Predictors Of Lymph Node Involvement Research Articles

Integrating PET/CT, radiomics and clinical data: An advanced multi-modal approach for lymph node metastasis prediction in prostate cancer

The involvement of lymph nodes critically affects patient outcomes in prostate cancer. While traditional risk models use factors like stage and PSA levels, the detection of lymph node involvement through modalities like PET targeting PSMA with 68Ga radiotracer plays a pivotal role in guiding treatments ranging from prostatectomy to pelvic radiotherapy. This study aims to create a deep learning model to predict lymph node involvement in intermediate to high-risk prostate cancer patients using 68Ga-PSMA PET/CT imagery, radiomics features, and various clinical parameters. For this study, 68Ga-PSMA PET/CT scans and corresponding clinical data from 229 prostate cancer patients were retrospectively collected. An artificial intelligence model, integrating PET/CT fusion images, clinical data, and radiomics features, was developed using a slice-wise feature extractor and MNASNet for spatial feature extraction. The model was trained on 181 cases and tested on 48 cases. To assess the model’s performance, a reader study was conducted on a balanced subset of the test data with five radiation oncologists. Among the 229 intermediate to high-risk patients with localized prostate cancer evaluated, 67 (30%) had lymph node metastasis, while 162 were non-metastatic. The proposed AI model achieved a mean accuracy of 0.85±0.03 and an F1 score of 0.73±0.03. In the reader study, radiation oncologists’ mean evaluations showed lower metrics (accuracy 0.71±0.08, F1 score 0.70±0.07), compared to the AI model’s mean accuracy of 0.79±0.02 and F1 score of 0.76±0.02. Our findings demonstrate the potential benefits of the proposed model in the clinical setting, particularly in enhancing decision-making by doctors in scenarios with high variability between readers, such as lymph node metastasis prediction.

NODESAFE Nomogram: A Novel Score System to Predict Lymph Node Involvement at the Time of Nephrectomy or Nodal Recurrence in Nonmetastatic Renal Cell Carcinoma

ObjectiveWe sought to develop a preoperative nomogram called NODESAFE (NODE SAFEty) to predict nodal involvement (NI) at time of surgery or subsequent follow up in localized renal cell carcinoma (RCC), as the role of lymphadenectomy in localized RCC remains controversial. MethodsWe conducted a multicenter retrospective analysis of RCC patients who underwent primary surgical resection. Patients with clinical metastasis at presentation were excluded. NI was defined as presence of histological RCC with lymphadenectomy at time of surgery, or subsequent development histologically proven NI. The dataset was divided into training (70%) and testing subsets to facilitate model evaluation which was constructed through a stepwise multivariable logistic regression (MLR) model. Accuracy was tested with receiver operator characteristic estimated area under the curve (AUC). ResultsTotal 3308 patients (2221 [67.1%] male) met inclusion criteria. During follow-up 25 patients (0.76 %) experienced nodal recurrence, and 22/25 were preoperatively classified as cN0. In our cohort, 112 (3.4%) patients had clinical lymphadenopathy preoperatively (cN1), and 34/112 were pN1. The following covariates were found to be statically significant on a MLR model: hypertension (Odds ratio [OR] 3.35, < .001), Charlson Comorbidity Index ≥ 5 (OR 1.93 P = .025), tumor size ≥ 6 cm (OR 2.63, P = .001), tumor necrosis at CT scan (OR 1.83, P = .036), cN1 (OR 5.59, P < .001) and CRP ≥ 8.5 mg/L (1.96, P = .018). Testing the prediction performance of the model in the validation set AUC of the model was 0.89. NODESAFE demonstrated a sensitivity of 83.9%, specificity of 86.1% and 99.1% negative predictive values using a 4% threshold probability. ConclusionCombining clinical features, serum biomarkers and radiographic findings, we developed a model capable of predicting NI with high degree of accuracy. NODESAFE may refine clinical decision making with respect to the performance of lymphadenectomy at the time of surgery, postsurgical surveillance, and spur consideration for adjuvant therapy.

