature from the A1 and A2 images were used for the Radiomics model. The joint model (MRI-MF + Radiomics + DL models) achieved the highest accuracy (area under the curve [AUC] = 0.857), being significantly superior to the MRI-MF (AUC = 0.724), Radiomics (AUC = 0.736), or DL (AUC = 0.740) model. Furthermore, it also outperformed the pairwise combination models: Radiomics + MRI-MF (AUC = 0.796), DL + MRI-MF (AUC = 0.796), or DL + Radiomics (AUC = 0.826). DATA CONCLUSION: The joint model incorporating MRI-MF, Radiomics, and DL approaches can effectively determine the LVI status in patients with BC before surgery. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

Adaptive anisotropic pixel-by-pixel correction method for a space-variant degraded image.

Large field-of-view optical imaging systems often face challenges in the presence of space-variant degradation. The existence of degradation leads to target detection and recognition being difficult or even unsuccessful. To address this issue, this paper proposes an adaptive anisotropic pixel-by-pixel space-variant correction method. First, we estimated region acquisition of local space-variant point spread functions (PSFs) based on Haar wavelet degradation degree distribution, and obtained initial PSF matrix estimation with inverse distance weighted spatial interpolation. Then, we established a pixel-by-pixel space-variant correction model based on the PSF matrix. Third, we imposed adaptive sparse regularization terms of the Haar wavelet based on the adaptive anisotropic iterative reweight strategy and non-negative regularization terms as the constraint in the pixel-by-pixel space-variant correction model. Finally, as the correction process is refined to each pixel, the split-Bregman multivariate separation solution algorithm was employed for the pixel-by-pixel spare-variant correction model to estimate the final PSF matrix and the gray value of each pixel. Through this algorithm, the "whole image correction" and "block correction" is avoided, the "pixel-by-pixel correction" is realized, and the final corrected images are obtained. Experimental results show that compared with the current advanced correction methods, the proposed approach in the space-variant wide field correction of a degraded image shows better performance in preserving the image details and texture information.

Breast cancer pre-clinical screening using infrared thermography and artificial intelligence: a prospective, multicentre, diagnostic accuracy cohort study.

BACKGROUND: Given the limited access to breast cancer (BC) screening, the authors developed and validated a mobile phone-artificial intelligence-based infrared thermography (AI-IRT) system for BC screening. MATERIALS AND METHODS: This large prospective clinical trial assessed the diagnostic performance of the AI-IRT system. The authors constructed two datasets and two models, performed internal and external validation, and compared the diagnostic accuracy of the AI models and clinicians. Dataset A included 2100 patients recruited from 19 medical centres in nine regions of China. Dataset B was used for independent external validation and included 102 patients recruited from Langfang People's Hospital. RESULTS: The area under the receiver operating characteristic curve of the binary model for identifying low-risk and intermediate/high-risk patients was 0.9487 (95% CI: 0.9231-0.9744) internally and 0.9120 (95% CI: 0.8460-0.9790) externally. The accuracy of the binary model was higher than that of human readers (0.8627 vs. 0.8088, respectively). In addition, the binary model was better than the multinomial model and used different diagnostic thresholds based on BC risk to achieve specific goals. CONCLUSIONS: The accuracy of AI-IRT was high across populations with different demographic characteristics and less reliant on manual interpretations, demonstrating that this model can improve pre-clinical screening and increase screening rates.

Leptomeningeal enhancement of myelin oligodendrocyte glycoprotein antibody-associated encephalitis: uncovering novel markers on contrast-enhanced fluid-attenuated inversion recovery images.

