A CT-based integrated model for preoperative prediction of occult lymph node metastasis in early tongue cancer.

Occult lymph node metastasis (OLNM) is an essential prognostic factor for early-stage tongue cancer (cT1-2N0M0) and a determinant of treatment decisions. Therefore, accurate prediction of OLNM can significantly impact the clinical management and outcomes of patients with tongue cancer. The aim of this study was to develop and validate a multiomics-based model to predict OLNM in patients with early-stage tongue cancer. The data of 125 patients diagnosed with early-stage tongue cancer (cT1-2N0M0) who underwent primary surgical treatment and elective neck dissection were retrospectively analyzed. A total of 100 patients were randomly assigned to the training set and 25 to the test set. The preoperative contrast-enhanced computed tomography (CT) and clinical data on these patients were collected. Radiomics features were extracted from the primary tumor as the region of interest (ROI) on CT images, and correlation analysis and the least absolute shrinkage and selection operator (LASSO) method were used to identify the most relevant features. A support vector machine (SVM) classifier was constructed and compared with other machine learning algorithms. With the same method, a clinical model was built and the peri-tumoral and intra-tumoral images were selected as the input for the deep learning model. The stacking ensemble technique was used to combine the multiple models. The predictive performance of the integrated model was evaluated for accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC-ROC), and compared with expert assessment. Internal validation was performed using a stratified five-fold cross-validation approach. Of the 125 patients, 41 (32.8%) showed OLNM on postoperative pathological examination. The integrated model achieved higher predictive performance compared with the individual models, with an accuracy of 84%, a sensitivity of 100%, a specificity of 76.5%, and an AUC-ROC of 0.949 (95% CI [0.870-1.000]). In addition, the performance of the integrated model surpassed that of younger doctors and was comparable to the evaluation of experienced doctors. The multiomics-based model can accurately predict OLNM in patients with early-stage tongue cancer, and may serve as a valuable decision-making tool to determine the appropriate treatment and avoid unnecessary neck surgery in patients without OLNM.

Han W ，Wang Y ，Li T ，Dong Y ，Dang Y ，He L ，Xu L ，Zhou Y ，Li Y ，Wang X ... -  《PeerJ》

Incidence rate of occult lymph node metastasis in clinical T(1-2)N(0)M(0) small cell lung cancer patients and radiomic prediction based on contrast-enhanced CT imaging: a multicenter study : Original research.

This study aimed to explore the incidence of occult lymph node metastasis (OLM) in clinical T1 - 2N0M0 (cT1 - 2N0M0) small cell lung cancer (SCLC) patients and develop machine learning prediction models using preoperative intratumoral and peritumoral contrast-enhanced CT-based radiomic data. By conducting a retrospective analysis involving 242 eligible patients from 4 centeres, we determined the incidence of OLM in cT1 - 2N0M0 SCLC patients. For each lesion, two ROIs were defined using the gross tumour volume (GTV) and peritumoral volume 15 mm around the tumour (PTV). By extracting a comprehensive set of 1595 enhanced CT-based radiomic features individually from the GTV and PTV, five models were constucted and we rigorously evaluated the model performance using various metrics, including the area under the curve (AUC), accuracy, sensitivity, specificity, calibration curve, and decision curve analysis (DCA). For enhanced clinical applicability, we formulated a nomogram that integrates clinical parameters and the rad_score (GTV and PTV). The initial investigation revealed a 33.9% OLM positivity rate in cT1 - 2N0M0 SCLC patients. Our combined model, which incorporates three radiomic features from the GTV and PTV, along with two clinical parameters (smoking status and shape), exhibited robust predictive capabilities. With a peak AUC value of 0.772 in the external validation cohort, the model outperformed the alternative models. The nomogram significantly enhanced diagnostic precision for radiologists and added substantial value to the clinical decision-making process for cT1 - 2N0M0 SCLC patients. The incidence of OLM in SCLC patients surpassed that in non-small cell lung cancer patients. The combined model demonstrated a notable generalization effect, effectively distinguishing between positive and negative OLMs in a noninvasive manner, thereby guiding individualized clinical decisions for patients with cT1 - 2N0M0 SCLC.

Jiang X ，Luo C ，Peng X ，Zhang J ，Yang L ，Liu LZ ，Cui YF ，Liu MW ，Miao L ，Jiang JM ，Ren JL ，Yang XT ，Li M ，Zhang L ... -  《RESPIRATORY RESEARCH》

Clinical value of dual-energy CT for predicting occult metastasis in central neck lymph nodes of papillary thyroid carcinoma.

