BACKGROUND: The log odds of positive lymph nodes (LODDS) was considered a superior staging system to N stage in colon cancer, yet its value in determining the optimal duration of adjuvant chemotherapy for stage III colon cancer patients has not been evaluated. This study aims to assess the prognostic value of a model that combines LODDS with clinicopathological information for stage III colon cancer patients and aims to stratify these patients using the model, identifying individuals who could benefit from varying durations of adjuvant chemotherapy.
METHODS: A total of 663 consecutive patients diagnosed with stage III colon cancer, who underwent colon tumor resection between November 2007 and June 2020 at Sun Yat-sen University Cancer Center and Longyan First Affiliated Hospital of Fujian Medical University, were enrolled in this study. Survival outcomes were analyzed using Kaplan-Meier, Cox regression. Nomograms were developed to forecast patient DFS, with the Area Under the Curve (AUC) values of time-dependent Receiver Operating Characteristic (timeROC) and calibration plots utilized to assess the accuracy and reliability of the nomograms.
RESULTS: Multivariate analysis revealed that perineural invasion (HR = 1.776, 95% CI: 1.052-3.003, P = 0.032), poor tumor differentiation (HR = 1.638, 95% CI: 1.084-2.475, P = 0.019), and LODDS groupings of 2 and 1 (HR = 1.920, 95% CI: 1.297-2.842, P = 0.001) were independent predictors of disease-free survival (DFS) in the training cohort. Nomograms constructed from LODDS, perineural invasion, and poor tumor differentiation demonstrated robust predictive performance for 3-year and 5-year DFS in both training (3-year AUC = 0.706, 5-year AUC = 0.678) and validation cohorts (3-year AUC = 0.744, 5-year AUC = 0.762). Stratification according to this model showed that patients in the high-risk group derived significant benefit from completing 8 cycles of chemotherapy (training cohort, 82.97% vs 67.17%, P = 0.013; validation cohort, 89.49% vs 63.97%, P = 0.030).
CONCLUSIONS: The prognostic model, integrating LODDS, pathological differentiation, and neural invasion, demonstrates strong predictive accuracy for stage III colon cancer prognosis. Moreover, stratification via this model offers valuable insights into optimal durations of postoperative adjuvant chemotherapy.

BACKGROUND: To date, radical surgery remains the best curative option in patients with early-stage lung cancer. In patients with small lung lesions, video-assisted thoracic surgery (VATS) should be increasingly chosen as a fundamental alternative to thoracotomy as it is associated with less postoperative pain and better quality of life. This scenario necessarily increases the need for thoracic surgeons to implement new localization techniques. The conventional near-infrared (NIR) indocyanine green (ICG) method demonstrated a significant limitation in deep cancer recognition, principally due to its intrinsic low-depth tissue penetration. Similarly, the lymph-node sentinel approach conducted by the ICG method was demonstrated to be inefficient, mainly due to the non-specificity of the tracker and the irregular pathway of pulmonary lymph node drainage. Our study aims to evaluate the effectiveness of Cetuximab- IRDye800CW in marking lung nodules and mediastinal lymph nodes.
METHODS: This study is defined as an open-label, single-arm, single-stage phase II trial evaluating the effectiveness of Cetuximab-IRDye800CW in detecting tumors and lymph-node metastases in patients with lung cancer who are undergoing video-assisted thoracic surgery (VATS). Cetuximab is a monoclonal antibody that binds, inhibits, and degrade the EGFR. The IRDye® 800CW, an indocyanine-type NIR fluorophore, demonstrated enhanced tissue penetration compared to other NIR dyes. The combination with the clinical approved monoclonal antibody anti-epidermal growth factor EGFR Cetuximab (Cetuximab-IRDye800) has shown promising results as a specific tracker in different cancer types (i.e., brain, pancreas, head, and neck). The study\'s primary outcome is focused on the proportion of patients with lung nodules detected during surgery using an NIR camera. The secondary outcomes include a broad spectrum of items, including the proportion of patients with detection of unexpected cancer localization during surgery by NIR camera and the proportion of patients with negative surgical margins, the evaluation of the time spawns between the insertion of the NIR camera and the visualization of the nodule and the possible morbidity of the drug assessed during and after the drug infusion.
BACKGROUND: This trial has been approved by the Ethical Committee of Azienda Ospedaliera Universitaria Città della Salute e della Scienza di Torino (Torino, Italy) and by the Italian Medicines Agency (AIFA). Findings will be written as methodology papers for conference presentations and published in peer-reviewed journals. The Azienda Ospedaliera Universitaria Città della Salute e della Scienza di Torino, the University of Torino, and the AIRC Public Engagement Divisions will help identify how best to publicize the findings.Trial registration EudraCT 202,100,645,430.
RESULTS: gov NCT06101394 (October 23, 2023).