Background: Myelin oligodendrocyte glycoprotein antibody disease (MOGAD) is a newly defined autoimmune inflammatory demyelinating central nervous system (CNS) disease characterized by antibodies against MOG. Leptomeningeal enhancement (LME) on contrast-enhanced fluid-attenuated inversion recovery (CE-FLAIR) images has been reported in patients with other diseases and interpreted as a biomarker of inflammation. This study retrospectively analyzed the prevalence and distribution of LME on CE-FLAIR images in children with MOG antibody-associated encephalitis (MOG-E). The corresponding magnetic resonance imaging (MRI) features and clinical manifestations are also presented. Methods: The brain MRI images (native and CE-FLAIR) and clinical manifestations of 78 children with MOG-E between January 2018 and December 2021 were analyzed. Secondary analyses evaluated the relationship between LME, clinical manifestations, and other MRI measures. Results: Forty-four children were included, and the median age at the first onset was 70.5 months. The prodromal symptoms were fever, headache, emesis, and blurred vision, which could be progressively accompanied by convulsions, decreased level of consciousness, and dyskinesia. MOG-E showed multiple and asymmetric lesions in the brain by MRI, with varying sizes and blurred edges. These lesions were hyperintense on the T2-weighted and FLAIR images and slightly hypointense or hypointense on the T1-weighted images. The most common sites involved were juxtacortical white matter (81.8%) and cortical gray matter (59.1%). Periventricular/juxtaventricular white matter lesions (18.2%) were relatively rare. On CE-FLAIR images, 24 (54.5%) children showed LME located on the cerebral surface. LME was an early feature of MOG-E (P = 0.002), and cases without LME were more likely to involve the brainstem (P = 0.041). Conclusion: LME on CE-FLAIR images may be a novel early marker among patients with MOG-E. The inclusion of CE-FLAIR images in MRI protocols for children with suspected MOG-E at an early stage may be useful for the diagnosis of this disease.

Deep learning-powered 3D segmentation derives factors associated with lymphovascular invasion and prognosis in clinical T1 stage non-small cell lung cancer.

Background: Lymphovascular invasion (LVI) is an invasive biologic behavior that affects the treatment and prognosis of patients with early-stage lung cancer. This study aimed to identify LVI diagnostic and prognostic biomarkers using deep learning-powered 3D segmentation with artificial intelligence (AI) technology. Methods: Between January 2016 and October 2021, we enrolled patients with clinical T1 stage non-small cell lung cancer (NSCLC). We used commercially available AI software (Dr. Wise system, Deep-wise Corporation, China) to extract quantitative AI features of pulmonary nodules automatically. Dimensionality reduction was achieved through least absolute shrinkage and selection operator regression; subsequently, the AI score was calculated.Then, the univariate and multivariate analysis was further performed on the AI score and patient baseline parameters. Results: Among 175 enrolled patients, 22 tested positive for LVI at pathology review. Based on the multivariate logistic regression results, we incorporated the AI score, carcinoembryonic antigen, spiculation, and pleural indentation into the nomogram for predicting LVI. The nomogram showed good discrimination (C-index = 0.915 [95% confidence interval: 0.89-0.94]); moreover, calibration of the nomogram revealed good predictive ability (Brier score = 0.072). Kaplan-Meier analysis revealed that relapse-free survival and overall survival were significantly higher among patients with a low-risk AI score and without LVI than those among patients with a high-risk AI score (p = 0.008 and p = 0.002, respectively) and with LVI (p = 0.013 and p = 0.008, respectively). Conclusions: Our findings indicate that a high-risk AI score is a diagnostic biomarker for LVI in patients with clinical T1 stage NSCLC; accordingly, it can serve as a prognostic biomarker for these patients.

A novel nomogram for the preoperative prediction of sentinel lymph node metastasis in breast cancer.

