Prognostic Relevance of Recurrent Sites of Gastric Cancer Treated With Curative Resection: A Single Center Retrospective Study.

PURPOSE: Gastric cancer treated with curative resection exhibits several recurrence patterns. The peritoneum is the most common site of recurrence. Some reports have indicated different prognostic influences according to the recurrence sites in other cancers, such as esophageal and colorectal cancers. This study investigated whether the recurrence sites influenced the prognosis of patients with recurrent gastric cancer. MATERIALS AND METHODS: The data of 115 patients who experienced tumor recurrence after curative gastrectomy were retrospectively reviewed. The sites of recurrence were divided into 4 groups: lymph node (LN), peritoneum, other single organs, and multiple lesions. Clinicopathological features were compared between the sites of recurrence. Prognosis after resection and recurrence were also compared. RESULTS: The peritoneum was the primary site of recurrence in 38 patients (33%). The tumor differentiation and pathological stages were significantly different. Survival after surgery did not show a statistically significant difference (hazard ratio [HR] of LN: 1, peritoneum: 1.083, other single organs: 1.025, and multiple lesions: 1.058; P=1.00). Survival after recurrence was significantly different (HR of LN, 1; peritoneum, 2.164; other single organs, 1.092; multiple lesions, 1.554; P=0.01), and patients with peritoneal and multiple lesion recurrences had worse prognosis. Furthermore, peritoneal recurrence seemed to occur later than that at other sites; the median times to recurrence in LN, peritoneal, other single-organ, and multiple lesions were 265, 722, 372, and 325 days, respectively. CONCLUSIONS: The sites of gastric cancer recurrence may have different prognostic effects. Peritoneal recurrence may be less sensitive to chemotherapy and occur during the late phase of recurrence.

Predictors of lateral lymph node metastasis and skip metastasis in patients with papillary thyroid microcarcinoma.

Background: Papillary thyroid microcarcinoma (PTMC) is characterized by its favorable prognosis and potential for active surveillance (AS) as a management option. However, the presence of cervical lymph node (LN) metastasis, especially lateral LN metastasis, significantly impacts management and prognosis. Previous studies have focused on post-surgery risk factors for cervical LN metastasis. This study aims to identify predictors of lateral LN metastasis by analyzing pre-operative ultrasonographic findings alongside clinicopathological factors. Methods: A retrospective review of medical records was conducted for patients with PTMC who underwent surgery at Chonnam National University Hwasun Hospital between 2004 and 2013. This is a case-control study that compares patients with lateral LN metastasis (N1b) to age- and sex-matched patients without LN metastasis (N0). Subgroup analysis was performed to evaluate risk factors of skip metastasis. Results: The study included 90 patients with PTMC with lateral LN metastasis (N1b) and 268 age- and sex-matched patients without LN metastasis (N0). The mean age was 49.3 years, and female patients were dominant in both groups. Structural recurrences of 4.4% (4/90) were observed only in the N1b group. The N1b group exhibited a higher frequency of upper lobe tumor location compared to the N0 group (38.9% vs. 16.0%, p < 0.001). There was no significant difference in the locations with the presence of invasion to adjacent organs. A higher proportion of non-parallel shape was observed in the N1b group than the N0 group (80.0% vs. 66.0%, p = 0.013). There were no differences in echogenicity, sonographic feature, margin, and AP diameter of the thyroid gland between the two groups. In multivariate analysis, independent risk factors for lateral LN metastasis included extrathyroidal extension, multiplicity, upper lobe tumor location, and non-parallel shape. Skip metastasis in patients with PTMC was associated with upper lobe tumor location. Conclusion: Detailed ultrasound examinations, evaluating tumor location, number, orientation, and the presence of ETE, are crucial in accurately predicting lateral LN metastasis especially when primary tumor was in the upper lobe to avoid missing skip metastasis. These evaluations can help guide the decision between AS and immediate surgery in patients with PTMC.

Cancer histology in metastatic lymph node predicts prognosis in patients with node-positive stage IV colorectal cancer.

