Prediction Of Node Status Research Articles

Diagnostic artificial intelligence model predicts lymph node status in non-small cell lung cancer using simplified examination.

Artificial intelligence (AI) technology was introduced in medical data area and applied disease prediction models. This study aimed to establish an AI model for predicting lymph node metastasis based on simple medical examinations in patients with non-small cell lung cancer (NSCLC). We retrospectively analyzed 988 patients with NSCLC who underwent radical pulmonary resection with mediastinal lymph node dissection between January 2011 and October 2022. We collected clinical characteristics including age, sex, smoking history, tumor marker levels, tumor side, segment location, total tumor size, solid tumor size and consolidation-to-tumor ratio, obtainable from medical interview, blood tests and plain computed tomography (CT) of the chest. All patients were randomly classified into a training set (n=790) and a validation set (n=198). Six algorithms including Support Vector Classification (SVC), k-nearest neighbor algorithm (k-NN), logistic regression (LR), random forest (RF), gradient boosting (GB) and multilayer perceptron (MLP) were created to decide the lymph node metastasis. The GB model showed the best diagnostic performance, with 80.0% accuracy, 95.6% specificity and an area under the curve (AUC) of 0.75. An AI model showed high specificity and accuracy for predicting lymph node metastasis. These models have potential to categorize suitable surgical procedures for NSCLC patients without needing contrast-enhanced CT or positron emission tomography.

Sentinel node mapping decreases the risk of failed detection of isolated positive para-aortic lymph node in endometrial cancer

BackgroundIsolated positive para-aortic lymph node metastasis in endometrial cancer is an uncommon event, ranging from 1% to 3%.ObjectiveOur aim was to evaluate the impact of sentinel lymph node (SLN) mapping...

Predicting axillary lymph node metastasis in breast cancer based on ultrasound radiofrequency time-series analysis.

The status of axillary lymph nodes (ALN) plays a critical role in the management of patients with breast cancer. It is an urgent demand to develop highly accurate, non-invasive methods for predicting ALN status. To evaluate the efficacy of ultrasound radiofrequency (URF) time-series parameters, in combination with clinical data, in predicting ALN metastasis in patients with breast cancer. We prospectively gathered clinicopathologic and ultrasonic data from patients diagnosed with breast cancer. Various machine-learning (ML) models were developed using all available features to determine the most efficient diagnostic model. Subsequently, distinct prediction models were created using the optimal ML model, and their diagnostic performances were evaluated and compared. The study encompassed 240 patients, of whom 88 had lymph node metastases. A leave-one-out cross-validation (LOOCV) method was used to split the entire dataset into training and testing subsets. The random forest ML model outperformed the other algorithms, with an area under the curve (AUC) of 0.92. Prediction models based on clinical, ultrasonic, URF parameters, clinical + ultrasonic, clinical + URF, and ultrasonic + URF parameters had AUCs of 0.56, 0.79, 0.78, 0.90, 0.80, and 0.84, respectively, in the testing set. The comprehensive diagnostic model (clinical + ultrasonic + URF parameters) demonstrated strong diagnostic capability, with an AUC of 0.94 in the testing set, exceeding any single prediction model. The combined model (clinical + ultrasonic + URF parameters) could be used preoperatively to predict lymph node status, offering valuable input for the design of individualized surgical approaches.

A transformer-based deep learning model for early prediction of lymph node metastasis in locally advanced gastric cancer after neoadjuvant chemotherapy using pretreatment CT images