OBJECTIVE: Patients suspected to have lung cancer, undergo endobronchial ultrasound bronchoscopy (EBUS) for the purpose of diagnosis and staging. For presumptive curable patients, the EBUS bronchoscopy is planned based on images and data from computed tomography (CT) images and positron emission tomography (PET). Our study aimed to evaluate the feasibility of a multimodal electromagnetic navigation platform for EBUS bronchoscopy, integrating ultrasound and segmented CT, and PET scan imaging data.
METHODS: The proof-of-concept study included patients with suspected lung cancer and pathological mediastinal/hilar lymph nodes identified on both CT and PET scans. Images obtained from these two modalities were segmented to delineate target lymph nodes and then incorporated into the CustusX navigation platform. The EBUS bronchoscope was equipped with a sensor, calibrated, and affixed to a 3D printed click-on device positioned at the bronchoscope\'s tip. Navigation accuracy was measured postoperatively using ultrasound recordings.
RESULTS: The study enrolled three patients, all presenting with suspected mediastinal lymph node metastasis (N1-3). All PET-positive lymph nodes were displayed in the navigation platform during the EBUS procedures. In total, five distinct lymph nodes were sampled, yielding malignant cells from three nodes and lymphocytes from the remaining two. The median accuracy of the navigation system was 7.7 mm.
CONCLUSIONS: Our study introduces a feasible multimodal electromagnetic navigation platform that combines intraoperative ultrasound with preoperative segmented CT and PET imaging data for EBUS lymph node staging examinations. This innovative approach holds promise for enhancing the accuracy and effectiveness of EBUS procedures.

BACKGROUND: The aim of this research is to prospectively investigate the diagnostic performance of intravoxel incoherent motion (IVIM) using the integrated slice-specific dynamic shimming (iShim) technique in staging primary esophageal squamous cell carcinoma (ESCC) and predicting presence of lymph node metastases from ESCC.
METHODS: Sixty-three patients with ESCC were prospectively enrolled from April 2016 to April 2019. MR and IVIM using iShim technique (b = 0, 25, 50, 75, 100, 200, 400, 600, 800 s/mm2) were performed on 3.0T MRI system before operation. Primary tumour apparent diffusion coefficient (ADC) and IVIM parameters, including true diffusion coefficient (D), pseudodiffusion coefficient (D*), pseudodiffusion fraction (f) were measured by two independent radiologists. The differences in D, D*, f and ADC values of different T and N stages were assessed. Intraclass correlation coefficients (ICCs) were calculated to evaluate the interobserver agreement between two readers. The diagnostic performances of D, D*, f and ADC values in primary tumour staging and prediction of lymph node metastasis of ESCC were determined using receiver operating characteristic (ROC) curve analysis.
RESULTS: The inter-observer consensus was excellent for IVIM parameters and ADC (D: ICC = 0.922; D*: ICC = 0.892; f: ICC = 0.948; ADC: ICC = 0.958). The ADC, D, D* and f values of group T1 + T2 were significantly higher than those of group T3 + T4a [ADC: (2.55 ± 0.43) ×10- 3 mm2/s vs. (2.27 ± 0.40) ×10- 3 mm2/s, t = 2.670, P = 0.010; D: (1.82 ± 0.39) ×10- 3 mm2/s vs. (1.53 ± 0.33) ×10- 3 mm2/s, t = 3.189, P = 0.002; D*: 46.45 (30.30,55.53) ×10- 3 mm2/s vs. 32.30 (18.60,40.95) ×10- 3 mm2/s, z=-2.408, P = 0.016; f: 0.45 ± 0.12 vs. 0.37 ± 0.12, t = 2.538, P = 0.014]. The ADC, D and f values of the lymph nodes-positive (N+) group were significantly lower than those of lymph nodes-negative (N0) group [ADC: (2.10 ± 0.33) ×10- 3 mm2/s vs. (2.55 ± 0.40) ×10- 3 mm2/s, t=-4.564, P < 0.001; D: (1.44 ± 0.30) ×10- 3 mm2/s vs. (1.78 ± 0.37) ×10- 3 mm2/s, t=-3.726, P < 0.001; f: 0.32 ± 0.10 vs. 0.45 ± 0.11, t=-4.524, P < 0.001]. The combination of D, D* and f yielded the highest area under the curve (AUC) (0.814) in distinguishing group T1 + T2 from group T3 + T4a. D combined with f provided the highest diagnostic performance (AUC = 0.849) in identifying group N + and group N0 of ESCC.
CONCLUSIONS: IVIM may be used as an effective functional imaging technique to evaluate preoperative stage of primary tumour and predict presence of lymph node metastases from ESCC.