Background: Appropriate prognostic indicators are required for patients with stage IV colorectal cancer (CRC). Lymph node metastasis mainly involves four histological types of CRC. Some metastatic lymph nodes (mLNs) showing cribriform carcinoma are associated with distant metastasis in patients with node-positive CRC and are correlated with recurrence and survival in stage III disease. However, the significance of mLN histology in the prognosis of patients with node-positive stage IV disease remains unclear. Methods: We enrolled 449 consecutive patients with CRC who underwent primary tumor resection with lymph node dissection between January 2011 and November 2018. This study included 88 patients with node-positive stage IV CRC and synchronous or metachronous distant metastases. We retrospectively investigated the association between cancer histology in the mLNs based on our classification and cancer-specific survival (CSS) in patients with node-positive stage IV CRC. Results: Kaplan-Meier analysis showed that CSS was better in patients with CRC and all the mLNs showing tubular-type carcinoma. In contrast, patients with at least some mLNs showing poorly differentiated-type carcinoma had poor prognosis. Multivariate analysis showed that "all mLNs showing tubular-type carcinoma" was an independent good prognostic factor for CSS in patients with node-positive stage IV CRC. In addition, "at least some mLNs showing poorly differentiated-type carcinoma" was an independent poor prognostic factor for CSS in patients with node-positive stage IV disease. Conclusions: The histological type of the mLN may indicate a better or poor prognosis for patients with stage IV CRC.

Predictive value of an ultrasound-based radiomics model for central lymph node metastasis of papillary thyroid carcinoma.

Purpose: We aimed to explore the predictive value of an ultrasound-based radiomics model for the central lymph node metastasis of papillary thyroid carcinoma. Methods: A total of 126 patients with papillary thyroid carcinoma treated between February 2021 and February 2023 were retrospectively enrolled and assigned into metastasis group (n=59, with cervical central lymph node metastasis) or non-metastasis group (n=67, without metastasis) based on surgical and pathological findings. Intergroup comparisons were conducted on the results of contrast-enhanced ultrasonography, preoperative conventional ultrasonography, as well as real-time shear wave elastography. Results: The maximum lesion diameter, echo, margin, capsule invasion, calcification, average elasticity modulus (Eavg), rising time (RT), and peak intensity (PI) had diagnostic value for papillary thyroid carcinoma, and their combination exhibited higher diagnostic value (area under the curve: 0.817). The logistic regression model was built, and the maximum lesion diameter, hypoechoic/extremely hypoechoic, lobulated or irregular margin (95% confidence interval: 1.451-6.755), capsule invasion, microcalcification/macrocalcification or peripheral calcification, high-level Eavg, low-level RT and high-level PI served as risk elements affecting papillary thyroid carcinoma from the aspect of central lymph node metastasis (odds ratio>1, P<0.05). According to the logistic regression model, the model was reliable and stable (area under the curve: 0.889, P<0.05). Conclusion: The established ultrasound-based radiomics model can be utilized for early identifying the central lymph node metastasis of papillary thyroid carcinoma.

An optical photothermal infrared investigation of lymph nodal metastases of oral squamous cell carcinoma.

In this study, optical photothermal infrared (O-PTIR) spectroscopy combined with machine learning algorithms were used to evaluate 46 tissue cores of surgically resected cervical lymph nodes, some of which harboured oral squamous cell carcinoma nodal metastasis. The ratios obtained between O-PTIR chemical images at 1252 cm-1 and 1285 cm-1 were able to reveal morphological details from tissue samples that are comparable to the information achieved by a pathologist's interpretation of optical microscopy of haematoxylin and eosin (H&E) stained samples. Additionally, when used as input data for a hybrid convolutional neural network (CNN) and random forest (RF) analyses, these yielded sensitivities, specificities and precision of 98.6 ± 0.3%, 92 ± 4% and 94 ± 5%, respectively, and an area under receiver operator characteristic (AUC) of 94 ± 2%. Our findings show the potential of O-PTIR technology as a tool to study cancer on tissue samples.

Prognostic Value of Preoperative N Subcategories in Patients with Stage IIIA N2 Non-Small Cell Lung Cancer.