Early prediction of lymph node status after neoadjuvant chemotherapy (NAC) facilitates promptly optimization of treatment strategies. This study aimed to develop and validate a deep learning network (DLN) using baseline computed tomography images to predict lymph node metastasis (LNM) after NAC in patients with locally advanced gastric cancer (LAGC). A total of 1205 LAGC patients were retrospectively recruited from three hospitals between January 2013 and March 2023, constituting a training cohort, an internal validation cohort, and two external validation cohorts. A transformer-based DLN was developed using 3D tumor images to predict LNM after NAC. A clinical model was constructed through multivariate logistic regression analysis as a baseline for subsequent comparisons. The performance of the models was evaluated through discrimination, calibration, and clinical applicability. Furthermore, Kaplan-Meier survival analysis was conducted to assess overall survival (OS) of LAGC patients at two follow-up centers. The DLN outperformed the clinical model and demonstrated a robust performance for predicting LNM in the training and validation cohorts, with areas under the curve (AUCs) of 0.804 (95% confidence interval [CI], 0.752-0.849), 0.748 (95% CI, 0.660-0.830), 0.788 (95% CI, 0.735-0.835), and 0.766 (95% CI, 0.717-0.814), respectively. Decision curve analysis exhibited a high net clinical benefit of the DLN. Moreover, the DLN was significantly associated with the OS of LAGC patients [Center 1: hazard ratio (HR), 1.789, P<0.001; Center 2:HR, 1.776, P=0.013]. The transformer-based DLN provides early and effective prediction of LNM and survival outcomes in LAGC patients receiving NAC, with promise to guide individualized therapy. Future prospective multicenter studies are warranted to further validate our model. National Natural Science Foundation of China (NO. 82373432, 82171923, 82202142), Project Funded by China Postdoctoral Science Foundation (NO. 2022M720857), Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (NO. U22A20345), National Science Fund for Distinguished Young Scholars of China (NO. 81925023), Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (NO. 2022B1212010011), High-level Hospital Construction Project (NO. DFJHBF202105), Natural Science Foundation of Guangdong Province for Distinguished Young Scholars (NO. 2024B1515020091).

A deep learning-based radiomics model for predicting lymph node status from lung adenocarcinoma

ObjectivesAt present, there are many limitations in the evaluation of lymph node metastasis of lung adenocarcinoma. Currently, there is a demand for a safe and accurate method to predict lymph node metastasis of lung cancer. In this study, radiomics was used to accurately predict the lymph node status of lung adenocarcinoma patients based on contrast-enhanced CT.MethodsA total of 503 cases that fulfilled the analysis requirements were gathered from two distinct hospitals. Among these, 287 patients exhibited lymph node metastasis (LNM +) while 216 patients were confirmed to be without lymph node metastasis (LNM-). Using both traditional and deep learning methods, 22,318 features were extracted from the segmented images of each patient's enhanced CT. Then, the spearman test and the least absolute shrinkage and selection operator were used to effectively reduce the dimension of the feature data, enabling us to focus on the most pertinent features and enhance the overall analysis. Finally, the classification model of lung adenocarcinoma lymph node metastasis was constructed by machine learning algorithm. The Accuracy, AUC, Specificity, Precision, Recall and F1 were used to evaluate the efficiency of the model.ResultsBy incorporating a comprehensively selected set of features, the extreme gradient boosting method (XGBoost) effectively distinguished the status of lymph nodes in patients with lung adenocarcinoma. The Accuracy, AUC, Specificity, Precision, Recall and F1 of the prediction model performance on the external test set were 0.765, 0.845, 0.705, 0.784, 0.811 and 0.797, respectively. Moreover, the decision curve analysis, calibration curve and confusion matrix of the model on the external test set all indicated the stability and accuracy of the model.ConclusionsLeveraging enhanced CT images, our study introduces a noninvasive classification prediction model based on the extreme gradient boosting method. This approach exhibits remarkable precision in identifying the lymph node status of lung adenocarcinoma patients, offering a safe and accurate alternative to invasive procedures. By providing clinicians with a reliable tool for diagnosing and assessing disease progression, our method holds the potential to significantly improve patient outcomes and enhance the overall quality of clinical practice.

ASO Visual Abstract: Development and Validation of PET/CT-Based Nomogram for Preoperative Prediction of Lymph Node Status in Esophageal Squamous Cell Carcinoma.

ASO Visual Abstract: Development and Validation of PET/CT-Based Nomogram for Preoperative Prediction of Lymph Node Status in Esophageal Squamous Cell Carcinoma.

Clinicopathological Correlation of P53 Protein Expression in different Histological Subtypes of Gastric Carcinoma