BACKGROUND: After receiving neoadjuvant chemoradiation, the number of examined lymph nodes in resectable gastroesophageal cancer (GEC) will decrease, this may not accurately determine the N staging. So our study evaluates the clinical significance of a new staging model based on the logarithmic odds of positive lymph nodes (LODDS) in patients with GEC after receiving neoadjuvant chemoradiation.
METHODS: A total of 1 130 patients with pathologically diagnosed GEC who received neoadjuvant chemoradiation from 2004 to 2019 included in the National Cancer Institute Surveillance, Epidemiology, and Results (SEER) database were selected for analysis. Lymph nodes were staged according to the AJCC TNM staging system (eighth edition) and LODDS. Patient prognosis across the two systems were evaluated by the Kaplan-Meier method, differences in node staging were evaluated by the Akaike information criterion and Bayesian information criterion. In addition, 914 patients from our center were externally validated.
RESULTS: Compared to the traditional TNM staging system, the new TLODDSM staging system was comprised of stage I, stage II, stage IIIA, stage IIIB, and stage IVA, and decision curve analysis showed that the new staging system had higher benefits for different decision thresholds than the old staging system. The Akaike information criterion and Bayesian information criterion of the new staging system was lower than those of the old staging system, indicating the sensitivity of the TLODDSM staging system for predicting the prognosis of patients was higher. In addition, stage-IIIB or -IVA patients in the new staging system benefited from adjuvant chemotherapy. The externally validated data from our center supported this conclusion.
CONCLUSIONS: Compared to the TNM staging system, the TLODDSM staging system has significant advantages in predicting prognosis of patients with GEC who have completed neoadjuvant chemoradiation, guiding the adjuvant chemotherapy for patients.

BACKGROUND: The presence of lateral lymph node metastases (LNM) in paediatric patients with papillary thyroid cancer (PTC) is an independent risk factor for recurrence. We aimed to identify risk factors and establish a prediction model for lateral LNM before surgery in children and adolescents with PTC.
METHODS: We developed a prediction model based on data obtained from 63 minors with PTC between January 2014 and June 2023. We collected and analysed clinical factors, ultrasound (US) features of the primary tumour, and pathology records of the patients. Multivariate logistic regression analysis was used to determine independent predictors and build a prediction model. We evaluated the predictive performance of risk factors and the prediction model using the area under the receiver operating characteristic (ROC) curve. We assessed the clinical usefulness of the predicting model using decision curve analysis.
RESULTS: Among the minors with PTC, 21 had lateral LNM (33.3%). Logistic regression revealed that independent risk factors for lateral LNM were multifocality, tumour size, sex, and age. The area under the ROC curve for multifocality, tumour size, sex, and age was 0.62 (p = 0.049), 0.61 (p = 0.023), 0.66 (p = 0.003), and 0.58 (p = 0.013), respectively. Compared to a single risk factor, the combined predictors had a significantly higher area under the ROC curve (0.842), with a sensitivity and specificity of 71.4% and 81.0%, respectively (cutoff value = 0.524). Decision curve analysis showed that the prediction model was clinically useful, with threshold probabilities between 2% and 99%.
CONCLUSIONS: The independent risk factors for lateral LNM in paediatric PTC patients were multifocality and tumour size on US imaging, as well as sex and age. Our model outperformed US imaging and clinical features alone in predicting the status of lateral LNM.