Purpose To evaluate the preoperative risk factors in patients with pathologic IIIA N2 non-small cell lung cancer (NSCLC) who underwent upfront surgery and to evaluate the prognostic value of new N subcategories. Materials and Methods Patients with pathologic stage IIIA N2 NSCLC who underwent upfront surgery in a single tertiary center from January 2015 to April 2021 were retrospectively reviewed. Each patient's clinical N (cN) was assigned to one of six subcategories (cN0, cN1a, cN1b, cN2a1, cN2a2, and cN2b) based on recently proposed N descriptors. Cox regression analysis was used to identify the significant prognostic factors for recurrence-free survival (RFS) and overall survival (OS). Results A total of 366 patients (mean age ± SD, 62.0 years ± 10.1; 202 male patients [55%]) were analyzed. The recurrence rate was 55% (203 of 366 patients) over a median follow-up of 37.3 months. Multivariable analysis demonstrated that cN (hazard ratios [HRs] for cN1 and cN2b compared with cN0, 1.66 [95% CI: 1.11, 2.48] and 2.11 [95% CI: 1.32, 3.38], respectively) and maximum lymph node (LN) size at N1 station (≥12 mm; HR, 1.62 [95% CI: 1.15, 2.29]), in addition to clinical T category (HR, 1.51 [95% CI: 1.14, 1.99]), were independent prognostic factors for RFS. For OS, clinical N subcategories (cN1, cN2a2, and cN2b vs cN0; HRs, 1.91 [95% CI: 1.11, 3.27], 1.89 [95% CI: 1.13, 2.18], and 2.02 [95% CI: 1.07, 3.80], respectively) and LN size at N1 station (HR, 1.75 [95% CI: 1.12, 2.71]) were independent prognostic factors. For clinical N1, OS was further stratified according to LN size (log-rank test, P < .001). Conclusion Assessing the proposed N subcategories by reporting single versus multistation involvement of N2 disease and maximum size of metastatic LN, reflecting metastatic burden, at preoperative CT may offer useful prognostic information for planning optimal treatment strategies. Keywords: CT, Lung, Staging, Non-Small Cell Lung Cancer Supplemental material is available for this article. ©RSNA, 2024.

Risk factors for cervical lymph node metastasis of papillary thyroid cancer in elderly patients aged 65 and older.

Objective: To assess the risk factors of cervical lymph node metastasis in elderly patients aged 65 years and older diagnosed with papillary thyroid cancer (PTC). Design and method: In this retrospective analysis, we included a total of 328 elderly patients aged 65 years and older diagnosed with PTC. We thoroughly examined clinical features from these patients. Utilizing univariate and multivariate logistic regression analyses, we aimed to identify factors contributing to the risk of central and lateral lymph node metastasis (CLNM/LLNM) in this specific population of PTC patients aged 65 years and older. Results: In the univariate analysis, CLNM was significantly associated with tumor size, multifocality, bilaterality, and microcalcification, while only tumor size ≥ 1cm (OR = 0.530, P = 0.019, 95% CI = 0.311 - 0.900) and multifocality (OR = 0.291, P < 0.001, 95% CI = 0.148 - 0.574) remained as risk factors in the multivariate analysis. LLNM was confirmed to be associated with male (OR = 0.454, P < 0.020, 95% CI = 0.233 - 0.884), tumor size ≥ 1cm (OR = 0.471, P = 0.030, 95% CI = 0.239 - 0.928), age ≥ 70 (OR = 0.489, P = 0.032, 95% CI = 0.254 - 0.941), and microcalcification (OR = 0.384, P = 0.008, 95% CI = 0.189 - 0.781) in the multivariate analysis. In elderly PTC patients with CLNM, male gender (OR = 0.350, P = 0.021, 95% CI = 0.143 - 0.855), age ≥ 70 (OR = 0.339, P = 0.015, 95% CI = 0.142 - 0.810), and bilaterality (OR = 0.320, P = 0.012, 95% CI = 0.131 - 0.779) were closely associated with concomitant LLNM in both univariate and multivariate analyses. Conclusion: For elderly PTC patients aged 65 and older, tumor size ≥ 1cm and multifocality are significant risk factors for CLNM. Meanwhile, male, tumor size ≥ 1cm, age ≥ 70, and microcalcification are crucial predictors for LLNM. In patients already diagnosed with CLNM, male, age ≥ 70, and bilaterality increase the risk of LLNM.

Development and validation of a clinical predictive model for high-volume lymph node metastasis of papillary thyroid carcinoma.