Introduction: Gastric cancer is one of the common cancers after lung cancer worldwide, especially in the developing countries. p53 immunohistochemical expression has been proposed as a potential tool to evaluate the biological behavior of gastric cancer. However, its practical implication in gastric cancer prognosis or treatment by restoration of mutated p53 function is yet to be fully exploited. Hence, this study with the aim of assessing the yield of p53 expression in gastric carcinoma and its relationship with various clinicopathological parameters was formulated. Objectives: To assess immunohistochemical expression of p53 protein in different histological subtypes of gastric carcinoma cases and to study correlation between p53 protein expression and different clinicopathological parameters like age, gender, tumour location, gross pattern, histological types, degree of tumor differentiation and pathological staging in the above said cases. Materials and Methods: This was a hospital based cross-sectional study done over a period of 18 months on 60 cases of gastrectomies with proven histological diagnosis of gastric carcinoma. Observation and Results: Strong p53 expression was frequent among elderly male patients (&gt;60 years) who had ulceroproliferative tumors, located at pyloric antrum belonging to pTNM stage IIIA. In 87% cases, it was expressed, predominantly in moderately differentiated intestinal type with serosal invasion. Also, cases even with no lymph node metastasis showed p53 expression. Conclusion: Few findings were contradictory to other similar studies e.g. lymph node negative cases showing P53 expression does not support the hypothesis of predicting lymph node status by p53 expression. No significant correlation between p53 expression and different clinicopathological parameters in gastric carcinoma cases was seen. Keywords: P53, immunohistochemical, clinicopathological, histological subtypes, metastasis

Prediction of lymph node metastasis in operable cervical cancer using clinical parameters and deep learning with MRI data: a multicentre study.

To develop and validate a magnetic resonance imaging-based (MRI) deep multiple instance learning (D-MIL) model and combine it with clinical parameters for preoperative prediction of lymph node metastasis (LNM) in operable cervical cancer. A total of 392 patients with cervical cancer were retrospectively enrolled. Clinical parameters were analysed by logistical regression to construct a clinical model (M1). A ResNet50 structure is applied to extract features at the instance level without using manual annotations about the tumour region and then construct a D-MIL model (M2). A hybrid model (M3) was constructed by M1 and M2 scores. The diagnostic performance of each model was evaluated by the area under the receiver operating characteristic curve (AUC) and compared using the Delong method. Disease-free survival (DFS) was evaluated by the Kaplan‒Meier method. SCC-Ag, maximum lymph node short diameter (LNmax), and tumour volume were found to be independent predictors of M1 model. For the diagnosis of LNM, the AUC of the training/internal/external cohort of M1 was 0.736/0.690/0.732, the AUC of the training/internal/external cohort of M2 was 0.757/0.714/0.765, and the AUC of the training/internal/external cohort of M3 was 0.838/0.764/0.835. M3 showed better performance than M1 and M2. Through the survival analysis, patients with higher hybrid model scores had a shorter time to reach DFS. The proposed hybrid model could be used as a personalised non-invasive tool, which is helpful for predicting LNM in operable cervical cancer. The score of the hybrid model could also reflect the DFS of operable cervical cancer. Lymph node metastasis is an important factor affecting the prognosis of cervical cancer. Preoperative prediction of lymph node status is helpful to make treatment decisions, improve prognosis, and prolong survival time. • The MRI-based deep-learning model can predict the LNM in operable cervical cancer. • The hybrid model has the highest diagnostic efficiency for the LNM prediction. • The score of the hybrid model can reflect the DFS of operable cervical cancer.

Development and validation of an ultrasound‑based radiomics nomogram to predict lymph node status in patients with high-grade serous ovarian cancer: a retrospective analysis

BackgroundDespite advances in medical imaging technology, the accurate preoperative prediction of lymph node status remains challenging in ovarian cancer. This retrospective study aimed to investigate the feasibility of using ultrasound-based radiomics combined with preoperative clinical characteristics to predict lymph node metastasis (LNM) in patients with high-grade serous ovarian cancer (HGSOC).ResultsPatients with 401 HGSOC lesions from two institutions were enrolled: institution 1 for the training cohort (n = 322) and institution 2 for the external test cohort (n = 79). Radiomics features were extracted from the three preoperative ultrasound images of each lesion. During feature selection, primary screening was first performed using the sample variance F-value, followed by recursive feature elimination (RFE) to filter out the 12 most significant features for predicting LNM. The radscore derived from these 12 radiomic features and three clinical characteristics were used to construct a combined model and nomogram to predict LNM, and subsequent 10-fold cross-validation was performed. In the test phase, the three models were tested with external test cohort. The radiomics model had an area under the curve (AUC) of 0.899 (95% confidence interval [CI]: 0.864–0.933) in the training cohort and 0.855 (95%CI: 0.774–0.935) in the test cohort. The combined model showed good calibration and discrimination in the training cohort (AUC = 0.930) and test cohort (AUC = 0.881), which were superior to those of the radiomic and clinical models alone.ConclusionsThe nomogram consisting of the radscore and preoperative clinical characteristics showed good diagnostic performance in predicting LNM in patients with HGSOC. It may be used as a noninvasive method for assessing the lymph node status in these patients.