OBJECTIVE: To evaluate the 5-year survival rate of patients with vulvar cancer, taking into account prognostic factors (International Federation of Gynecology and Obstetrics (FIGO) stage and age) and the influence of lymph node involvement and tumour size as well as the correlation between FIGO stage and age at onset.
METHODS: Retrospective cohort study.
METHODS: German cancer registries.
METHODS: The study included 17 017 patients diagnosed with vulvar cancer between 2004 and 2014 with follow-up until 2017. Exclusion criteria were incomplete documentation, age<18 years and death certificate only.
METHODS: The primary outcome was overall and relative survival according to FIGO stage and age and the distribution of FIGO stages by age group. Secondary outcomes analysed were tumour size and lymph node status as risk factors for mortality.
RESULTS: The median age at diagnosis was 70 years. The overall 5-year survival rate was 69.5% (95% CI: 68.7% to 70.3%). The 5-year relative survival rates ranged from 91.9% (95% CI: 90.5% to 93.3%) to 21.3% (95% CI: 15.6% to 27.0%) for FIGO I to FIGO IVB and from 89.2% (95% CI: 87.6% to 90.8%) to 68.0% (95% CI: 65.3% to 70.7%) for age groups <55 to >75 years. Younger patients were significantly more often diagnosed with a low FIGO stage (p<0.001). Lymph node status and tumour size were independent factors influencing survival (HR: 1.79 (95% CI: 1.73 to 1.84; p<0.001) and 1.88 (95% CI: 1.80 to 1.96); p<0.001, respectively). The median follow-up time was 57 months.
CONCLUSIONS: Notably, the steepest decline in survival occurred within the first 3 years after diagnosis. Patients with the highest FIGO stages and those in the oldest age group had the worst survival rates. Furthermore, patients in the oldest age group were more likely to be diagnosed at higher stages. Lymph node status and tumour size were additional independent prognostic factors for mortality.

OBJECTIVE: This study aimed to evaluate the incidence and predict the risk factors of lymph node (LN) metastasis among patients with grossly apparent early-stage epithelial ovarian cancer (EOC).
METHODS: We retrospectively reviewed the clinicopathologic data and follow-up information of 266 patients who underwent LN dissection for apparent early-stage EOC between January 2018 and September 2022 at the Obstetrics and Gynecology Hospital of Fudan University.
RESULTS: Among 266 patients, 44 (16.5%) showed LN metastasis, of which 65.9% and 59.1% presented in the pelvic region and para-aortic region, respectively. Univariate analysis revealed higher LN positivity in patients with high-grade serous carcinoma (HGSC), preoperative imaging suggestive of LN metastasis, bilateral adnexal involvement, lymphovascular space invasion (LVSI), positive peritoneal cytology, and clinical stage IIA. LN metastases were identified in 7.9%, 10.2%, and 39.7% of clinical stage IA/B, IC, and IIA disease cases, respectively. Multivariate analysis confirmed significantly higher LN positivity rates in patients with HGSC, LVSI, and clinical stage IIA. In clinical stage IIA EOC, the 3-year progression-free survival (PFS) rates were 65.8% and 77.4% (P = 0.360) for LN-negative and LN-positive groups, respectively. In clinical stage I EOC, the 3-year PFS rates were 93.5% and 59.4% (P < 0.001) for LN-negative and LN-positive groups, respectively.
CONCLUSIONS: High-grade serous histology, LVSI, and clinical stage IIA disease are predictive factors for LN involvement in early-stage EOC. In addition, LN metastasis appears to be associated with worse PFS in clinical stage I EOC compared with clinical stage IIA EOC.