The central lymph node metastasis (CLNM) status in the cervical region serves as a pivotal determinant for the extent of surgical intervention and prognosis in papillary thyroid carcinoma (PTC). This paper seeks to devise and validate a predictive model based on clinical parameters for the early anticipation of high-volume CLNM (hv-CLNM, > 5 nodes) in high-risk patients. A retrospective analysis of the pathological and clinical data of patients with PTC who underwent surgical treatment at Medical Centers A and B was conducted. The data from Center A was randomly divided into training and validation sets in an 8:2 ratio, with those from Center B serving as the test set. Multifactor logistic regression was harnessed in the training set to select variables and construct a predictive model. The generalization ability of the model was assessed in the validation and test sets. The model was evaluated through the receiver operating characteristic area under the curve (AUC) to predict the efficiency of hv-CLNM. The goodness of fit of the model was examined via the Brier verification technique. The incidence of hv-CLNM in 5897 PTC patients attained 4.8%. The occurrence rates in males and females were 9.4% (128/1365) and 3.4% (156/4532), respectively. Multifactor logistic regression unraveled male gender (OR = 2.17, p < .001), multifocality (OR = 4.06, p < .001), and lesion size (OR = 1.08 per increase of 1 mm, p < .001) as risk factors, while age emerged as a protective factor (OR = 0.95 per an increase of 1 year, p < .001). The model constructed with four predictive variables within the training set exhibited an AUC of 0.847 ([95%CI] 0.815-0.878). In the validation and test sets, the AUCs were 0.831 (0.783-0.879) and 0.845 (0.789-0.901), respectively, with Brier scores of 0.037, 0.041, and 0.056. Subgroup analysis unveiled AUCs for the prediction model in PTC lesion size groups (≤ 10 mm and > 10 mm) as 0.803 (0.757-0.85) and 0.747 (0.709-0.785), age groups (≤ 31 years and > 31 years) as 0.778 (0.720-0.881) and 0.837 (0.806-0.867), multifocal and solitary cases as 0.803 (0.767-0.838) and 0.809 (0.769-0.849), and Hashimoto's thyroiditis (HT) and non-HT cases as 0.845 (0.793-0.897) and 0.845 (0.819-0.871). Male gender, multifocality, and larger lesion size are risk factors for hv-CLNM in PTC patients, whereas age serves as a protective factor. The clinical predictive model developed in this research facilitates the early identification of high-risk patients for hv-CLNM, thereby assisting physicians in more efficacious risk stratification management for PTC patients.

Tumor deposits in thyroid carcinomas.

Tumor deposits (TDs) are defined as discontinuous neoplastic masses within the lymphatic drainage pathway of the primary tumor. The poor prognostic implication of these masses have been demonstrated in various cancers. The aim of this study is to investigate the incidence of TDs in our thyroid carcinoma cases, which has not been studied so far to the best of our knowledge, and to determine the prognostic value of their existence. In this retrospective cohort study, 194 thyroid carcinoma cases with cervical lymph node sampling and/or dissection were reevaluated for TDs. The case series consisted of 176 thyroid papillary carcinoma (TPC) and 18 thyroid medullary carcinoma (TMC) patients. TDs were detected in 54 (27.8%) patients. TMC cases (55.6%) had significantly more TDs compared to TPCs (25.0%; P = .006). TDs were more common in women (P = .045), and in multifocal tumors (P = .017). In addition, cases with TDs had larger tumor size (P = .002), more lymphatic invasion (P = .009), extrathyroidal extension (P < .001), and distant metastasis (P < .001). The mean follow-up period of the patients was 120.1 months (range, 4-341 months). Locoregional recurrence detected in 17 patients (8.8%) was more common in TMC (33.3%) than TPC cases (6.3%; P = .002). Distant metastasis was identified in 27 patients (13.9%). Ten-year recurrence free survival (RFS) and overall survival (OS) for all patients were 89.0% and 92.4%, respectively. Mean estimated OS time for TD negative and TD positive cases were: 281.9 (±17.2), 325.6 (±6.2) and 217.6 (±27.4) months, respectively (P = .002). Sex (P = .001), tumor type (P = .002), pT classification of the tumor (P < .001), perineural invasion (P = .002) and TDs (P = .002) were significantly associated with OS. In TPC cases individually, extrathyroidal extension (P = .001) and TDs (P = .002) were significantly correlated with distant metastasis. In multivariate analysis, only tumor size was detected as an independent prognostic marker in TPC cases (P = .005). Our results demonstrate the existence of TDs in thyroid carcinoma cases, and indicate a more aggressive behavior pattern of TDs in these tumors.

Prediction of the number of positive axillary lymph nodes according to sentinel lymph node involvement and biological subtypes in patients receiving neoadjuvant chemotherapy.