BACKGROUND OR PURPOSE: A practical noninvasive method to identify sentinel lymph node (SLN) status in breast cancer patients, who had a suspicious axillary lymph node (ALN) at ultrasound (US), but a negative clinical physical examination is needed. To predict SLN metastasis using a nomogram based on US and biopsy-based pathological features, this retrospective study investigated associations between clinicopathological features and SLN status. METHODS: Patients treated with SLN dissection at four centers were apportioned to training, internal, or external validation sets (n = 472, 175, and 81). Lymph node ultrasound and pathological characteristics were compared using chi-squared and t-tests. A nomogram predicting SLN metastasis was constructed using multivariate logistic regression models. RESULTS: In the training set, statistically significant factors associated with SLN+ were as follows: histology type (p < 0.001); progesterone receptor (PR: p = 0.003); Her-2 status (p = 0.049); and ALN-US shape (p = 0.034), corticomedullary demarcation (CMD: p < 0.001), and blood flow (p = 0.001). With multivariate analysis, five independent variables (histological type, PR status, ALN-US shape, CMD, and blood flow) were integrated into the nomogram (C-statistic 0.714 [95% CI: 0.688-0.740]) and validated internally (0.816 [95% CI: 0.784-0.849]) and externally (0.942 [95% CI: 0.918-0.966]), with good predictive accuracy and clinical applicability. CONCLUSION: This nomogram could be a direct and reliable tool for individual preoperative evaluation of SLN status, and therefore aids decisions concerning ALN dissection and adjuvant treatment.

Measuring pure ground-glass nodules on computed tomography: assessing agreement between a commercially available deep learning algorithm and radiologists' readings.

BACKGROUND: Deep learning algorithms (DLAs) could enable automatic measurements of solid portions of mixed ground-glass nodules (mGGNs) in agreement with the invasive component sizes measured during pathologic examinations. However, the measurement of pure ground-glass nodules (pGGNs) based on DLAs has rarely been reported in the literature. PURPOSE: To evaluate the use of a commercially available DLA for the automatic measurement of pGGNs on computed tomography (CT). MATERIAL AND METHODS: In this retrospective study, we included 68 patients with 81 pGGNs. The maximum diameter of the nodules was manually measured by senior radiologists and automatically segmented and measured by the DLA. Agreement between the measurements by the radiologist and DLA was assessed using Bland-Altman plots, and correlations were analyzed using Pearson correlation. Finally, we evaluated the association between the radiologist and DLA measurements and the invasiveness of lung adenocarcinoma in patients with pGGNs on preoperative CT. RESULTS: The radiologist and DLA measurements exhibited good agreement with a Bland-Altman bias of 3.0%, which were clinically acceptable. The correlation between both sets of maximum diameters was also strong, with a Pearson correlation coefficient of 0.968 (P < 0.001). In addition, both sets of maximum diameters were larger in the invasive adenocarcinoma group than in the non-invasive adenocarcinoma group (P < 0.001). CONCLUSION: Automatic pGGNs measurements by the DLA were comparable with those measured manually and were closely associated with the invasiveness of lung adenocarcinoma.

Development and Validation of a Nomogram for Predicting the 1-, 3-, and 5-year Survival in Patients with Acinar-predominant Lung Adenocarcinoma.

OBJECTIVE: This study aimed to develop a nomogram to predict the overall survival (OS) of patients with acinar-predominant adenocarcinoma (APA). METHODS: Data from patients with APA obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2008 and 2016 were used. Significant prognostic factors were incorporated to construct a nomogram for predicting the 1-, 3-, and 5-year OS in these patients. The discrimination and calibration abilities of the nomogram were assessed using a C-index and calibration curves, respectively. RESULTS: A total of 2242 patients with APA were randomly divided into a training cohort (n=1576) and validation cohort (n=666). The independent prognostic factors for OS incorporated into the nomogram included marital status, age, gender, differentiation grade, T stage, N stage, and M stage. The nomogram showed good prediction capability, as indicated by the C-index [0.713, 95% confidence interval (CI): 0.705-0.721 in the training cohort, and 0.662, 95% CI: 0.649-0.775 in the validation cohort]. The calibration curves demonstrated that the 1-, 3-, and 5-year OS probabilities were consistent between the observed and predicted outcome frequencies. Patients were divided into the high-risk and low-risk groups with the former showing significantly worse survival than the latter (P<0.001). CONCLUSION: Using the SEER database, a nomogram was established to predict the 1-, 3-, and 5-year OS of patients with APA and was superior to the tumor size, lymph node, and metastasis staging system in terms of evaluating long-term prognosis.

Survival benefit after neoadjuvant or adjuvant radiotherapy for stage II-III gastroesophageal junction adenocarcinoma: A large population-based cohort study.