Prediction of lymph node status in patients with surgically treated head and neck squamous cell carcinoma via neck lavage cytology: A pilot study.

Neck dissection is a standardized surgical procedure for patients with head and neck squamous cell carcinoma (HNSCC) and plays a critical role in the choice of adjuvant treatment based on histopathological findings. Saline irrigation is routinely performed at the end of surgery. However, this irrigant is not used for diagnostic purposes. Intraoperative irrigation of the neck dissection wound was performed in 56 patients with HNSCC (N=93 neck dissections), and the cytological suspension obtained was processed via the liquid-based cytology (LBC) technique, Papanicolaou staining, and immunocytochemical staining. Microscopic preparations were screened for the presence of tumor cells and classified as positive, borderline, or negative. These results were correlated with the histopathological and clinical data. Neck lavage LBC demonstrated high diagnostic value in detecting lymph node metastases (N+) with extracapsular spread (ECS), with a specificity, sensitivity, negative predictive value, and positive predictive value of 93.1%, 100%, 100%, and 80%, respectively. Tumor cells were detected in 4.8% of N- cases, 20% of N+ cases without ECS, and 100% of N+ cases with ECS. Receiver operating characteristic curve analysis showed an area under the curve of 0.8429 for the prediction of N+ (p<.0001) and 0.9658 for the prediction of N+ with ECS (p<.0001). Differential lavage cytology can provide valid and rapid information on the lymph node status in patients with HNSCC and showed an excellent correlation with histopathology. Thus, neck lavage LBC may facilitate faster and more reasonable planning of adjuvant treatment and help improve the therapeutic management of patients with HNSCC.

The role of adenocarcinoma subtypes and immunohistochemistry in predicting lymph node metastasis in early invasive lung adenocarcinoma

BackgroundIdentifying lymph node metastasis areas during surgery for early invasive lung adenocarcinoma remains challenging. The aim of this study was to develop a nomogram mathematical model before the end of surgery for predicting lymph node metastasis in patients with early invasive lung adenocarcinoma.MethodsIn this study, we included patients with invasive lung adenocarcinoma measuring ≤ 2 cm who underwent pulmonary resection with definite pathology at Qilu Hospital of Shandong University from January 2020 to January 2022. Preoperative biomarker results, clinical features, and computed tomography characteristics were collected. The enrolled patients were randomized into a training cohort and a validation cohort in a 7:3 ratio. The training cohort was used to construct the predictive model, while the validation cohort was used to test the model independently. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors. The prediction model and nomogram were established based on the independent risk factors. Recipient operating characteristic (ROC) curves were used to assess the discrimination ability of the model. Calibration capability was assessed using the Hosmer–Lemeshow test and calibration curves. The clinical utility of the nomogram was assessed using decision curve analysis (DCA).ResultsThe overall incidence of lymph node metastasis was 13.23% (61/461). Six indicators were finally determined to be independently associated with lymph node metastasis. These six indicators were: age (P < 0.001), serum amyloid (SA) (P = 0.008); carcinoma antigen 125 (CA125) (P = 0. 042); mucus composition (P = 0.003); novel aspartic proteinase of the pepsin family A (Napsin A) (P = 0.007); and cytokeratin 5/6 (CK5/6) (P = 0.042). The area under the ROC curve (AUC) was 0.843 (95% CI: 0.779–0.908) in the training cohort and 0.838 (95% CI: 0.748–0.927) in the validation cohort. the P-value of the Hosmer–Lemeshow test was 0.0613 in the training cohort and 0.8628 in the validation cohort. the bias of the training cohort corrected C-index was 0.8444 and the bias-corrected C-index for the validation cohort was 0.8375. demonstrating that the prediction model has good discriminative power and good calibration.ConclusionsThe column line graphs created showed excellent discrimination and calibration to predict lymph node status in patients with ≤ 2 cm invasive lung adenocarcinoma. In addition, the predictive model has predictive potential before the end of surgery and can inform clinical decision making.