BACKGROUND: Sentinel lymph node biopsy (SLNB) has replaced axillary lymph node dissection (ALND) for assessing axillary lymph node status in clinically node-negative breast cancer patients. However, the approach to axillary surgery after neoadjuvant treatment is still controversial. In the present study, our objective was to predict the pathological nodal stage based on SLNB results and the clinicopathological characteristics of patients who initially presented with clinical N1 positivity but whose disease status was converted to clinical N0 after neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS: After NAC, 150 clinically node-negative patients were included. The relationships between clinicopathologic parameters and the number of positive lymph nodes in SLNBs and ALNDs were assessed through binary/multivariate logistic regression analysis. RESULTS: Among 150 patients, 78 patients had negative SLNBs, and 72 patients had positive SLNBs. According to the ALND data of 21 patients with SLNB1+, there was no additional node involvement (80.8%), 1-2 lymph nodes were positive in 5 patients (19.2%), and no patient had ≥ 3 lymph nodes involved. Following the detection of SLNB1 + positivity, the rate of negative non-sentinel nodes were 75% in the luminal A/B subgroup, 100% in the HER-2-positive subgroup, and 100% in the triple-negative subgroup. Patients with a lower T stage (T1-3 vs. T4), fewer than 4 clinical nodes before NAC (< 4 vs. ≥4), and a decreased postoperative Ki-67 index (< 10% vs. stable/increase) were included. According to both univariate and multivariate analyses, being in the triple-negative or HER2-positive subgroup, compared to the luminal A/B subgroup (luminal A/B vs. HER2-positive/triple-negative), was found to be predictive of complete lymph node response. CONCLUSION: The number of SLNB-positive nodes, tumor-related parameters, and response to treatment may predict no additional nodes to be positive at ALND.

Modelling the lymphatic metastatic progression pathways of OPSCC from multi-institutional datasets.

The elective clinical target volume (CTV-N) in oropharyngeal squamous cell carcinoma (OPSCC) is currently based mostly on the prevalence of lymph node metastases in different lymph node levels (LNLs) for a given primary tumor location. We present a probabilistic model for ipsilateral lymphatic spread that can quantify the microscopic nodal involvement risk based on an individual patient's T-category and clinical involvement of LNLs at diagnosis. We extend a previously published hidden Markov model (HMM), which models the LNLs (I, II, III, IV, V, and VII) as hidden binary random variables (RVs). Each represents a patient's true state of lymphatic involvement. Clinical involvement at diagnosis represents the observed binary RVs linked to the true state via sensitivity and specificity. The primary tumor and the hidden RVs are connected in a graph. Each edge represents the conditional probability of metastatic spread per abstract time-step, given disease at the edge's starting node. To learn these probabilities, we draw Markov chain Monte Carlo samples from the likelihood of a dataset (686 OPSCC patients) from three institutions. We compute the model evidence using thermodynamic integration for different graphs to determine which describes the data best.The graph maximizing the model evidence connects the tumor to each LNL and the LNLs I through V in order. It predicts the risk of occult disease in level IV is below 5% if level III is clinically negative, and that the risk of occult disease in level V is below 5% except for advanced T-category (T3 and T4) patients with clinical involvement of levels II, III, and IV. The provided statistical model of nodal involvement in OPSCC patients trained on multi-institutional data may guide the design of clinical trials on volume-deescalated treatment of OPSCC and contribute to more personal guidelines on elective nodal treatment.

Risk factors and development of machine learning diagnostic models for lateral lymph node metastasis in rectal cancer: multicentre study.