Objective: The standard treatment for stage II-III gastroesophageal junction adenocarcinoma (GEJA) remains controversial, and the role of radiotherapy (RT) in stage II-III GEJA is unclear. Herein, we aimed to evaluate the prognosis of different RT sequences and identify potential candidates to undergo neoadjuvant RT (NART) or adjuvant RT (ART). Materials and methods: In total, we enrolled 3,492 patients with resectable stage II-III GEJA from the Surveillance, Epidemiology, and End Results (SEER) database, subsequently assigned to three categories: T1-2N+, T3-4N-, and T3-4N+. Survival curves were evaluated using the Kaplan-Meier method along with the log-rank test. We compared survival curves for NART, ART, and non-RT in the three categories. To further determine histological types impacting RT-associated survival, we proposed new categories by combining the tumor, node, and metastasis (TNM) stage with Lauren's classification. Results: ART afforded a significant survival benefit in patients with T1-2N+ and T3-4N+ tumors. In addition, NART conferred a survival advantage in patients with T3-4N+ and T3-4 exhibiting the intestinal type. Notably, ART and NART were both valuable in patients with T3-4N+, although no significant differences between treatment regimens were noted. Conclusions: Both NART and ART can prolong the survival of patients with stage II-III GEJA. Nevertheless, the selection of NART or ART requires a concrete analysis based on the patient's condition.

Multiparametric magnetic resonance imaging-derived deep learning network to determine ferroptosis-related gene signatures in gliomas.

Introduction: Ferroptosis-related gene (FRG) signature is important for assessing novel therapeutic approaches and prognosis in glioma. We trained a deep learning network for determining FRG signatures using multiparametric magnetic resonance imaging (MRI). Methods: FRGs of patients with glioma were acquired from public databases. FRG-related risk score stratifying prognosis was developed from The Cancer Genome Atlas (TCGA) and validated using the Chinese Glioma Genome Atlas. Multiparametric MRI-derived glioma images and the corresponding genomic information were obtained for 122 cases from TCGA and The Cancer Imaging Archive. The deep learning network was trained using 3D-Resnet, and threefold cross-validation was performed to evaluate the predictive performance. Results: The FRG-related risk score was associated with poor clinicopathological features and had a high predictive value for glioma prognosis. Based on the FRG-related risk score, patients with glioma were successfully classified into two subgroups (28 and 94 in the high- and low-risk groups, respectively). The deep learning networks TC (enhancing tumor and non-enhancing portion of the tumor core) mask achieved an average cross-validation accuracy of 0.842 and an average AUC of 0.781, while the deep learning networks WT (whole tumor and peritumoral edema) mask achieved an average cross-validation accuracy of 0.825 and an average AUC of 0.781. Discussion: Our findings indicate that FRG signature is a prognostic indicator of glioma. In addition, we developed a deep learning network that has high classification accuracy in automatically determining FRG signatures, which may be an important step toward the clinical translation of novel therapeutic approaches and prognosis of glioma.

Sentinel Lymph Node Positive Rate Predicts Non-Sentinel Lymph Node Metastasis in Breast Cancer.

BACKGROUND: To investigate retrospectively an association between the number of metastatic sentinel lymph nodes (SLNs) per total number of SLNs per patient (i.e., the SLN positive rate, or SLN-PR) and non-SLN metastasis in breast cancer. METHODS: A large population (n = 2250) underwent SLN dissection from January 1, 2014 to January 1, 2020; 627 (27.87%) had at least one positive SLN (SLN+). Among these, 283 underwent axillary lymph node (ALN) dissection, and formed the test group. Four external validation groups comprised 43 patients treated in 2019. SLN mappings were examined using methylene blue and indocyanine green. Lymph node ultrasound, SLN-PR, and pathological characteristics were compared between patients with and without non-SLN metastasis. An SLN-PR cutoff value was calculated using receiver operating characteristic (ROC) curves. Associations between clinicopathological variables and SLN-PR with non-SLN metastasis were analyzed by multivariate logistic regression model. RESULTS: The median age was 47 years (IQR: 42-56 y). The median number of resected SLNs was 4. Patients with positive non-SLNs (126/283, 44.52%) had a median of 2 positive node. SLN-PR > 0.333 was a risk factor for non-SLN positivity (area under the ROC curve, 0.726); and carried significantly higher risk of non-SLN metastasis (P < 0.001). This was validated in the external group. CONCLUSIONS: SLN-PR > 0.333 was associated with greater risk of non-SLN metastasis. This provides a reference to non-SLN metastasis in patients with SLN metastasis, an indication for ALN dissection and choice of adjuvant treatment.