Preoperative CT-based deep learning radiomics model to predict lymph node metastasis and patient prognosis in bladder cancer: a two-center study

ObjectiveTo establish a model for predicting lymph node metastasis in bladder cancer (BCa) patients.MethodsWe retroactively enrolled 239 patients who underwent three-phase CT and resection for BCa in two centers (training set, n = 185; external test set, n = 54). We reviewed the clinical characteristics and CT features to identify significant predictors to construct a clinical model. We extracted the hand-crafted radiomics features and deep learning features of the lesions. We used the Minimum Redundancy Maximum Relevance algorithm and the least absolute shrinkage and selection operator logistic regression algorithm to screen features. We used nine classifiers to establish the radiomics machine learning signatures. To compensate for the uneven distribution of the data, we used the synthetic minority over-sampling technique to retrain each machine-learning classifier. We constructed the combined model using the top-performing radiomics signature and clinical model, and finally presented as a nomogram. We evaluated the combined model’s performance using the area under the receiver operating characteristic, accuracy, calibration curves, and decision curve analysis. We used the Kaplan–Meier survival curve to analyze the prognosis of BCa patients.ResultsThe combined model incorporating radiomics signature and clinical model achieved an area under the receiver operating characteristic of 0.834 (95% CI: 0.659–1.000) for the external test set. The calibration curves and decision curve analysis demonstrated exceptional calibration and promising clinical use. The combined model showed good risk stratification performance for progression-free survival.ConclusionThe proposed CT-based combined model is effective and reliable for predicting lymph node status of BCa patients preoperatively.Critical relevance statementBladder cancer is a type of urogenital cancer that has a high morbidity and mortality rate. Lymph node metastasis is an independent risk factor for death in bladder cancer patients. This study aimed to investigate the performance of a deep learning radiomics model for preoperatively predicting lymph node metastasis in bladder cancer patients.Key points• Conventional imaging is not sufficiently accurate to determine lymph node status.• Deep learning radiomics model accurately predicted bladder cancer lymph node metastasis.• The proposed method showed satisfactory patient risk stratification for progression-free survival.Graphical

Prediction of lymph node status in stage T1 esophageal squamous cell carcinoma.

263 Background: With the development of endoscopic therapy and the introduction of immunotherapies, it is more important for preserving the esophagus and uninvaded lymph nodes to avoid the damages involving the long-term quality of life and normal immune function caused by esophagectomy with radical lymphadenectomy. Methods: We collected patients with T1 ESCC who underwent endoscopic resection or esophagectomy from 2014 to 2021 in three cancer centers. The primary end point was presence of lymph node metastasis (LNM) in surgical resection specimens or detection of metastases during follow-up. Univariate and multivariate logistic regression were used to screen the clinicopathological features related to LNM. Using the function 'lrm' constructed the prediction model. The area under the curve (AUC) of receiver operating characteristic and calibration graphs were used to assess the performance of the model. An external validation was conducted to evaluate the generalization of the model. We then prospectively collected 28 surgically resected tissue samples of T1 ESCC to further supplement the model from the cellular and molecular level, and used 6 markers (CK, CD34, D2-40, CD4, CD8, FOXP3) and DAPI dye to process multiple immunofluorescence (mIF) staining. Afterwards, we analyzed the density, spatial distribution of microvessels, lymphatic capillaries and tumor-associated immune cells and the interaction between them and malignant cells in the tumor microenvironment of T1 ESCC to explore the relationship between tumor microenvironment and risk of LNM. Results: 1109 patients with T1 ESCC were included. 653 patients from two centers constituted the training cohort, and 456 patients from the third center served as the validation cohort. Multivariate logistic regression based on the training cohort showed that tumor invasion into the submucosa, poor histological grade, and lymphovascular invasion were risk factors for LNM in T1 ESCC. Subsequently, we constructed a model based on three variables to predict the risk of LNM in T1 ESCC. The model demonstrated good discriminative ability (AUC 0.858, 95%CI 0.826-0.890). After mIF staining of 28 prospectively collected T1 ESCC tissue samples, we observed that microvessels and lymphatic capillaries were abundant in the mucosa in lymph node-positive samples but not in lymph node-negative samples. In addition, the average density of FOXP3+ T cells in samples with and without metastasis was 253/mm2 and 26/mm2 (P &lt; 0.001), and the average density of CD8+ T cells was 112/mm2 and 432/mm2 (P &lt; 0.001) in quantitative analysis, respectively. Conclusions: This study (RENMIN-237) developed a well-performing LNM risk stratification model of T1 ESCC to reduce both overtreatment and undertreatment, and we found that FOXP3+ T cells in the tumor microenvironment are a risk factor for LNM, while CD8+ T cells can serve as a protective factor against LNM.