Comparison between Three Radiomics Models and Clinical Nomograms for Prediction of Lymph Node Involvement in PCa Patients Combining Clinical and Radiomic Features.

We aim to compare the performance of three different radiomics models (logistic regression (LR), random forest (RF), and support vector machine (SVM)) and clinical nomograms (Briganti, MSKCC, Yale, and Roach) for predicting lymph node involvement (LNI) in prostate cancer (PCa) patients. The retrospective study includes 95 patients who underwent mp-MRI and radical prostatectomy for PCa with pelvic lymphadenectomy. Imaging data (intensity in T2, DWI, ADC, and PIRADS), clinical data (age and pre-MRI PSA), histological data (Gleason score, TNM staging, histological type, capsule invasion, seminal vesicle invasion, and neurovascular bundle involvement), and clinical nomograms (Yale, Roach, MSKCC, and Briganti) were collected for each patient. Manual segmentation of the index lesions was performed for each patient using an open-source program (3D SLICER). Radiomic features were extracted for each segmentation using the Pyradiomics library for each sequence (T2, DWI, and ADC). The features were then selected and used to train and test three different radiomics models (LR, RF, and SVM) independently using ChatGPT software (v 4o). The coefficient value of each feature was calculated (significant value for coefficient ≥ ±0.5). The predictive performance of the radiomics models and clinical nomograms was assessed using accuracy and area under the curve (AUC) (significant value for p ≤ 0.05). Thus, the diagnostic accuracy between the radiomics and clinical models were compared. This study identified 343 features per patient (330 radiomics features and 13 clinical features). The most significant features were T2_nodulofirstordervariance and T2_nodulofirstorderkurtosis. The highest predictive performance was achieved by the RF model with DWI (accuracy 86%, AUC 0.89) and ADC (accuracy 89%, AUC 0.67). Clinical nomograms demonstrated satisfactory but lower predictive performance compared to the RF model in the DWI sequences. Among the prediction models developed using integrated data (radiomics and semantics), RF shows slightly higher diagnostic accuracy in terms of AUC compared to clinical nomograms in PCa lymph node involvement prediction.

Her-2/neu gene amplification and its correlation with other prognostic factors in Iranian breast cancer patients.

Breast cancer is one of the most common diseases in females, arising from overexpression of a variety of oncogenes like HER2/neu. The amplification rate of this gene is variable in different breast cancer patients. In this study, the amplification of the HER2/neu oncogene was distinguished in breast cancer patients and its correlation with prognostic factors. Also, the simultaneous effect of prognostic factors on the occurrence of a specific prognostic factor was investigated. The multiplex PCR technique was used to assay the amplification of the HER2/neu oncogene in breast cancer patients. After extracting DNA from 100 tumor tissue and 8 normal breast tissue samples, the amplification of the HER2/neu gene was distinguished by the co-amplification of a single-copy reference gene, γ-IFN, and the target gene HER2/neu in the PCR reaction and using the Gel analyzer software. SPSS 23 and STATA 9.1 software were used for statistical analysis. The HER2/neu gene was amplification in 30% of the tumor samples. The statistical analysis showed a statistically significant relationship between HER2/neu gene amplification and progesterone receptors. Amplification of the HER2/neu gene significantly increases the chance of lymph node involvement. Also, the amplification of this gene in tumors with histological grade II tissue is more than grade I. The amplification of the HER2/neu gene can be used as an independent prognostic factor in predicting lymph node involvement and histological grade in breast cancer patients.

Preoperative assessment of lymph nodal metastases with [68Ga]Ga-DOTATOC PET radiomics for improved surgical planning in well-differentiated pancreatic neuroendocrine tumours.