The prognostic value of tumor deposits and the impact on the TNM classification system in esophageal cancer patients.

OBJECTIVE: To explore the prognostic significance of tumor deposits (TDs), isolated tumor foci lacking residual lymph nodes, in esophageal cancer (EC). METHODS: A retrospective review of patients with EC undergoing esophagectomy between 2005 and 2017 was conducted. The prognostic value of TD was evaluated using a Cox regression model. Patients from different sources and periods were split into discovery and validation sets. A propensity score matching model was used in the validation set to reduce the confounding bias. The impact of TD on the TNM classification system was evaluated. RESULTS: The discovery and validation sets included 179 and 2875 patients, respectively. Propensity-matched patients with and without TDs were constructed in the validation set with 132 patients in each group. Overall survival (p < .001 and p = .004, respectively) and disease-free survival (p < .001 and p = .019, respectively) were both decreased in TD positive patients in the discovery set and propensity-matched groups of validation set. Classifying patients with TDs into pN3 stage improved the discriminative power of the current TNM staging system. CONCLUSIONS: TD is an independent prognostic factor for EC. The inclusion of TD in the TNM staging system may upstage appropriate patients to help guide therapy, and future studies are warranted.

Development and validation of a nomogram for predicting survival in patients with surgically resected lung invasive mucinous adenocarcinoma.

BACKGROUND: Lung invasive mucinous adenocarcinoma (LIMA) is a unique and rare subtype of lung adenocarcinoma. We identified prognostic factors and developed a nomogram for predicting overall survival (OS) in LIMA patients after surgery. METHODS: Patients diagnosed with LIMA between 2008 and 2016 from the Surveillance, Epidemiology, and End Results (SEER) database were randomized into training (n=1,254) and test (n=538) cohorts. A nomogram was established using the prognostic signature from the training cohort after multivariable Cox regression analysis. We externally validated the nomogram in a group of 369 patients from China. We separately tested for accuracy and clinical practicability using Harrell's concordance-index (C-index), calibration plots, and decision curve analysis (DCA). RESULTS: We included 2,161 patients in the analysis. Seven factors, all of which significantly affected OS, were incorporated into the final model, including age, sex, differentiation grade, the extent of surgery, lymphadenectomy, and T, N, and M stage. C-indexes for the training, test, and external validation cohorts were 0.735, 0.736, and 0.773, respectively. The areas under the time-dependent receiver operating characteristic curves at five years were 0.747, 0.798, and 0.777, respectively. The nomogram was discriminative and well-calibrated when applied to the test and external validation cohorts. Significant between-group differences in OS were observed when classifying groups by nomogram score (log-rank P<0.001). An online web server for clinical use was developed using the nomogram. CONCLUSIONS: The nomogram facilitates accurate prediction of survival for patients with LIMA and can be used to stratify clinical risk groups for individualized treatment.

Survival Nomogram for Stage IB Non-Small-Cell Lung Cancer Patients, Based on the SEER Database and an External Validation Cohort.