To predict the probability of occult lymph node metastasis (OLNM) in the central cervical by analyzing the dual-energy computed tomography (DECT) parameters derived from papillary thyroid carcinoma (PTC). Data were retrospectively collected from patients with pathologically confirmed PTC who underwent arterial and venous phases of enhanced DECT with concurrent central neck lymph node dissection (CLND). Three clinical features, three shape-related features, and twenty-six DECT-derived parameters were measured. The univariate and multivariate analyses were applied to select the relevant parameters and develop the nomogram. A total 140 cases with negative diagnosis of cervical central lymph node metastases by preoperative evaluation were included, among which 88 patients with metastasis (OLNM +) and 52 patients without metastasis (OLNM -) were finally confirmed by pathology. (1) Anteroposterior/transverse diameter ratio (A/T) derived from the PTC focus had significant difference between the OLNM + and OLNM - groups (p < 0.05). (2) In the arterial phase, iodine concentration (ICarterial), normalized iodine concentration (NICarterial), effective atomic number (Zeff-arterial), electron density (EDarterial), and slope of energy curve (karterial) from PTC focus showed significant difference (all p < 0.05) between the two groups. In the venous phase, only the CT value under the 40 keV (HU40keVvenous) had differences (p < 0.05). (3) The nomogram was produced to predict the probability of OLNM, and the AUC, sensitivity, and specificity in the training and test cohort were 0.830, 75.0%, 76.9%, and 0.829, 65.9%, 84.6%, respectively. DECT parameters combined with shape-related feature derived from PTC might be used as predictors of OLNM in the central neck. Preoperative imaging evaluation combining shape-related features and dual-energy CT parameters could serve as a reference to discern occult lymph node metastasis in central neck during the surgically planning of papillary thyroid carcinoma. • Papillary thyroid carcinoma (PTC) patients may have occult lymph node metastasis (OLNM) in the central neck, which is extremely difficult to find by preoperative imaging examination. • Dual-energy CT quantitative evaluation has higher accuracy than conventional CT and can predicting OLNM in the central neck of PTC. • Dual-energy CT quantitative parameters and morphology of PTC can serve as a useful tool in predicting OLNM in the central neck, and as a guide for personalized treatment.

Zhao W ，Shen S ，Ke T ，Jiang J ，Wang Y ，Xie X ，Hu X ，Tang X ，Han D ，Chen J ... -  《-》

Development of a radiomics and machine learning model for predicting occult cervical lymph node metastasis in patients with tongue cancer.

We aimed to develop a predictive model for occult cervical lymph node metastasis in patients with tongue cancer using radiomics and machine learning from pretreatment contrast-enhanced computed tomography. This study included 161 patients with tongue cancer who received local treatment. Computed tomography images were transferred to a radiomics platform. The volume of interest was the total neck node level, including levels Ia, Ib, II, III, and IVa at the ipsilateral side, and each neck node level. The dimensionality of the radiomics features was reduced using least absolute shrinkage and selection operator logistic regression analysis. We compared 5 classifiers with or without the synthetic minority oversampling technique (SMOTE). For the analysis at the total neck node level, random forest with SMOTE was the best model, with an accuracy of 0.85 and an area under the curve score of 0.92. For the analysis at each neck node level, a support vector machine with SMOTE was the best model, with an accuracy of 0.96 and an area under the curve score of 0.98. Predictive models using radiomics and machine learning have potential as clinical decision support tools in the management of patients with tongue cancer for prediction of occult cervical lymph node metastasis.

Kubo K ，Kawahara D ，Murakami Y ，Takeuchi Y ，Katsuta T ，Imano N ，Nishibuchi I ，Saito A ，Konishi M ，Kakimoto N ，Yoshioka Y ，Toratani S ，Ono S ，Ueda T ，Takeno S ，Nagata Y ... -  《-》

Computed Tomography-Based Radiomics Model to Predict Central Cervical Lymph Node Metastases in Papillary Thyroid Carcinoma: A Multicenter Study.

This study aimed to develop a computed tomography (CT)-based radiomics model to predict central lymph node metastases (CLNM) preoperatively in patients with papillary thyroid carcinoma (PTC). In this retrospective study, 678 patients with PTC were enrolled from Yantai Yuhuangding Hot3spital (n=605) and the Affiliated Hospital of Binzhou Medical University (n=73) within August 2010 to December 2020. The patients were randomly divided into a training set (n=423), an internal test set (n=182), and an external test set (n=73). Radiomics features of each patient were extracted from preoperative plain scan and contrast-enhanced CT images (arterial and venous phases). One-way analysis of variance (ANOVA) and least absolute shrinkage and selection operator algorithm were used for feature selection. The K-nearest neighbor, logistics regression, decision tree, linear-support vector machine (linear-SVM), Gaussian-SVM, and polynomial-SVM algorithms were used to establish radiomics models for CLNM prediction. The clinical risk factors were selected by ANOVA and multivariate logistic regression. Incorporated with clinical risk factors, a combined radiomics model was established for the preoperative prediction of CLNM in patients with PTCs. The performance of the combined radiomics model was evaluated using the receiver operating characteristic (ROC) and calibration curves in the training and test sets. The clinical usefulness was evaluated through decision curve analysis (DCA). A total of 4227 radiomic features were extracted from the CT images of each patient, and 14 non-zero coefficient features associated with CLNM were selected. Four clinical variables (sex, age, tumor diameter, and CT-reported lymph node status) were significantly associated with CLNM. Linear-SVM led to the best prediction model, which incorporated radiomic features and clinical risk factors. Areas under the ROC curves of 0.747 (95% confidence interval [CI] 0.706-0.782), 0.710 (95% CI 0.634-0.786), and 0.764 (95% CI 0.654-0.875) were obtained in the training, internal, and external test sets, respectively. The linear-SVM algorithm also showed better sensitivity (0.702 [95% CI 0.600-0.790] vs. 0.477 [95% CI 0.409-0.545]) and accuracy (0.670 [95% CI 0.600-0.738] vs. 0.642 [95% CI 0.569-0.712]) than an experienced radiologist in the internal test set in the combined radiomics model. The calibration plot reflected a favorable agreement between the actual and estimated probabilities of CLNM. The DCA indicated the clinical usefulness of the combined radiomics model. The combined radiomics model is a non-invasive preoperative tool that incorporates radiomic features and clinical risk factors to predict CLNM in patients with PTC.

Li J ，Wu X ，Mao N ，Zheng G ，Zhang H ，Mou Y ，Jia C ，Mi J ，Song X ... -  《Frontiers in Endocrinology》