Accurate identification of lymph node (LN) metastases is pivotal for surgical planning of pancreatic neuroendocrine tumours (PanNETs); however, current imaging techniques have sub-optimal diagnostic sensitivity. Aim of this study is to investigate whether [68Ga]Ga-DOTATOC PET radiomics might improve the identification of LN metastases in patients with non-functioning PanNET (NF-PanNET) referred to surgical intervention. Seventy-two patients who performed preoperative [68Ga]Ga-DOTATOC PET between December 2017 and March 2022 for NF-PanNET. [68Ga]Ga-DOTATOC PET qualitative assessment of LN metastases was measured using diagnostic balanced accuracy (bACC), sensitivity (SN), specificity (SP), positive and negative predictive values (PPV, NPV). SUVmax, SUVmean, Somatostatin receptor density (SRD), total lesion SRD (TLSRD) and IBSI-compliant radiomic features (RFs) were obtained from the primary tumours. To predict LN involvement, these parameters were engineered, selected and used to train different machine learning models. Models were validated using tenfold repeated cross-validation and control models were developed. Models' bACC, SN, SP, PPV and NPV were collected and compared (Kruskal-Wallis, Mann-Whitney). LN metastases were detected in 29/72 patients at histology. [68Ga]Ga-DOTATOC PET qualitative examination of LN involvement provided bACC = 60%, SN = 24%, SP = 95%, PPV = 78% and NPV = 65%. The best-performing radiomic model provided a bACC = 70%, SN = 77%, SP = 61%, PPV = 60% and NPV = 83% (outperforming the control model, p < 0.05*). In this study, [68Ga]Ga-DOTATOC PET radiomics allowed to increase diagnostic sensitivity in detecting LN metastases from 24 to 77% in NF-PanNET patients candidate to surgery. Especially in case of micrometastatic involvement, this approach might assist clinicians in a better patients' stratification.

ASO Author Reflections: Optimizing Therapy Strategies for Early Gastric Cancer by Leveraging Innovative Predictors of Lymph Node Involvement.

ASO Author Reflections: Optimizing Therapy Strategies for Early Gastric Cancer by Leveraging Innovative Predictors of Lymph Node Involvement.

The Key Role of Tumor Budding in Predicting the Status of Lymph Node Involvement in Early Gastric Cancer Patients: A Clinical Multicenter Validation in China.

Accurate preoperative prediction of lymph node (LN) involvement is essential for the management of early gastric cancer (EGC). Our objective was to formulate a potent nomogram for predicting LN involvement in EGC by leveraging an innovative predictor of tumor budding. We assembled a cohort of EGC patients who underwent radical surgery at two tertiary cancer centers. Tumor budding was stratified by using an optimal cutoff value and integrated with other clinicopathological variables to ascertain the risk factors associated with LN involvement. A nomogram was developed and its predictive performance was assessed by using receiver operating characteristic (ROC) curves and calibration plots. In addition, we conducted decision curve analysis to evaluate its clinical utility. Finally, an external validation was conducted by using an independent cohort. Finally, 307 eligible patients (215 in the primary cohort and 92 in the validation cohort) were included. Tumor budding, categorized by a count of two, exhibited a robust association with LN involvement (OR 14.12, p = 0.012). Other significant risk factors include lymphovascular invasion, depth of tumor invasion, ulceration, and tumor differentiation. Notably, the nomogram demonstrated exceptional discriminative power (area under the ROC curve, 0.872 in the primary cohort and 0.885 in the validation cohort) and precise predictive capabilities. Furthermore, the nomogram showed notable clinical applicability through decision curve analysis, particularly in endoscopic curability C-2, by mitigating the risk of overtreatment. Tumor budding is a robust predictor of LN involvement in EGC. The incorporation of tumor budding into a nomogram is an effective strategy, thereby informing and enhancing clinical decision-making.

Predictive nomogram for lymph node metastasis and survival in gastric cancer using contrast-enhanced computed tomography-based radiomics: a retrospective study.