UNASSIGNED: The tumor burden within the axillary lymph nodes (ALNs) constitutes a pivotal factor in breast cancer, serving as the primary determinant for treatment decisions and exhibiting a close correlation with prognosis.
UNASSIGNED: This study aimed to investigate the potential of ultrasound-based radiomics and clinical characteristics in non-invasively distinguishing between low tumor burden (1-2 positive nodes) and high tumor burden (more than 2 positive nodes) in patients with node-positive breast cancer.
UNASSIGNED: A total of 215 patients with node-positive breast cancer, who underwent preoperative ultrasound examinations, were enrolled in this study. Among these patients, 144 cases were allocated to the training set, 37 cases to the validation set, and 34 cases to the testing set. Postoperative histopathology was used to determine the status of ALN tumor burden. The region of interest for breast cancer was delineated on the ultrasound image. Nine models were developed to predict high ALN tumor burden, employing a combination of three feature screening methods and three machine learning classifiers. Ultimately, the optimal model was selected and tested on both the validation and testing sets. In addition, clinical characteristics were screened to develop a clinical model. Furthermore, Shapley additive explanations (SHAP) values were utilized to provide explanations for the machine learning model.
UNASSIGNED: During the validation and testing sets, the models demonstrated area under the curve (AUC) values ranging from 0.577 to 0.733 and 0.583 to 0.719, and accuracies ranging from 64.9% to 75.7% and 64.7% to 70.6%, respectively. Ultimately, the Boruta_XGB model, comprising five radiomics features, was selected as the final model. The AUC values of this model for distinguishing low from high tumor burden were 0.828, 0.715, and 0.719 in the training, validation, and testing sets, respectively, demonstrating its superiority over the clinical model.
UNASSIGNED: The developed radiomics models exhibited a significant level of predictive performance. The Boruta_XGB radiomics model outperformed other radiomics models in this study.

OBJECTIVE: The ultrasonographic characteristics of lymph node metastasis in breast cancer patients were retrospectively analyzed, and a predictive nomogram model was constructed to provide an imaging basis for better clinical evaluation.
METHODS: B-mode ultrasound was used to retrospectively analyze the imaging characteristics of regional lymph nodes and tumors. Pathological examination confirmed the presence of lymph node metastasis in breast cancer patients. Univariable and multivariable logistic regression analyses were performed to analyze the risk factors for lymph node metastasis. LASSO regression analysis was performed to screen noninvasive indicators, and a nomogram prediction model was constructed for breast cancer patients with lymph node metastasis.
RESULTS: A total of 187 breast cancer patients were enrolled, including 74 patients with lymph node metastasis in the positive group and 113 patients without lymph node metastasis in the negative group. Multivariate analysis revealed that pathological type (OR = 4.58, 95% CI: 1.44-14.6, p = 0.01), tumor diameter (OR = 1.37, 95% CI: 1.07-1.74, p = 0.012), spiculated margins (OR = 7.92, 95% CI: 3.03-20.67, p < 0.001), mixed echo of the breast tumor (OR = 37.09, 95% CI: 3.49-394.1, p = 0.003), and unclear lymphatic hilum structure (OR = 16.07, 95% CI: 2.41-107.02, p = 0.004) were independent risk factors for lymph node metastasis. A nomogram model was constructed for predicting breast cancer with lymph node metastasis, incorporating three significantly correlated indicators identified through LASSO regression analysis, namely, tumor spiculated margins, cortical thickness of lymph nodes, and unclear lymphatic hilum structure. The receiver operating characteristic (ROC) curve revealed that the area under the curve (AUC) was 0.717 (95% CI, 0.614-0.820) for the training set and 0.817 (95% CI, 0.738-0.890) for the validation set. The Hosmer-Lemeshow test results for the training set and the validation set were p = 0.9148 and p = 0.1648, respectively. The prediction nomogram has good diagnostic performance.
CONCLUSIONS: B-mode ultrasound is helpful in the preoperative assessment of breast cancer patients with lymph node metastasis. The predictive nomogram model, which is based on logistic regression and LASSO regression analysis, is clinically safe, reliable, and highly practical.