A Preoperative Risk Prediction Model for Lymph Node Examination of Stage I-III Colon Cancer Patients: A Population-Based Study.
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ABSTRACT:
SUBMITTER: Wang Y
PROVIDER: S-EPMC7097944 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThis study identified predictors of favorable overall survival (OS) for stage III colon cancer patients who had only one lymph node (LN) metastasis (N1a).MethodsVariables, including preoperative carcinoembryonic antigen (CEA) level, LN sampling status, and the choices of postoperative adjuvant chemotherapy, were recorded. Prognostic significance was determined using the log-rank test and multivariate Cox regression analysis.ResultsThe median 42-month follow-up period included 363 eligible patients. Among them, 230 (63.3%) received only 5-flurouracil (5-FU) adjuvant chemotherapy; 76 (20.9%) underwent oxaliplatin-based regimens; and 57 (15.7%) chose surgery alone. The 5-year survival rate of these evaluated patients was 75%, 63%, and 77%, respectively (P = 0.823). Multivariate analysis revealed that normal preoperative CEA level (≦5 ng/mL) and adequate LN sampling (LN ≧ 12) were significant predictors for higher 5-year OS (P < 0.001; P = 0.007, respectively). However, the use of postoperative adjuvant chemotherapy in these N1a colon cancer patients did not significantly affect their 5-year OS.ConclusionsA preoperative CEA level of less than or equal to 5 ng/mL, and curative surgery with an adequate lymphadenectomy determined a favorable OS outcome in stage III colon cancer with only one LN metastasis.
| S-EPMC4575069 | biostudies-literature
Project description:Lymph node (LN) status after surgery for rectal cancer is affected by preoperative radiotherapy. The purpose of this study was to perform a population-based evaluation of the impact of pathologic LN status after neoadjuvant radiotherapy on survival. A total of 1,650 patients receiving neoadjuvant chemotherapy in Surveillance, Epidemiology, and End Results Program (SEER)-registered ypIII stage rectal cancer was analyzed. We identified the optimal cutoff for retrieved LNs as 10 (χ2 = 14.006, P < 0.001), which was validated as an independent prognosis factors in a Cox regression model. Further analysis showed that the LN count was only a prognosis factor with the number from 8 to 16(except for 13).After the number 16, the 5-year survival rate decreased gradually. Collectively, our results confirmed that the number of LNs in yp III stage rectal patients was a prognosis factor only with the numbers from 8 to 16(except for 13). Using the total mesorectal excision technique with an adequate pathologic examination, a large number of LNs retrieved (≥17) might indicate worse tumor response grade and poorer survival.
| S-EPMC4652213 | biostudies-other
Project description:ObjectiveTo build and validate a CT radiomic model for pre-operatively predicting lymph node metastasis in early cervical carcinoma.Methods and materialsA data set of 150 patients with Stage IB1 to IIA2 cervical carcinoma was retrospectively collected from the Nanfang hospital and separated into a training cohort (n = 104) and test cohort (n = 46). A total of 348 radiomic features were extracted from the delay phase of CT images. Mann-Whitney U test, recursive feature elimination, and backward elimination were used to select key radiomic features. Ridge logistics regression was used to build a radiomic model for prediction of lymph node metastasis (LNM) status by combining radiomic and clinical features. The area under the receiver operating characteristic curve (AUC) and κ test were applied to verify the model.ResultsTwo radiomic features from delay phase CT images and one clinical feature were associated with LNM status: log-sigma-2-0 mm-3D_glcm_Idn (p = 0.01937), wavelet-HL_firstorder_Median (p = 0.03592), and Stage IB (p = 0.03608). Radiomic model was built consisting of the three features, and the AUCs were 0.80 (95% confidence interval: 0.70 ~ 0.90) and 0.75 (95% confidence intervalI: 0.53 ~ 0.93) in training and test cohorts, respectively. The κ coefficient was 0.84, showing excellent consistency.ConclusionA non-invasive radiomic model, combining two radiomic features and a International Federation of Gynecology and Obstetrics stage, was built for prediction of LNM status in early cervical carcinoma. This model could serve as a pre-operative tool.Advances in knowledgeA noninvasive CT radiomic model, combining two radiomic features and the International Federation of Gynecology and Obstetrics stage, was built for prediction of LNM status in early cervical carcinoma.