Lymph node involvement significantly impacts the survival of gastric cancer patients and is a crucial factor in determining the appropriate treatment. This study aimed to evaluate the potential of enhanced computed tomography (CT)-based radiomics in predicting lymph node metastasis (LNM) and survival in patients with gastric cancer before surgery. Retrospective analysis of clinical data from 192 patients diagnosed with gastric carcinoma was conducted. The patients were randomly divided into a training cohort (n=128) and a validation cohort (n=64). Radiomic features of CT images were extracted using the Pyradiomics software platform, and distinctive features were further selected using a Lasso Cox regression model. Features significantly associated with LNM were identified through univariate and multivariate analyses and combined with radiomic scores to create a nomogram model for predicting lymph node involvement before surgery. The predictive performance of radiomics features, CT-reported lymph node status, and the nomogram model for LNM were compared in the training and validation cohorts by plotting receiver operating characteristic (ROC) curves. High-risk and low-risk groups were identified in both cohorts based on the cut-off value of 0.582 within the radiomics evaluation scheme, and survival rates were compared. Seven radiomic features were identified and selected, and patients were stratified into high-risk and low-risk groups using a 0.582 cut-off radiomics score. Univariate and multivariate analyses revealed that radiomics features, diabetes mellitus, Nutrition Risk Screening (NRS) 2002 score, and CT-reported lymph node status were significant predictors of LNM in patients with gastric cancer. A predictive nomogram model was developed by combining these predictors with the radiomics score, which accurately predicted LNM in gastric cancer patients before surgery and outperformed other models in terms of accuracy and sensitivity. The AUC values for the training and validation cohorts were 0.82 and 0.722, respectively. The high-risk and low-risk groups in both the training and validation cohorts showed significant differences in survival rates. The radiomics nomogram, based on contrast-enhanced computed tomography (CECT ), is a promising non-invasive tool for preoperatively predicting LNM in gastric cancer patients and postoperative survival.

Preoperative Albumin-to-Alkaline Phosphatase Ratio as an Independent Predictor of Lymph Node Involvement in Penile Cancer.

Background and Objectives: To investigate the role of preoperative albumin-to-alkaline phosphatase ratio (AAPR) in predicting pathologic node-positive (pN+) disease in penile cancer (PC) patients undergoing inguinal lymph node dissection (ILND). Materials and Methods: Clinical data of patients with squamous cell carcinoma (SCC) PC + ILND at a single high-volume institution between 2016 and 2021 were collected and retrospectively analyzed. An AAPR was obtained from preoperative blood analyses performed within 30 days from their scheduled surgery. A ROC curve analysis was used to assess AAPR cutoff, in addition to the Youden Index. Logistic regression analysis was utilized for an odds ratio (OR), 95% confidence interval (CI) calculations, and an estimate of pN+ disease. A p value < 0.05 was considered to be as statistically significant. Results: Overall, 42 PC patients were included in the study, with a mean age of 63.6 ± 12.9 years. The AAPR cut-off point value was determined to be 0.53. The ROC curve analysis reported an AUC of 0.698. On multivariable logistic regression analysis lymphovascular invasion (OR = 5.38; 95% CI: 1.47-9.93, p = 0.022), clinical node-positive disease (OR = 13.68; 95% CI: 4.37-43.90, p < 0.009), and albumin-to-alkaline phosphatase ratio ≤ 0.53 (OR = 3.61; 95% CI: 1.23-12.71, p = 0.032) were predictors of pN+ involvement. Conclusions: Preoperative AAPR may be a potentially valuable prognostic marker of pN+ disease in patients who underwent surgery for PC.

The 2012 Briganti nomogram not only predicts lymph node involvement but also disease progression in surgically treated intermediate-risk prostate cancer patients with PSA

We assessed the prognostic impact of the 2012 Briganti nomogram on prostate cancer (PCa) progression in intermediate-risk (IR) patients presenting with PSA <10ng/mL, ISUP grade group 3, and clinical stage up to cT2b treated with robot assisted radical prostatectomy eventually associated with extended pelvic lymph node dissection. From January 2013 to December 2021, data of surgically treated IR PCa patients were retrospectively evaluated. Only patients presenting with the above-mentioned features were considered. The 2012 Briganti nomogram was assessed either as a continuous and a categorical variable (up to the median, which was detected as 6%, vs. above the median). The association with PCa progression, defined as biochemical recurrence, and/or metastatic progression, was evaluated by Cox proportional hazard regression models. Overall, 147 patients were included. Compared to subjects with a nomogram score up to 6%, those presenting with a score above 6% were more likely to be younger, had larger/palpable tumors, presented with higher PSA, underwent tumor upgrading, harbored non-organ confined disease, and had positive surgical margins at final pathology. PCa progression, which occurred in 32 (21.7%) cases, was independently predicted by the 2012 Briganti nomogram both considered as a continuous (Hazard Ratio [HR]:1.04, 95% Confidence Interval [CI]:1.01-1.08;p=0.021), and a categorical variable (HR:2.32; 95%CI:1.11-4.87;p=0.026), even after adjustment for tumor upgrading. In IR PCa patients with PSA <10ng/mL, ISUP grade group 3, and clinical stage up to cT2b, the 2012 Briganti nomogram independently predicts PCa progression. In this challenging subset of patients, this tool can identify prognostic subgroups, independently by upgrading issues.