BACKGROUND: The diagnostic criteria for lateral lymph node metastasis in rectal cancer have not been established. This research aimed to investigate the risk factors for lateral lymph node metastasis and develop machine learning models combining these risk factors to improve the diagnostic performance of standard imaging. METHOD: This multicentre prospective study included patients who underwent lateral lymph node dissection without preoperative treatment for rectal cancer between 2017 and 2019 in 15 Japanese institutions. First, preoperative clinicopathological factors and magnetic resonance imaging findings were evaluated using multivariable analyses for their correlation with lateral lymph node metastasis. Next, machine learning diagnostic models for lateral lymph node metastasis were developed combining these risk factors. The models were tested in a training set and in an internal validation cohort and their diagnostic performance was tested using receiver operating characteristic curve analyses. RESULTS: Of 212 rectal cancers, 122 patients were selected, including 232 lateral pelvic sides, 30 sides of which had pathological lateral lymph node metastasis. Multivariable analysis revealed that poorly differentiated/mucinous adenocarcinoma, extramural vascular invasion, tumour deposit and a short-axis diameter of lateral lymph node ≥ 6.0 mm were independent risk factors for lateral lymph node metastasis. Patients were randomly divided into a training cohort (139 sides) and a test cohort (93 sides) and machine learning models were computed on the basis of a combination of significant features (including: histological type, extramural vascular invasion, tumour deposit, short- and long-axis diameter of lateral lymph node, body mass index, serum carcinoembryonic antigen level, cT, cN, cM, irregular border and mixed signal intensity). The top three models with the highest sensitivity in the training cohort were as follows: support vector machine (sensitivity, 1.000; specificity, 0.773), light gradient boosting machine (sensitivity, 0.950; specificity, 0.918) and ensemble learning (sensitivity, 0.950; specificity, 0.917). The diagnostic performances of these models in the test cohort were as follows: support vector machine (sensitivity, 0.750; specificity, 0.667), light gradient boosting machine (sensitivity, 0.500; specificity, 0.852) and ensemble learning (sensitivity, 0.667; specificity, 0.864). CONCLUSION: Machine learning models combining multiple risk factors can contribute to improving diagnostic performance of lateral lymph node metastasis.

Correlations of pathomorphological parameters between lesions at the invasive front and lymph node metastases in colorectal cancer: a retrospective clinical study.

BACKGROUND: Lymph node (LN) metastasis is one of the most important indicators to evaluate stage, choose treatment strategy, and predict outcome of colorectal cancer (CRC). The morphological correlation between primary tumors and LN metastases can help predict the incidence of LN metastasis in CRC more accurately and assist with more individualized risk-stratification management decisions. METHODS: A retrospective study was devised with paired tissue specimens from the invasive front of primary tumors and LN metastases in 426 patients after a radial surgery for CRC. According to the presence (N +) or absence (N-) of regional LN metastasis and the number of LN metastases (pN1a/1b/1c/2a/2b), comparisons were performed regarding tumor budding (TB) and poorly-differentiated clusters (PDC). In addition, their correlation with the incidence of LN metastasis and the extent were explored. RESULTS: The TB and PDC in the invasive front of primary tumors presented significant correlations with the incidence of LN metastasis and the number of LN metastases in CRC (P < 0.001). TB2/3 led to a risk of LN metastasis 6.68-fold higher than TB1, while PDC2/3 resulted in a risk of LN metastasis 8.46-fold higher than PDC1. Additionally, the risk of developing 4 or more LN metastases was 3.08-fold and 2.86-fold higher upon TB2/3 and PDC2/3 than that with TB1 and PDC1, respectively. Moderate positive correlations were found between the invasive front of primary tumors and LN metastases in terms of TB and PDC, respectively. CONCLUSIONS: TB and PDC, at the invasive tumor front are important morphological markers to evaluate LN metastasis in CRC, and they can be employed as reference indicators to assess or predict the potential of LN metastasis in CRC in clinical practice.

Diagnostic performance of Node Reporting and Data System (Node-RADS) for assessing mesorectal lymph node in rectal cancer by CT.

BACKGROUND: To compare the diagnostic performance of the Node-RADS scoring system and lymph node (LN) size in preoperative LN assessment for rectal cancer (RC), and to investigate whether the selection of size as the primary criterion whereas morphology as the secondary criterion for LNs can be considered the preferred method for clinical assessment. METHODS: Preoperative CT data of 146 RC patients treated with radical resection surgery were retrospectively analyzed. The Node-RADS score and short-axis diameter of size-prioritized LNs and the morphology-prioritized LNs were obtained. The correlations of Node-RADS score to the pN stage, LNM number and lymph node ratio (LNR) were investigated. The performances on assessing pathological lymph node metastasis were compared between Node-RADS score and short-axis diameter. A nomogram combined the Node-RADS score and clinical features was also evaluated. RESULTS: Node-RADS score showed significant correlation with pN stage, LNM number and LNR (Node-RADS of size-prioritized LN: r = 0.600, 0.592, and 0.606; Node-RADS of morphology-prioritized LN: r = 0.547, 0.538, and 0.527; Node-RADSmax: r = 0.612, 0.604, and 0.610; all p < 0.001). For size-prioritized LN, Node-RADS achieved an AUC of 0.826, significantly superior to short-axis diameter (0.826 vs. 0.743, p = 0.009). For morphology-prioritized LN, Node-RADS exhibited an AUC of 0.758, slightly better than short-axis diameter (0.758 vs. 0.718, p = 0.098). The Node-RADS score of size-prioritized LN was significantly better than that of morphology-prioritized LN (0.826 vs. 0.758, p = 0.038). The nomogram achieved the best diagnostic performance (AUC = 0.861) than all the other assessment methods (p < 0.05). CONCLUSIONS: The Node-RADS scoring system outperforms the short-axis diameter in predicting lymph node metastasis in RC. Size-prioritized LN demonstrates superior predictive efficacy compared to morphology-prioritized LN. The nomogram combined the Node-RADS score of size-prioritized LN with clinical features exhibits the best diagnostic performance. Moreover, a clear relationship was demonstrated between the Node-RADS score and the quantity-dependent pathological characteristics of LNM.

