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Mathematical models for intraoperative prediction of metastasis to regional lymph nodes in patients with clinical stage I non-small cell lung cancer.

ABSTRACT: It remains challenging to determine the regions of metastasis to lymph nodes during operation for clinical stage I non-small cell lung cancer (NSCLC). This study aimed to establish intraoperative mathematical models with nomograms for predicting the hilar-intrapulmonary node metastasis (HNM) and the mediastinal node metastasis (MNM) in patients with clinical stage I NSCLC. The clinicopathological variables of 585 patients in a derivation cohort who underwent thoracoscopic lobectomy with complete lymph node dissection were retrospectively analyzed for their association with the HNM or the MNM. After analyzing the variables, we developed multivariable logistic models with nomograms to estimate the risk of lymph node metastasis in different regions. The predictive efficacy was then validated in a validation cohort of 418 patients. It was confirmed that carcinoembryonic antigen (>5.75 ng/mL), CYFRA211 (>2.85 ng/mL), the maximum diameter of tumor (>2.75 cm), tumor differentiation (grade III), bronchial mucosa and cartilage invasion, and vascular invasion were predictors of HNM, and carcinoembryonic antigen (>8.25 ng/mL), CYFRA211 (>2.95 ng/mL), the maximum diameter of tumor (>2.75 cm), tumor differentiation (grade III), bronchial mucosa and cartilage invasion, vascular invasion, and visceral pleural invasion were predictors of MNM. The validation of the prediction models based on the above results demonstrated good discriminatory power. Our predictive models are helpful in the decision-making process of specific therapeutic strategies for the regional lymph node metastasis in patients with clinical stage I NSCLC.

SUBMITTER: Zhou Y

PROVIDER: S-EPMC9592279 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Mathematical models for intraoperative prediction of metastasis to regional lymph nodes in patients with clinical stage I non-small cell lung cancer.

Zhou Yue Y Du Junjie J Ma Changhui C Zhao Fei F Li Hai H Ping Guoqiang G Wang Wei W Luo Jinhua J Chen Liang L Zhang Kai K Zhang Shijiang S

Medicine 20221001 42

It remains challenging to determine the regions of metastasis to lymph nodes during operation for clinical stage I non-small cell lung cancer (NSCLC). This study aimed to establish intraoperative mathematical models with nomograms for predicting the hilar-intrapulmonary node metastasis (HNM) and the mediastinal node metastasis (MNM) in patients with clinical stage I NSCLC. The clinicopathological variables of 585 patients in a derivation cohort who underwent thoracoscopic lobectomy with complete ...[more]

PMID: 36281188

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Project description:Objective:To explore the risk factors for postoperatively pathological lymph node metastasis in patients with clinical T2N0M0 stage prostate cancer (PCa). Methods:We retrospectively analyzed clinicopathological data of 316 patients with clinical T2 stage PCa and preoperative negative lymph nodes [LN(-)] indicated by imaging (cT2N0M0) between January 2014 and May 2019. Multivariate logistic regression analysis was performed to determine risk factors for postoperatively pathological pLN(+) in patients with cT2N0M0 stage PCa. Spearman correlation analysis was used to explore the relationship between tumor burden and Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) score. Results:A total of 45 patients (14.2%) were confirmed by postoperative pathology to have LN metastasis. Univariate analysis indicated that total prostate-specific antigen (tPSA), PI-RADS v2 score, postoperative Gleason grade group (GGG), intraductal carcinoma of the prostate (IDC-P), clinical T2 substaging, and postoperative pathological tumor burden were risk factors for pLN(+) in all patients. Multivariate analysis showed that tPSA and postoperative GGG were risk factors for pLN(+) in all patients. Univariate analysis revealed that tPSA, PIRADS v2 score, clinical T2 substaging, IDC-P, postoperative pathological tumor burden, and postoperative GGG were risk factors for pLN(+) in patients with GGG ? 3. Multivariate analysis suggested that tPSA, PI-RADS v2 score, clinical T2 substaging, postoperative pathological tumor burden, and GGG were risk factors for pLN (+) in patients with GGG ? 3. Spearman correlation analysis showed that PI-RADS v2 score was positively correlated with clinical T2 substaging and postoperative pathological tumor burden. Conclusion:There was a high risk of LN metastasis in patients with cT2 PCa if they had high preoperative tPSA or high postoperative GGG. Patients with cT2 PCa and GGG ? 3 had a high risk of LN metastasis if they had high PI-RADS v2 score, high preoperative clinical stage or high postoperative pathological tumor burden. PI-RADS v2 score predicted tumor burden well in patients with GGG ? 3.

Project description:Background and objectivesSentinel lymph node (SLN) biopsy is a standard procedure for patients with breast cancer and normal axilla on imaging. Positive SLNs on histological examination can lead to a subsequent surgery for axillary lymph node clearance (ALNC). Here we report a non-destructive technique based on autofluorescence (AF) imaging and Raman spectroscopy for intra-operative assessment of SLNs excised in breast cancer surgery.MethodsA microscope integrating AF imaging and Raman spectroscopy modules was built to allow scanning of lymph node biopsy samples. During AF-Raman measurements, AF imaging determined optimal sampling locations for Raman spectroscopy measurements. After optimisation of the AF image analysis and training of classification models based on data from 85 samples, the AF-Raman technique was tested on an independent set of 81 lymph nodes comprising 58 fixed and 23 fresh specimens. The sensitivity and specificity of AF-Raman were calculated using post-operative histology as a standard of reference.ResultsThe independent test set contained 66 negative lymph nodes and 15 positive lymph nodes according to the reference standard, collected from 78 patients. For this set of specimens, the area under the receiver operating characteristic (ROC) curve for the AF-Raman technique was 0.93 [0.83-0.98]. AF-Raman was then operated in a regime that maximised detection specificity, producing a 94% detection accuracy: 80% sensitivity and 97% specificity. The main confounders for SLN metastasis were areas rich in histiocytes clusters, for which only few Raman spectra had been included in the training dataset.DiscussionThis preliminary study indicates that with further development and extension of the training dataset by inclusion of additional Raman spectra of histiocytes clusters and capsule, the AF-Raman may become a promising technique for intra-operative assessment of SLNs. Intra-operative detection of positive biopsies could avoid second surgery for axillary clearance.

Dataset Information

Mathematical models for intraoperative prediction of metastasis to regional lymph nodes in patients with clinical stage I non-small cell lung cancer.

Publications

Mathematical models for intraoperative prediction of metastasis to regional lymph nodes in patients with clinical stage I non-small cell lung cancer.

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