Advancing Tailored Treatments: A Predictive Nomogram, Based on Ultrasound and Laboratory Data, for Assessing Nodal Involvement in Endometrial Cancer Patients.

Assessing lymph node metastasis is crucial in determining the optimal therapeutic approach for endometrial cancer (EC). Considering the impact of lymphadenectomy, there is an urgent need for a cost-effective and easily applicable method to evaluate the risk of lymph node metastasis in cases of sentinel lymph node (SLN) biopsy failure. This retrospective monocentric study enrolled EC patients, who underwent surgical staging with nodal assessment. Data concerning demographic, clinicopathological, ultrasound, and surgical characteristics were collected from medical records. Ultrasound examinations were conducted in accordance with the IETA statement. We identified 425 patients, and, after applying exclusion criteria, the analysis included 313 women. Parameters incorporated into the nomogram were selected via univariate and multivariable analyses, including platelet count, myometrial infiltration, minimal tumor-free margin, and CA 125. The nomogram exhibited good accuracy in predicting lymph node involvement, with an AUC of 0.88. Using a cutoff of 10% likelihood of nodal involvement, the nomogram displayed a low false-negative rate of 0.04 (95% CI 0.00-0.19) in the training set. The adaptability of this straightforward model renders it suitable for implementation across diverse clinical settings, aiding gynecological oncologists in preoperative patient evaluations and facilitating the design of personalized treatments. However, external validation is mandatory for confirming diagnostic accuracy.

Diffusion-Weighted MRI as a Quantitative Imaging Biomarker in Colon Tumors.

To assess the use of quantitative diffusion-weighted MRI (DW-MRI) as a diagnostic imaging biomarker in differentiating between benign colon adenoma, early, and advanced cancer of the colon, as well as predicting lymph node involvement, and finally comparing mucinous-producing colon cancer with adenomas and non-mucinous colon cancer. Patients with a confirmed tumor on colonoscopy were eligible for inclusion in this study. Using a 3.0 Tesla MRI machine, the main tumor mean apparent diffusion coefficient (mADC) was obtained. Surgically resected tumor specimens served as an endpoint, except in mucinous colon cancers, which were classified based on T2 images. A total of 152 patients were included in the study population. The mean age was 71 years. A statistically significant mADC mean difference of -282 × 10-6 mm2/s [-419--144 95% CI, p < 0.001] was found between colon adenomas and early colon cancer, with an AUC of 0.80 [0.68-0.93 95% CI] and an optimal cut off value of 1018 × 10-6 mm2/s. Only a small statistically significant difference (p = 0.039) in mADC was found between benign tumors and mucinous colon cancer. We found no statistical difference in mADC mean values between early and advanced colon cancer, and between colon cancer with and without lymph node involvement. Quantitative DW-MRI is potentially useful for determining whether a colonic tumor is benign or malignant. Mucinous colon cancer shows less diffusion restriction when compared to non-mucinous colon cancer, a potential pitfall.

Can Machine Learning Models Detect and Predict Lymph Node Involvement in Prostate Cancer? A Comprehensive Systematic Review.