Deep learning to predict lymph node status on pre-operative staging CT in patients with colon cancer.

Lymph node (LN) metastases are an important determinant of survival in patients with colon cancer, but remain difficult to accurately diagnose on preoperative imaging. This study aimed to develop and evaluate a deep learning model to predict LN status on preoperative staging CT. In this ambispective diagnostic study, a deep learning model using a ResNet-50 framework was developed to predict LN status based on preoperative staging CT. Patients with a preoperative staging abdominopelvic CT whounderwent surgical resection for colon cancer were enrolled. Data were retrospectively collected from February 2007 to October 2019 and randomly separated into training, validation, and testing cohort 1. To prospectively test the deep learning model, data for testing cohort 2 was collected from October 2019 to July 2021. Diagnostic performance measures were assessed by the AUROC. A total of 1,201 patients (median [range] age, 72 [28-98 years]; 653 [54.4%] male) fulfilled the eligibility criteria and were included in the training (n = 401), validation (n = 100), testing cohort 1 (n = 500) and testing cohort 2 (n = 200). The deep learning model achieved an AUROC of 0.619 (95% CI 0.507-0.731) in the validation cohort. In testing cohort 1 and testing cohort 2, the AUROC was 0.542 (95% CI 0.489-0.595) and 0.486 (95% CI 0.403-0.568), respectively. A deep learning model based on a ResNet-50 framework does not predict LN status on preoperative staging CT in patients with colon cancer.

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.

Deep learning trained on lymph node status predicts outcome from gastric cancer histopathology: a retrospective multicentric study

AimGastric cancer (GC) is a tumour entity with highly variant outcomes. Lymph node metastasis is a prognostically adverse biomarker. We hypothesised that GC primary tissue contains information that is predictive of lymph node status and patient prognosis and that this information can be extracted using deep learning (DL). MethodsUsing three patient cohorts comprising 1146 patients, we trained and validated a DL system to predict lymph node status directly from haematoxylin and eosin–stained GC tissue sections. We investigated the concordance between the DL-based prediction from the primary tumour slides (aiN score) and the histopathological lymph node status (pN). Furthermore, we assessed the prognostic value of the aiN score alone and when combined with the pN status. ResultsThe aiN score predicted the pN status reaching area under the receiver operating characteristic curves of 0.71 in the training cohort and 0.69 and 0.65 in the two test cohorts. In a multivariate Cox analysis, the aiN score was an independent predictor of patient survival with hazard ratios of 1.5 in the training cohort and of 1.3 and 2.2 in the two test cohorts. A combination of the aiN score and the pN status prognostically stratified patients by survival with p-values <0.05 in logrank tests. ConclusionGC primary tumour tissue contains additional prognostic information that is accessible using the aiN score. In combination with the pN status, this can be used for personalised management of GC patients after prospective validation.

A clinically practical model for the preoperative prediction of lymph node metastasis in bladder cancer: a multicohort study.

The aim of this study was to construct a clinically practical model to precisely predict lymph node (LN) metastasis in bladder cancer patients. Four independent cohorts were included. The least absolute shrinkage and selection operator regression with multivariate logistic regression were applied. The diagnostic efficacy of LN score and CT/MRI was compared by accuracy, sensitivity, specificity, and area under curve (AUC). A total of 606 patients were included to develop a basic prediction model. After multistep gene selection, the LN metastasis prediction model was constructed with 5 genes. The model can accurately predict LN metastasis with an AUC of 0.781. For clinically practical use, we transformed the model into a Fast LN Scoring System using the SYSMH cohort (n = 105). High LN score patients exhibited a 72.2% LN metastasis rate, while low LN score patients showed a 3.4% LN metastasis rate. The LN score achieved a superior accuracy than CT/MRI (0.882 vs. 0.727). Application of LN score can correct the diagnosis of 88% (22/25) patients who were misdiagnosed by CT/MRI. The clinically practical LN score can precisely, rapidly, and conveniently predict LN status, which will assist preoperative diagnosis for LN metastasis and guide precise therapy.