| S-EPMC7362918 | biostudies-literature
Project description:Lymph node involvement increases the risk of breast cancer recurrence. An accurate non-invasive assessment of nodal involvement is valuable in cancer staging, surgical risk, and cost savings. Radiomics has been proposed to pre-operatively predict sentinel lymph node (SLN) status; however, radiomic models are known to be sensitive to acquisition parameters. The purpose of this study was to develop a prediction model for preoperative prediction of SLN metastasis using deep learning-based (DLB) features and compare its predictive performance to state-of-the-art radiomics. Specifically, this study aimed to compare the generalizability of radiomics vs DLB features in an independent test set with dissimilar resolution. Dynamic contrast-enhancement images from 198 patients (67 positive SLNs) were used in this study. Of these subjects, 163 had an in-plane resolution of 0.7 × 0.7 mm2, which were randomly divided into a training set (approximately 67%) and a validation set (approximately 33%). The remaining 35 subjects with a different in-plane resolution (0.78 × 0.78 mm2) were treated as independent testing set for generalizability. Two methods were employed: (1) conventional radiomics (CR), and (2) DLB features which replaced hand-curated features with pre-trained VGG-16 features. The threshold determined using the training set was applied to the independent validation and testing dataset. Same feature reduction, feature selection, model creation procedures were used for both approaches. In the validation set (same resolution as training), the DLB model outperformed the CR model (accuracy 83% vs 80%). Furthermore, in the independent testing set of the dissimilar resolution, the DLB model performed markedly better than the CR model (accuracy 77% vs 71%). The predictive performance of the DLB model outperformed the CR model for this task. More interestingly, these improvements were seen particularly in the independent testing set of dissimilar resolution. This could indicate that DLB features can ultimately result in a more generalizable model.
| S-EPMC8901808 | biostudies-literature
Project description:Lymph node (LN) harvest is influenced by several factors, including tumor genetics. Microsatellite instability (MSI) is associated with improved node harvest, but the association to other genetic factors is largely unknown. Research methods included a prospective series of stage I-III colon cancer patients undergoing ex vivo sentinel-node sampling. The presence of MSI, KRAS mutations in codons 12 and 13, and BRAF V600E mutations was analyzed. Uni- and multivariate regression models for node sampling were adjusted for clinical, pathological and molecular features. Of 204 patients, 67% had an adequate harvest (≥ 12 nodes). Adequate harvest was highest in patients whose tumors exhibited MSI (79%; odds ratio [OR] 2.5, 95% confidence interval [CI] 1.2-4.9; P = 0.007) or were located in the proximal colon (73%; 2.8, 1.5-5.3; P = 0.002). In multiple linear regression, MSI was a significant predictor of the total LN count (P = 0.02). Total node count was highest for cancers with MSI and no KRAS/BRAF mutations. The independent association between MSI and a high LN count persisted for stage I and II cancers (P = 0.04). Tumor location in the proximal colon was the only significant predictor of an adequate LN harvest (adjusted OR 2.4, 95% CI 1.2-4.9; P = 0.01). An increase in the total number of nodes harvested was not associated with an increase in nodal metastasis. In conclusion, number of nodes harvested is highest for cancers of the proximal colon and with MSI. The nodal harvest associated with MSI is influenced by BRAF and KRAS genotypes, even for cancers of proximal location. Mechanisms behind the molecular diversity and node yield should be further explored.