Deep learning-based pathological prediction of lymph node metastasis for patient with renal cell carcinoma from primary whole slide images.

BACKGROUND: Metastasis renal cell carcinoma (RCC) patients have extremely high mortality rate. A predictive model for RCC micrometastasis based on pathomics could be beneficial for clinicians to make treatment decisions. METHODS: A total of 895 formalin-fixed and paraffin-embedded whole slide images (WSIs) derived from three cohorts, including Shanghai General Hospital (SGH), Clinical Proteomic Tumor Analysis Consortium (CPTAC) and Cancer Genome Atlas (TCGA) cohorts, and another 588 frozen section WSIs from TCGA dataset were involved in the study. The deep learning-based strategy for predicting lymphatic metastasis was developed based on WSIs through clustering-constrained-attention multiple-instance learning method and verified among the three cohorts. The performance of the model was further verified in frozen-pathological sections. In addition, the model was also tested the prognosis prediction of patients with RCC in multi-source patient cohorts. RESULTS: The AUC of the lymphatic metastasis prediction performance was 0.836, 0.865 and 0.812 in TCGA, SGH and CPTAC cohorts, respectively. The performance on frozen section WSIs was with the AUC of 0.801. Patients with high deep learning-based prediction of lymph node metastasis values showed worse prognosis. CONCLUSIONS: In this study, we developed and verified a deep learning-based strategy for predicting lymphatic metastasis from primary RCC WSIs, which could be applied in frozen-pathological sections and act as a prognostic factor for RCC to distinguished patients with worse survival outcomes.

Significance of metastatic lymph nodes ratio in overall survival for patients with resected nonsmall cell lung cancer: a retrospective cohort study.

OBJECTIVE: The tumor, node and metastasis stage is widely applied to classify lung cancer and is the foundation of clinical decisions. However, increasing studies have pointed out that this staging system is not precise enough for the N status. In this study, we aim to build a convenient survival prediction model that incorporates the current items of lymph node status. METHODS: We performed a retrospective cohort study and collected the data from resectable nonsmall cell lung cancer (NSCLC) (IA-IIIB) patients from the Surveillance, Epidemiology, and End Results database (2006-2015). The x-tile program was applied to calculate the optimal threshold of metastatic lymph node ratio (MLNR). Then, independent prognostic factors were determined by multivariable Cox regression analysis and enrolled to build a nomogram model. The calibration curve as well as the Concordance Index (C-index) were selected to evaluate the nomogram. Finally, patients were grouped based on their specified risk points and divided into three risk levels. The prognostic value of MLNR and examined lymph node numbers (ELNs) were presented in subgroups. RESULTS TOTALLY,: 40853 NSCLC patients after surgery were finally enrolled and analyzed. Age, metastatic lymph node ratio, histology type, adjuvant treatment and American Joint Committee on Cancer 8th T stage were deemed as independent prognostic parameters after multivariable Cox regression analysis. A nomogram was built using those variables, and its efficiency in predicting patients' survival was better than the conventional American Joint Committee on Cancer stage system after evaluation. Our new model has a significantly higher concordance Index (C-index) (training set, 0.683 v 0.641, respectively; P < 0.01; testing set, 0.676 v 0.638, respectively; P < 0.05). Similarly, the calibration curve shows the nomogram was in better accordance with the actual observations in both cohorts. Then, after risk stratification, we found that MLNR is more reliable than ELNs in predicting overall survival. CONCLUSION: We developed a nomogram model for NSCLC patients after surgery. This novel and useful tool outperforms the widely used tumor, node and metastasis staging system and could benefit clinicians in treatment options and cancer control.