Prediction of lymph node status in patients with early-stage cervical cancer based on radiomic features of magnetic resonance imaging (MRI) images

BackgroundLymph node metastasis is an important factor affecting the treatment and prognosis of patients with cervical cancer. However, the comparison of different algorithms and features to predict lymph node metastasis is not well understood. This study aimed to construct a non-invasive model for predicting lymph node metastasis in patients with cervical cancer based on clinical features combined with the radiomic features of magnetic resonance imaging (MRI) images.MethodsA total of 180 cervical cancer patients were divided into the training set (n = 126) and testing set (n = 54). In this cross-sectional study, radiomic features of MRI images and clinical features of patients were collected. The least absolute shrinkage and selection operator (LASSO) regression was used to filter the features. Seven machine learning methods, including eXtreme Gradient Boosting (XGBoost), Logistic Regression, Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosting Decision Tree (GBDT) are used to build the models. Receiver operating characteristics (ROC) curve and area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the performance of the models.ResultsOf these 180 patients, 49 (27.22%) patients had lymph node metastases. Five of the 122 radiomic features and 3 clinical features were used to build predictive models. Compared with other models, the MNB model was the most robust, with its AUC, specificity, and accuracy on the testing set of 0.745 (95%CI: 0.740–0.750), 0.900 (95%CI: 0.807–0.993), and 0.778 (95%CI: 0.667–0.889), respectively. Furthermore, the AUCs of the MNB models with clinical features only, radiomic features only, and combined features were 0.698 (95%CI: 0.692–0.704), 0.632 (95%CI: 0.627–0.637), and 0.745 (95%CI: 0.740–0.750), respectively.ConclusionThe MNB model, which combines the radiomic features of MRI images with the clinical features of the patient, can be used as a non-invasive tool for the preoperative assessment of lymph node metastasis.

A deep-learning radiomics-based lymph node metastasis predictive model for pancreatic cancer: a diagnostic study.

Preoperative lymph node (LN) status is essential in formulating the treatment strategy among pancreatic cancer patients. However, it is still challenging to evaluate the preoperative LN status precisely now. A multivariate model was established based on the multiview-guided two-stream convolution network (MTCN) radiomics algorithms, which focused on primary tumor and peri-tumor features. Regarding discriminative ability, survival fitting, and model accuracy, different models were compared. Three hundred and sixty-three pancreatic cancer patients were divided in to train and test cohorts by 7:3. The modified MTCN (MTCN+) model was established based on age, CA125, MTCN scores, and radiologist judgement. The MTCN+ model outperformed the MTCN model and the artificial model in discriminative ability and model accuracy. [Train cohort area under curve (AUC): 0.823 vs. 0.793 vs. 0.592; train cohort accuracy (ACC): 76.1 vs. 74.4 vs. 56.7%; test cohort AUC: 0.815 vs. 0.749 vs. 0.640; test cohort ACC: 76.1 vs. 70.6 vs. 63.3%; external validation AUC: 0.854 vs. 0.792 vs. 0.542; external validation ACC: 71.4 vs. 67.9 vs. 53.5%]. The survivorship curves fitted well between actual LN status and predicted LN status regarding disease free survival and overall survival. Nevertheless, the MTCN+ model performed poorly in assessing the LN metastatic burden among the LN positive population. Notably, among the patients with small primary tumors, the MTCN+ model performed steadily as well (AUC: 0.823, ACC: 79.5%). A novel MTCN+ preoperative LN status predictive model was established and outperformed the artificial judgement and deep-learning radiomics judgement. Around 40% misdiagnosed patients judged by radiologists could be corrected. And the model could help precisely predict the survival prognosis.

Progress in research on ultrasound radiomics for predicting the prognosis of breast cancer.

Breast cancer is the most common malignant tumor and the leading cause of cancer-related deaths in women worldwide. Effective means of predicting the prognosis of breast cancer are very helpful in guiding treatment and improving patients' survival. Features extracted by radiomics reflect the genetic and molecular characteristics of a tumor and are related to its biological behavior and the patient's prognosis. Thus, radiomics provides a new approach to noninvasive assessment of breast cancer prognosis. Ultrasound is one of the commonest clinical means of examining breast cancer. In recent years, some results of research into ultrasound radiomics for diagnosing breast cancer, predicting lymph node status, treatment response, recurrence and survival times, and other aspects, have been published. In this article, we review the current research status and technical challenges of ultrasound radiomics for predicting breast cancer prognosis. We aim to provide a reference for radiomics researchers, promote the development of ultrasound radiomics, and advance its clinical application.