OBJECTIVE: This study aimed to construct a nomogram to effectively predict the overall survival (OS) of patients with stage IB non-small-cell lung cancer (NSCLC). METHODS: In total, 5513 patients with stage IB NSCLC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and used as the training cohort. We enrolled 440 patients from the Cancer Hospital, Chinese Academy of Medical Sciences, for the external validation cohort. A nomogram was constructed based on the risk factors affecting prognosis using a Cox proportional hazards regression model. The discrimination and calibration of the nomogram were evaluated by C-indexes and calibration curves. RESULTS: Six independent risk factors for OS were identified and integrated into the nomogram. The discrimination of the nomogram revealed good prognostic accuracy and clinical applicability as indicated by C-index values of 0.637 (95% CI 0.634-0.641) and 0.667 (95% CI 0.656-0.678) for the training cohort and the external validation cohort, respectively. Additionally, the patients were divided into two groups according to risk (sum-score > 185), and significant differences in OS were observed between the high-risk and low-risk groups in the training and external validation cohorts (P < 0.001). Finally, chemotherapy was significantly associated with OS in patients with differentiation grades II-IV (P = 0.004) and patients with adenocarcinoma (P = 0.005). CONCLUSION: This nomogram provides a convenient and reliable tool for individual evaluations and clinical decision-making for patients with stage IB NSCLC; among these patients, those with differentiation grades II-IV or adenocarcinoma could benefit from chemotherapy.

Synthesis and In Vitro Study of a Dual-Mode Probe Targeting Integrin αvß3.

Malignant tumors constitute a serious disease that threaten human life, and early diagnosis and metastasis prediction are critical to the choice of treatment plan and the timing of treatment. Integrin αvß3, which has received broad attention as a molecular marker of the tumor neovasculature, is an important target for monitoring tumorigenesis and progression in molecular imaging research. This study reports a magnetic resonance (MR)/fluorescence dual-mode molecular probe, cRGD-Gd-Cy5.5, which targets the integrin αvß3 receptor and uses liposomes as carrier. The obtained nanoprobe had a size of 60.08 ± 0.45 nm, with good dispersion in water, a uniform distribution of sizes, desirable stability, and high relaxivity. Its r1 relaxation rate was 10.515 mM-1 s-1, much higher than that of other Gd chelates in clinical use. The probe showed no cytotoxicity at the tested concentrations in vitro, and its ability to target A549 cells and SUNE-1-5-8F cells was preliminarily evaluated through in vitro fluorescence imaging and MR imaging. The results demonstrated that the cRGD-Gd-Cy5.5 nanoprobe had good characteristics, showing desirable stability and biosafety, a high T1 relaxation rate, and strong targeting and binding to tumors with high expression of integrin αvß3. Therefore, cRGD-Gd-Cy5.5 is a promising agent for the visual monitoring of tumor metastasis.

Value of Diffusion-Weighted Imaging Combined with Susceptibility-Weighted Imaging in Differentiating Benign from Malignant Parotid Gland Lesions.

BACKGROUND The aim of this study was to investigate the diagnostic value of diffusion-weighted imaging (DWI) in combination with susceptibility-weighted imaging (SWI) for differentiating benign parotid gland lesions from malignant ones. MATERIAL AND METHODS This retrospective study was approved by the Ethics Committee of our hospital. A total of 36 patients (26 benign cases and 10 malignant cases) were confirmed by surgical pathology. The apparent diffusion coefficient (ADC), normalized ADC (ADCNormalized), intratumoral susceptibility signals (ITSS), and morphological characteristics were analyzed with SPSS 19.0 software. RESULTS The mean ADC values of parotid gland lesions was not different between malignant and benign lesions (P=0.07), while the differences between ADCNormalized (P=0.026) and ITSS grading (P=0.014) were statistically significant. Logistic regression analysis identified use of ADCNormalized and ITSS as the only independent predictor of malignant lesions (odds ratio 0.038; 95% confidence interval 0.001~0.988; P=0.011) and (odds ratio 4.867; 95% confidence interval 1.442~16.423; P=0.049), respectively. The optimum threshold of the ADCNormalized values was -0.45%, ITSS grade was 2, the corresponding areas under the receiver operating characteristic curve (AUC) were 0.750 and 0.787 respectively, and the combination of the 2 was 0.846. CONCLUSIONS DWI integrated with SWI can significantly improve the diagnostic efficacy in distinguishing benign from malignant parotid lesions.