OBJECTIVE: Risk stratification of regional recurrence (RR) is clinically important in the design of adjuvant treatment and surveillance strategies in patients with clinical stage I non-small cell lung cancer (NSCLC) treated with stereotactic body radiation therapy (SBRT). This study aimed to develop a radiomics model predicting occult lymph node metastasis (OLNM) using surgical data and apply it to the prediction of RR in SBRT-treated early-stage NSCLC patients.
METHODS: Patients with clinical stage I NSCLC who underwent curative surgery with systematic lymph node dissection from January 2013 to December 2018 (the training cohort) and from January 2019 to December 2020 (the validation cohort) were included. A preoperative computed tomography-based radiomics model, a clinical feature model, and a fusion model predicting OLNM were constructed. The performance of the 3 models was quantified and compared in the training and validation cohorts. Subsequently, the radiomics model was used to predict RR in a cohort of consecutive SBRT-treated early-stage NSCLC patients from 2 academic medical centers.
RESULTS: A total of 769 patients were included. Eight computed tomography features were identified in the radiomics model, achieving areas under the curves of 0.85 (95% CI, 0.81-0.89) and 0.83 (95% CI, 0.80-0.88) in the training and validation cohorts, respectively. Nevertheless, adding clinical features did not improve the performance of the radiomics model. With a median follow-up of 40.0 (95% CI, 35.2-44.8) months, 32 of the 213 patients in the SBRT cohort developed RR and those in the high-risk group based on the radiomics model had a higher cumulative incidence of RR (P < .001) and shorter regional recurrence-free survival (P = .02), progression-free survival (P = .004) and overall survival (P = .006) than those in the low-risk group.
CONCLUSIONS: The radiomics model based on pathologically confirmed data effectively identified patients with OLNM, which may be useful in the risk stratification among SBRT-treated patients with clinical stage I NSCLC.

Occult lymph node metastasis (OLNM) is one of the main causes of regional recurrence in inoperable N0 non-small cell lung cancer (NSCLC) patients following stereotactic ablation body radiotherapy (SABR) treatment. The integration of immunotherapy and SABR (I-SABR) has shown preliminary efficiency in mitigating this recurrence. Therefore, it is necessary to explore the functional dynamics of critical immune effectors, particularly CD8+ T cells in the development of OLNM. In this study, tissue microarrays (TMAs) and multiplex immunofluorescence (mIF) were used to identify CD8+ T cells and functional subsets (cytotoxic CD8+ T cells/predysfunctional CD8+ T cells (CD8+ Tpredys)/dysfunctional CD8+ T cells (CD8+ Tdys)/other CD8+ T cells) among the no lymph node metastasis, OLNM, and clinically evident lymph node metastasis (CLNM) groups. As the degree of lymph node metastasis escalated, the density of total CD8+ T cells and CD8+ Tdys cells, as well as their proximity to tumor cells, increased progressively and remarkably in the invasive margin (IM). In the tumor center (TC), both the density and proximity of CD8+ Tpredys cells to tumor cells notably decreased in the OLNM group compared with the group without metastasis. Furthermore, positive correlations were found between the dysfunction of CD8+ T cells and HIF-1α+CD8 and cancer microvessels (CMVs). In conclusion, the deterioration in CD8+ T cell function and interactive dynamics between CD8+ T cells and tumor cells play a vital role in the development of OLNM in NSCLC. Strategies aimed at improving hypoxia or targeting CMVs could potentially enhance the efficacy of I-SABR.

OBJECTIVE: To evaluate the effectiveness of deep learning radiomics nomogram in distinguishing the occult lymph node metastasis (OLNM) status in clinical stage IA lung adenocarcinoma.
METHODS: A cohort of 473 cases of lung adenocarcinomas from two hospitals was included, with 404 cases allocated to the training cohort and 69 cases to the testing cohort. Clinical characteristics and semantic features were collected, and radiomics features were extracted from the computed tomography (CT) images. Additionally, deep transfer learning (DTL) features were generated using RseNet50. Predictive models were developed using the logistic regression (LR) machine learning algorithm. Moreover, gene analysis was conducted on RNA sequencing data from 14 patients to explore the underlying biological basis of deep learning radiomics scores.
RESULTS: The training and testing cohorts achieved AUC values of 0.826 and 0.775 for the clinical model, 0.865 and 0.801 for the radiomics model, 0.927 and 0.885 for the DTL-radiomics model, and 0.928 and 0.898 for the nomogram model. The nomogram model demonstrated superiority over the clinical model. The decision curve analysis (DCA) revealed a net benefit in predicting OLNM for all models. The investigation into the biological basis of deep learning radiomics scores identified an association between high scores and pathways related to tumor proliferation and immune cell infiltration in the microenvironment.
CONCLUSIONS: The nomogram model, incorporating clinical-semantic features, radiomics, and DTL features, exhibited promising performance in predicting OLNM. It has the potential to provide valuable information for non-invasive lymph node staging and individualized therapeutic approaches.