(1) Background: Recently, Artificial Intelligence (AI)-based models have been investigated for lymph node involvement (LNI) detection and prediction in Prostate cancer (PCa) patients, in order to reduce surgical risks and improve patient outcomes. This review aims to gather and analyze the few studies available in the literature to examine their initial findings. (2) Methods: Two reviewers conducted independently a search of MEDLINE databases, identifying articles exploring AI's role in PCa LNI. Sixteen studies were selected, and their methodological quality was appraised using the Radiomics Quality Score. (3) Results: AI models in Magnetic Resonance Imaging (MRI)-based studies exhibited comparable LNI prediction accuracy to standard nomograms. Computed Tomography (CT)-based and Positron Emission Tomography (PET)-CT models demonstrated high diagnostic and prognostic results. (4) Conclusions: AI models showed promising results in LN metastasis prediction and detection in PCa patients. Limitations of the reviewed studies encompass retrospective design, non-standardization, manual segmentation, and limited studies and participants. Further research is crucial to enhance AI tools' effectiveness in this area.

Preoperative assessment of grade, T stage, and lymph node involvement: machine learning-based CT texture analysis in colon cancer.

To investigate whether texture analysis of primary colonic mass in preoperative abdominal computed tomography (CT) scans of patients diagnosed with colon cancer could predict tumor grade, T stage, and lymph node involvement using machine learning (ML) algorithms. This retrospective study included 73 patients diagnosed with colon cancer. Texture features were extracted from contrast-enhanced CT images using LifeX software. First, feature reduction was performed by two radiologists through reproducibility analysis. Using the analysis of variance method, the parameters that best predicted lymph node involvement, grade, and T stage were determined. The predictive performance of these parameters was assessed using Orange software with the k-nearest neighbor (kNN), random forest, gradient boosting, and neural network models, and their area under the curve values were calculated. There was excellent reproducibility between the two radiologists in terms of 49 of the 58 texture parameters that were subsequently subject to further analysis. Considering all four ML algorithms, the mean AUC and accuracy ranges were 0.557-0.800 and 47-76%, respectively, for the prediction of lymph node involvement; 0.666-0.846 and 68-77%, respectively, for the prediction of grade; and 0.768-0.962 and 81-88%, respectively, for the prediction of T stage. The best performance was achieved with the random forest model in the prediction of LN involvement, the kNN model for the prediction of grade, and the gradient boosting model for the prediction of T stage. The results of this study suggest that the texture analysis of preoperative CT scans obtained for staging purposes in colon cancer can predict the presence of advanced-stage tumors, high tumor grade, and lymph node involvement with moderate specificity and sensitivity rates when evaluated using ML models.

The Prognostic Impact of Substantial Lymphovascular Space Invasion in Women with FIGO Stage I Uterine Endometrioid Carcinoma with Pathologically Negative Nodal Evaluation

Substantiallymphovascular space invasion (LVSI) is an important predictor of lymph node involvement in women with endometrial carcinoma. However, its prognostic significance in women with stage I who had pathologic negative nodal evaluation (PNNE) was not fully evaluated. We aimed to evaluate the prognostic significance of substantial LVSI on recurrence-free (RFS), disease-specific (DSS) and overall survival (OS) in women with FIGO stage I uterine endometrioid adenocarcinoma (EC). Our uterine cancer database was queried for women with stage I EC who had a hysterectomy and PNNE at our institution between 1/1990 and 11/2022. Postoperatively, patients were managed with observation or adjuvant radiation therapy (RT) with pelvic external beam RT or vaginal cuff brachytherapy (VB). Women with synchronous malignancies and those who received adjuvant chemotherapy were excluded. Pathologic specimens were retrieved and LVSI was quantified by Gynecology pathologists (none, focal or substantial). Patients' demographics, surgical and pathologic variables were analyzed. Predictors of RFS, DSS and OS using univariate (UVA) and multivariate analysis (MVA) were studied. One thousand fifty-two patients were identified with a median age of 63 years and median follow-up of 9.7 years. Median number of examined lymph node (LN) were 9 (range 4-18). 907 patients (86.2%) had no LVSI, 87 (8.3%) had focal and 58 (5.5%) had substantial LVSI. In patients with focal LVSI, 32.2% received pelvic RT and 39.1% received VB. In patients with substantial LVSI, 20.7% received pelvic RT and 58.6% received VB.Recurrence was diagnosed in 86 patients (8.2%). While any LVSI was associated with tumor recurrence, there was no significant difference for the site of initial recurrence between patients with focal vs. substantial LVSI. 5-year RFS was 93.3% (95% CI 91.5-95.1), 76.8% (67.2-87.7) and 79.1% (67.6-95.3) for no, focal and substantial LVSI. The 5-year DSS was 97.6% (96.5-98.7), 83.5% (75-93.1), and 90% (81.8-99.9); and 5-year OS was 90.7% (88.7-92.8), 72.8% (62.9-84.2) and 86% (76.2-97.2), respectively. Independent predictors of worse 5-year RFS and DSS include any LVSI, age > 60 years, higher tumor grade. Independent predictors of worse 5- year OS include any LVSI, age > 60, high comorbidity burden, and higher tumor grade. Our large data suggest similar recurrence-free, disease specific and OS for women with stage I uterine endometrioid carcinoma who had pathologically negative nodal evaluation and substantial or focal LVSI. There was no significant difference for the site of initial recurrence between patients with focal or substantial LVSI. Multi-institutional pooled analyses may be needed to validate our results.