| S-EPMC4344456 | biostudies-literature
Project description:Background: This study aimed to construct a preoperative model predicting lymph node metastasis (LNM) in IB1-IIA2 stage cervical squamous cell cancer (CSCC) based on hematological indexes. Merhods: Between February 2011 and February 2022, 463 patients with IB1-IIA2 stage CSCC underwent radical resection. Patients were allocated to either a model-development cohort (n=337) or a validation cohort (n=126). The final model was determined by comparing different methods of variable selection, and then its discrimination and calibration metrics were evaluated. A predicted probability of LNM < 5% was defined as low risk. ROC curves were used to define high risk. Results: Age, lactate dehydrogenase level, FIGO stage, squamous cell carcinoma antigen, cancer antigen 125, and cancer antigen 199 were identified as critical factors for the construction of the model. The model demonstrated good discrimination and calibration (concordance index, 0.761; 95% confidence interval, 0.666-0.884). In the validation cohort the discrimination accuracy was 0.821 (95% confidence interval, 0.714 - 0.927). In the model-development cohort, 11.9% were classified as low risk with a negative predictive value of 95.0%, and 24.9% were classified as high risk with a positive predictive value of 39.3%. Conclusion: A predictive model was developed and validated for LNM in IB1-IIA2 stage CSCC. The model will assist physicians in appraising the risk of LNM in preoperative patients and could aid in patient counseling and individualized clinical decision-making.
| S-EPMC10355196 | biostudies-literature
Project description:Objective. To develop an artificial intelligence method predicting lymph node metastasis (LNM) for patients with colorectal cancer (CRC). Impact Statement. A novel interpretable multimodal AI-based method to predict LNM for CRC patients by integrating information of pathological images and serum tumor-specific biomarkers. Introduction. Preoperative diagnosis of LNM is essential in treatment planning for CRC patients. Existing radiology imaging and genomic tests approaches are either unreliable or too costly. Methods. A total of 1338 patients were recruited, where 1128 patients from one centre were included as the discovery cohort and 210 patients from other two centres were involved as the external validation cohort. We developed a Multimodal Multiple Instance Learning (MMIL) model to learn latent features from pathological images and then jointly integrated the clinical biomarker features for predicting LNM status. The heatmaps of the obtained MMIL model were generated for model interpretation. Results. The MMIL model outperformed preoperative radiology-imaging diagnosis and yielded high area under the curve (AUCs) of 0.926, 0.878, 0.809, and 0.857 for patients with stage T1, T2, T3, and T4 CRC, on the discovery cohort. On the external cohort, it obtained AUCs of 0.855, 0.832, 0.691, and 0.792, respectively (T1-T4), which indicates its prediction accuracy and potential adaptability among multiple centres. Conclusion. The MMIL model showed the potential in the early diagnosis of LNM by referring to pathological images and tumor-specific biomarkers, which is easily accessed in different institutes. We revealed the histomorphologic features determining the LNM prediction indicating the model ability to learn informative latent features.
| S-EPMC10521754 | biostudies-literature
Project description:BACKGROUND:Regional lymph node (LN) metastasis is a strong and well-established prognostic factor in colon cancer, and recent data suggest a prognostic value of detecting micrometastases and isolated tumor cells in regional LNs. The aim of the study was to investigate the clinical relevance of detecting sentinel lymph node (SLN) metastases in colon cancer patients by measuring the novel metastasis marker PHGR1 mRNA. METHODS:Using quantitative reverse-transcription polymerase chain reaction, we measured PHGR1 mRNA levels in SLNs and primary tumors from 206 patients surgically treated for stage I to III colon cancer and 52 normal LNs from patients undergoing surgery for benign colon diseases. The prognostic impact of these findings was evaluated by Kaplan-Meier analysis and Cox proportional-hazards regression. RESULTS:Compared to normal LNs, elevated PHGR1 mRNA levels were detected in SLNs from 56 (89%) of the 63 patients with pN+ disease. Furthermore, 68 (48%) of the 143 node-negative (pN0) patients had elevated PHGR1 mRNA levels in SLNs, suggesting occult metastases. With a median follow-up of 7.2 years, a significantly shorter recurrence-free (P=.005) and disease-specific (P=.02) survival was observed in patients with elevated PHGR1 mRNA levels in SLNs. Multivariable modeling showed that the SLN PHGR1 mRNA level was an independent prognostic factor. However, when the survival analyses were restricted to pN0 patients, no significant prognostic information was found. CONCLUSION:Measuring PHGR1 mRNA in SLNs provided independent prognostic information on operable colon cancer patients but not in the pN0 subgroup.