UNASSIGNED: Active surveillance (AS) is generally accepted as an alternative to immediate surgery for papillary thyroid carcinoma (PTC) measuring ≤1.0 cm (cT1a) without risk factors. This study investigated the clinicopathologic characteristics of PTCs measuring ≤2.0 cm without cervical lymph node metastasis (cT1N0) by tumor size group to assess the feasibility of AS for PTCs between 1.0 cm and 1.5 cm (cT1b≤1.5).
UNASSIGNED: This study enrolled clinically T1N0 patients with preoperative ultrasonography information (n= 935) from a cohort of 1259 patients who underwent lobectomy and were finally diagnosed with PTC from June 2020 to March 2022.
UNASSIGNED: The cT1b≤1.5 group (n = 171; 18.3 %) exhibited more lymphatic invasion and occult central lymph node (LN) metastasis with a higher metastatic LN ratio than the cT1a group (n = 719; 76.9 %). However, among patients aged 55 years or older, there were no significant differences in occult central LN metastasis and metastatic LN ratio between the cT1a, cT1b≤1.5, and cT1b>1.5 groups. Multivariate regression analyses revealed that occult central LN metastasis was associated with age, sex, tumor size, extrathyroidal extension, and lymphatic invasion in patients under 55, while in those aged 55 or older, it was associated only with age and lymphatic invasion.
UNASSIGNED: For PTC patients aged 55 years or older with cT1b≤1.5, AS could be a viable option due to the absence of a significant relationship between tumor size and occult central LN.

UNASSIGNED: The occult lymph node metastasis (LNM) of laryngeal squamous cell carcinoma (LSCC) affects the treatment and prognosis of patients. This study aimed to comprehensively compare the performance of the three-dimensional and two-dimensional deep learning models, radiomics model, and the fusion models for predicting occult LNM in LSCC.
UNASSIGNED: In this retrospective diagnostic study, a total of 553 patients with clinical N0 stage LSCC, who underwent surgical treatment without distant metastasis and multiple primary cancers, were consecutively enrolled from four Chinese medical centres between January 01, 2016 and December 30, 2020. The participant data were manually retrieved from medical records, imaging databases, and pathology reports. The study cohort was divided into a training set (n = 300), an internal test set (n = 89), and two external test sets (n = 120 and 44, respectively). The three-dimensional deep learning (3D DL), two-dimensional deep learning (2D DL), and radiomics model were developed using CT images of the primary tumor. The clinical model was constructed based on clinical and radiological features. Two fusion strategies were utilized to develop the fusion model: the feature-based DLRad_FB model and the decision-based DLRad_DB model. The discriminative ability and correlation of 3D DL, 2D DL and radiomics features were analysed comprehensively. The performances of the predictive models were evaluated based on the pathological diagnosis.
UNASSIGNED: The 3D DL features had superior discriminative ability and lower internal redundancy compared to 2D DL and radiomics features. The DLRad_DB model achieved the highest AUC (0.89-0.90) among all the study sets, significantly outperforming the clinical model (AUC = 0.73-0.78, P = 0.0001-0.042, Delong test). Compared to the DLRad_DB model, the AUC values for the DLRad_FB, 3D DL, 2D DL, and radiomics models were 0.82-0.84 (P = 0.025-0.46), 0.86-0.89 (P = 0.75-0.97), 0.83-0.86 (P = 0.029-0.66), and 0.79-0.82 (P = 0.0072-0.10), respectively in the study sets. Additionally, the DLRad_DB model exhibited the best sensitivity (82-88%) and specificity (79-85%) in the test sets.
UNASSIGNED: The decision-based fusion model DLRad_DB, which combines 3D DL, 2D DL, radiomics, and clinical data, can be utilized to predict occult LNM in LSCC. This has the potential to minimize unnecessary lymph node dissection and prophylactic radiotherapy in patients with cN0 disease.
UNASSIGNED: National Natural Science Foundation of China, Natural Science Foundation of Shandong Province.

BACKGROUND: In solid-predominantly invasive lung adenocarcinoma (SPILAC), occult lymph node metastasis (OLNM) is pivotal for determining treatment strategies. This study seeks to develop and validate a fusion model combining radiomics and deep learning to predict OLNM preoperatively in SPILAC patients across multiple centers.
METHODS: In this study, 1325 cT1a-bN0M0 SPILAC patients from six hospitals were retrospectively analyzed and divided into pathological nodal positive (pN+) and negative (pN-) groups. Three predictive models for OLNM were developed: a radiomics model employing decision trees and support vector machines; a deep learning model using ResNet-18, ResNet-34, ResNet-50, DenseNet-121, and Swin Transformer, initialized randomly or pre-trained on large-scale medical data; and a fusion model integrating both approaches using addition and concatenation techniques. The model performance was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC).
RESULTS: All patients were assigned to four groups: training set (n = 470), internal validation set (n = 202), and independent test set 1 (n = 227) and 2 (n = 426). Among the 1325 patients, 478 (36%) had OLNM (pN+). The fusion model, combining radiomics with pre-trained ResNet-18 features via concatenation, outperformed others with an average AUC (aAUC) of 0.754 across validation and test sets, compared to aAUCs of 0.715 for the radiomics model and 0.676 for the deep learning model.
CONCLUSIONS: The radiomics-deep learning fusion model showed promising ability to generalize in predicting OLNM from CT scans, potentially aiding personalized treatment for SPILAC patients across multiple centers.