Features of very thin melanoma that predict lymph node involvement.

Features of very thin melanoma that predict lymph node involvement.

A nomogram for predicting lymph node metastasis in early gastric signet ring cell carcinoma

At present, the risk factors for lymph node metastasis in early gastric signet ring cell carcinoma (SRCC) remain unclear. However, it is worth noting that the LNM rate and prognosis of early gastric SRCC are superior to those of other undifferentiated cancers. With advancements in endoscopic technology, the 5-year survival rate following endoscopic treatment of early gastric cancer is comparable to traditional surgery while offering a better quality of life. The objective of this study was to develop a nomogram that can predict lymph node status in early gastric SRCC before surgery, aiding clinicians in selecting the optimal treatment strategy. A research cohort was established by retrospectively collecting data from 183 patients with early gastric SRCC who underwent radical gastrectomy with lymph node dissection at our hospital between January 2014 and June 2022. The predictors of early gastric signet ring cell carcinoma lymph node metastasis were identified in the study cohort using the least absolute selection and shrinkage operator (Lasso) and multivariate regression analysis, and a nomogram was developed. The discrimination, accuracy, and clinical practicability of the nomogram were assessed using receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis. The incidence of lymph node metastasis was 21.9% (40/183) overall. Multivariate logistic regression analysis revealed that tumor size and lymphovascular invasion (LVI) were independent risk factors for lymph node metastasis. Lasso regression analysis demonstrated that tumor size, invasion depth, LVI, E-cadherin expression, dMMR, CA242, NLR, and macroscopic type were associated with lymph node metastasis. The integrated discrimination improvement (IDI) (P = 0.034) and net reclassification index (NRI) (P = 0.023) were significantly improved when dMMR was added to model 1. In addition, the area under curve (AUC) (P = 0.010), IDI (P = 0.001) and NRI (P < 0.001) of the model were significantly improved when type_1 was included. Therefore, we finally included tumor size, invasion depth, dMMR, and macroscopic type to establish a nomogram, which had good discrimination (AUC = 0.757, 95% CI 0.687–0.828) and calibration. Decision curve analysis showed that the nomogram had good clinical performance. We have developed a risk prediction model for early gastric signet ring cell carcinoma that accurately predicts lymph node involvement, providing clinicians with a valuable tool to aid in patient counseling and treatment decision-making.

Lymph node size detected by magnetic resonance imaging for prediction of lymph node involvement in locally advanced cervical cancer.