| S-EPMC5884186 | biostudies-literature
Project description:Background: Preoperative accurate prediction of lymph node status is especially important for the formulation of treatment plans for patients with gastric cancer (GC). The purpose of this study was to establish decision rules and a risk assessment model for lymph node metastasis (LNM) in GC using preoperative indicators. Methods: The clinical data of 554 patients who underwent gastrectomy with D2 lymphadenectomy were collected. A 1:1 propensity score matching (PSM) system was used, and the clinical data of the matched 466 patients were further analyzed. The important risk factors for LNM were extracted by the random forest algorithm, and decision rules and nomogram models for LNM were constructed with a classification tree and the "rms" package of R software, respectively. Results: Tumor size (OR: 2.058; P = 0.000), computed tomography (CT) findings (OR: 1.969; P = 0.001), grade (OR: 0.479; P = 0.000), hemoglobin (Hb) (OR: 1.211; P = 0.005), CEA (OR: 1.111; P = 0.017), and CA19-9 (OR: 1.040; P = 0.033) were independent risk factors for LNM in GC. Tumor size did rank first in the ranking of important factors for LNM in GC and was the first-level segmentation of the two initial branches of the classification tree. The accuracy, sensitivity, specificity, and positive predictive value of the decision rules in diagnosing preoperative LNM in GC were 75.6, 85.7, 73.9, 73.5, and 79.3%, respectively. The accuracy, sensitivity, and specificity of the risk assessment model in predicting preoperative LNM in GC were 79.3, 80.3, and 79.4%, respectively. Conclusion: Tumor size was the most important factor for evaluating LNM in GC. This decision rules and nomogram model constructed to take into account tumor size, CT findings, grade, hemoglobin, CEA, and CA19-9 effectively predicted the incidence of LNM in preoperative GC.
| S-EPMC7492596 | biostudies-literature
Project description:BackgroundBased on the "seed and soil" theory proposed by previous studies, we aimed to develop and validate a combined model of machine learning for predicting lymph node metastasis (LNM) in patients with peripheral lung adenocarcinoma (PLADC).MethodsRadiomics models were developed in a primary cohort of 390 patients (training cohort) with pathologically confirmed PLADC from January 2016 to August 2018. The patients were divided into the LNM (-) and LNM (+) groups. Thereafter, the patients were subdivided according to TNM stages N0, N1, N2, and N3. Radiomic features from unenhanced computed tomography (CT) were extracted. Radiomic signatures of the primary tumor (R1) and adjacent pleura (R2) were built as predictors of LNM. CT morphological features and clinical characteristics were compared between both groups. A combined model incorporating R1, R2, and CT morphological features, and clinical risk factors was developed by multivariate analysis. The combined model's performance was assessed by receiver operating characteristic (ROC) curve. An internal validation cohort containing 166 consecutive patients from September 2018 to November 2019 was also assessed.ResultsThirty-one radiomic features of R1 and R2 were significant predictors of LNM (all P < 0.05). Sex, smoking history, tumor size, density, air bronchogram, spiculation, lobulation, necrosis, pleural effusion, and pleural involvement also differed significantly between the groups (all P < 0.05). R1, R2, tumor size, and spiculation in the combined model were independent risk factors for predicting LNM in patients with PLADC, with area under the ROC curves (AUCs) of 0.897 and 0.883 in the training and validation cohorts, respectively. The combined model identified N0, N1, N2, and N3, with AUCs ranging from 0.691-0.927 in the training cohort and 0.700-0.951 in the validation cohort, respectively, thereby indicating good performance.ConclusionCT phenotypes of the primary tumor and adjacent pleura were significantly associated with LNM. A combined model incorporating radiomic signatures, CT morphological features, and clinical risk factors can assess LNM of patients with PLADC accurately and non-invasively.
| S-EPMC8180898 | biostudies-literature