BACKGROUND: The current NCCN guidelines recommend considering elective neck dissection (END) for early-stage oral cavity squamous cell carcinoma (OCSCC) with a depth of invasion (DOI) exceeding 3 mm. However, this DOI threshold, determined by evaluating the occult lymph node metastatic rate, lacks robust supporting evidence regarding its impact on patient outcomes. In this nationwide study, we sought to explore the specific indications for END in patients diagnosed with OCSCC at stage cT2N0M0, as defined by the AJCC Eighth Edition staging criteria.
METHODS: We examined 4723 patients with cT2N0M0 OCSCC, of which 3744 underwent END and 979 were monitored through neck observation (NO).
RESULTS: Patients who underwent END had better 5-year outcomes compared to those in the NO group. The END group had higher rates of neck control (95% vs. 84%, p < 0.0001), disease-specific survival (DSS; 87% vs. 84%, p = 0.0259), and overall survival (OS; 79% vs. 73%, p = 0.0002). Multivariable analysis identified NO, DOI ≥5.0 mm, and moderate-to-poor tumor differentiation as independent risk factors for 5-year neck control, DSS, and OS. Based on these prognostic variables, three distinct outcome subgroups were identified within the NO group. These included a low-risk subgroup (DOI <5 mm plus well-differentiated tumor), an intermediate-risk subgroup (DOI ≥5.0 mm or moderately differentiated tumor), and a high-risk subgroup (poorly differentiated tumor or DOI ≥5.0 mm plus moderately differentiated tumor). Notably, the 5-year survival outcomes (neck control/DSS/OS) for the low-risk subgroup within the NO group (97%/95%/85%, n = 251) were not inferior to those of the END group (95%/87%/79%).
CONCLUSIONS: By implementing risk stratification within the NO group, we found that 26% (251/979) of low-risk patients achieved outcomes similar to those in the END group. Therefore, when making decisions regarding the implementation of END in patients with cT2N0M0 OCSCC, factors such as DOI and tumor differentiation should be taken into account.

暂无摘要。

BACKGROUND: This study was to compare the clinical presentations and survivals between the non-small cell lung cancer (NSCLC) patients with occult lymph node metastasis (OLNM) and those with evident lymph node metastasis (ELNM). We also intended to analyze the predictive factors for OLNM.
METHODS: Kaplan-Meier method with log-rank test was used to compare survivals between groups. Propensity score matching (PSM) was used to reduce bias. The least absolute shrinkage and selection operator (LASSO)-penalized Cox multivariable analysis was used to identify the prognostic factors. Random forest was used to determine the predictive factors for OLNM.
RESULTS: A total of 2,067 eligible cases (N0: 1,497 cases; occult N1: 165 cases; evident N1: 54 cases; occult N2: 243 cases; evident N2: 108 cases) were included. The rate of OLNM was 21.4%. Patients with OLNM were tend to be female, non-smoker, adenocarcinoma and had smaller-sized tumors when compared with the patients with ELNM. Survival curves showed that the survivals of the patients with OLNM were similar to those of the patients with ELNM both before and after PSM. Multivariable Cox analysis suggested that positive lymph nodes (PLN) was the only prognostic factor for the patients with OLNM. Random forest showed that clinical tumor size was an important predictive factor for OLNM.
CONCLUSIONS: OLNM was not rare. OLNM was not a favorable sign for resected NSCLC patients with lymph node metastasis. PLN determined the survivals of the patients with OLNM. Clinical tumor size was a strong predictive factor for OLNM.

While papillary thyroid carcinoma (PTC) is associated with high occult central neck metastasis (CNM) rates, prophylactic central neck dissection (pCND) is controversial. This meta-analysis aims to look at the occult CNM rate according to tumor size.
A literature search was conducted in PubMed from inception to April 2023. Inclusion criteria were primary studies that determined occult CNM rates in cN0 PTC by tumor size. Heterogeneity, influential case diagnostics, and proportion data were evaluated with Cochran\'s Q-test, Baujat plots and Forest plots, respectively.
Fifty-two studies were included in this meta-analysis. The findings demonstrated an occult CNM rate of 30.3% for tumors ≤ 5 mm, 32.7% for tumors ≤ 1 cm, 46.0% for tumors between 1 and 2 cm, 43.1% for tumors between 2 and 4 cm, and 61.2% for tumors > 4 cm. The heterogeneity of each study group was high, though no publication bias was noted. While there was a trend towards increased occult CNM rates with larger tumors, comparisons between different size cutoffs varied in significance.
This comprehensive review affirms that occult CNM is high and that an ipsilateral pCND can be justified in all PTC patients for accurate differentiation between Stage I and Stage II disease and its clinical implications.