To investigate lymph node (LN) size detected by Magnetic Resonance Imaging (MRI) for prediction of LN involvement in locally advanced cervical cancer (LACC). A total of 55 cases diagnosed with LACC (IIB-IVA FIGO 2018) between 28 December 2010 and 30 October 2020 were evaluated in this retrospective study. LN involvement was evaluated in patients who underwent surgical staging and MRI. The prediction of LN involvement based on LN size on MRI was calculated statistically. The mean age of 55 patients was 56 (33-78) years. For the cases, 76.4% were stage IIB, 12.7% were stage III, and 10.9% were stage IVA. Squamous cell histological type was detected with a rate of 90.9%. The cut-off value for pelvic LN diameter on MRI in predicting pelvic LN involvement was 18.5 mm, and the sensitivity and specificity values were calculated as 50.0% and 93.6%, respectively (p=0.027). The cut-off value for pelvic and para-aortic LN diameter on MRI in predicting pelvic and para-aortic LN involvement was 17.0 mm with a sensitivity of 41.7% and specificity of 88.4% (p=0.081). In LACC, prediction of LN involvement by LN diameter detected on MRI has moderate sensitivity and high specificity with a cut-off value of 17.0 mm.

Management of malignant T1 colorectal cancer polyps: results from a 10-year prospective observational study.

The recurrence risk associated with residual malignant cells (bowel wall/regional nodes) following T1 colorectal cancer (CRC) polypectomy must be weighed against operative morbidity. Our aim was to describe the management and outcomes of a large prospective cohort of T1 CRCs. All T1 CRCs diagnosed between March 2007 and March 2017 at the Glasgow Royal Infirmary were included. Patients were grouped by polypectomy, rectal local excision and formal resection status. χ2 testing, multivariate binary logistic and Cox regression were performed. Of 236 patients, 90 (38.1%) underwent polypectomy only, six (2.6%) polypectomy and then rectal excision, 57 (24.2%) polypectomy and then resection, 14 (5.9%) rectal excision only and 69 (29.2%) primary resection. Polypectomy only correlated with male sex (P = 0.028), older age (P < 0.001), distal CRCs (P < 0.001) and pedunculated polyps (P < 0.001); primary resection with larger polyps (P < 0.001); polypectomy then resection with piecemeal excision (P = 0.002) and involved polypectomy margin (P < 0.001). Poor differentiation (OR 7.860, 95% CI 1.117-55.328; P = 0.038) independently predicted lymph node involvement. Submucosal venous invasion (hazard ratio [HR] 10.154, 95% CI 2.087-49.396; P = 0.004) and mucinous subtype (HR 7.779, 95% CI 1.566-38.625; P = 0.012) independently predicted recurrence. Submucosal venous invasion (HR 5.792, 95% CI 1.056-31.754; P = 0.043) predicted CRC-specific survival. Although 64.4% of polypectomy-only patients had margin involvement/other risk factors, none developed recurrence. Of 94 with polypectomy margin involvement, five (5.3%) had confirmed residual tumour. Overall, lymph node metastases (7.1%), recurrence (4.2%) and cancer-specific mortality (3.0%) were rare. Cancer-specific 5-year survival was high: polypectomy only (100%), polypectomy and then resection (98.2%), primary resection (100%). Surveillance may be safe for more T1 CRC polyp patients. Multidisciplinary team discussion and informed patient choice are critical.

The Impact of Tumor Size on Node Involvement in Typical Lung Carcinoids

BackgroundObservation has been suggested as an alternative to surgical resection for small typical lung carcinoids. We sought to evaluate the potential impact of tumor growth and lymphatic spread during observation by examining predictors of node positivity and the impact of tumor size and node status on survival. MethodsNational Cancer Database cases of typical lung carcinoid resections from 2006 to 2016 were analyzed. Predictors of lymph node involvement and survival were determined. ResultsOverall, 1019 of 8257 patients who underwent typical carcinoid resection had at least 1 positive node (12.3%). The incidence of node positivity among the 921 patients with subcentimeter tumors was 5.4% (n = 50). Increasing tumor size was independently associated with nodal involvement. Patients with nodal involvement had significantly worse 5-year survival (89.5% vs 94.0%; P < .001). Increasing tumor size was not associated with worse survival in multivariable analysis, but node positivity did independently predict worse survival. ConclusionsMore than 5% of patients with subcentimeter typical carcinoids of the lung have nodal metastases, and node involvement is an independent predictor of worse survival, whereas tumor size is not. These data suggest that even patients with small tumors should generally undergo resection when diagnosed.