Comparative study of radiomics,tumor morphology,and clinicopathological factors in predicting overall survival of patients with rectal cancer before surgery

We compared the ability of a radiomics model, morphological imaging model, and clinicopathological risk model to predict 3-year overall survival (OS) in 206 patients with rectal cancer who underwent radical surgery and had magnetic resonance imaging, clinicopathological, and OS data available. The patients were randomized to a training cohort (n = 146) and a verification cohort (n = 60). Radiomics features were extracted from preoperative T2-weighted images, and a radiomics score model was constructed. Factors that were significant in the Cox multivariate analysis were used to construct the final morphological tumor model and clinicopathological model. A comprehensive model in the form of a line chart was established by combining the three models. Ten radiomics features significantly related to OS were selected to construct the radiomics feature model and calculate the radiomics score. In the morphological model, mesorectal extension depth and distance between the lower tumor margin and the anal margin were significant prognostic factors. N stage was the only significant clinicopathological factor. The comprehensive model combined with the above factors had the best prediction performance for OS. The C-index had a predictive performance of 0.872 (95% confidence interval [CI]: 0.832–0.912) in the training cohort and 0.944 (95% CI: 0.890–0.990) in the verification cohort, which was better than for any single model. The comprehensive model was divided into high-risk and low-risk groups. Kaplan-Meier curve analysis showed that all factors were significantly correlated with poor OS in the high-risk group. A comprehensive nomogram based on multi-model radiomics features can predict 3-year OS after rectal cancer surgery.     

A nomogram model integrating LI-RADS features and radiomics based on contrast-enhanced magnetic resonance imaging for predicting microvascular invasion in hepatocellular carcinoma falling the Milan criteria

PurposeTo establish and validate a nomogram model incorporating both liver imaging reporting and data system (LI-RADS) features and contrast enhanced magnetic resonance imaging (CEMRI)-based radiomics for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) falling the Milan criteria.MethodsIn total, 161 patients with 165 HCCs diagnosed with MVI (n = 99) or without MVI (n = 66) were assigned to a training and a test group. MRI LI-RADS characteristics and radiomics features selected by the LASSO algorithm were used to establish the MRI and Rad-score models, respectively, and the independent features were integrated to develop the nomogram model. The predictive ability of the nomogram was evaluated with receiver operating characteristic (ROC) curves.ResultsThe risk factors associated with MVI (P<0.05) were related to larger tumor size, nonsmooth margin, mosaic architecture, corona enhancement and higher Rad-score. The areas under the ROC curve (AUCs) of the MRI feature model for predicting MVI were 0.85 (95% CI: 0.78–0.92) and 0.85 (95% CI: 0.74–0.95), and those for the Rad-score were 0.82 (95% CI: 0.73–0.90) and 0.80 (95% CI: 0.67–0.93) in the training and test groups, respectively. The nomogram presented improved AUC values of 0.87 (95% CI: 0.81–0.94) in the training group and 0.89 (95% CI: 0.81–0.98) in the test group (P<0.05) for predicting MVI. The calibration curve and decision curve analysis demonstrated that the nomogram model had high goodness-of-fit and clinical benefits.ConclusionsThe nomogram model can effectively predict MVI in patients with HCC falling within the Milan criteria and serves as a valuable imaging biomarker for facilitating individualized decision-making.     

Development and validation of a novel model incorporating MRI-based radiomics signature with clinical biomarkers for distinguishing pancreatic carcinoma from mass-forming chronic pancreatitis

PurposeIt is difficult to make a clear differential diagnosis of pancreatic carcinoma (PC) and mass-forming chronic pancreatitis (MFCP) via conventional examinations. We aimed to develop a novel model incorporating an MRI-based radiomics signature with clinical biomarkers for distinguishing the two lesions.MethodsA total of 102 patients were retrospectively enrolled and randomly divided into the training and validation cohorts. Radiomics features were extracted from four different sequences. Individual imaging modality radiomics signature, multiparametric MRI (mp-MRI) radiomics signature, and a final mixed model based on mp-MRI and clinically independent risk factors were established to discriminate between PC and MFCP. The diagnostic performance of each model and model discrimination were assessed in both the training and validation cohorts.ResultsADC had the best predictive performance among the four individual radiomics models, but there were no significant differences between the pairs of models (all p > 0.05). Six potential radiomics features were finally selected from the 960 texture features to formulate the radiomics score (rad-score) of the mp-MRI model. In addition, the boxplot results of the distributions of rad-scores identified the rad-score as an independent predictive factor for the differentiation of PC and MFCP (p< 0.001). Notably, the nomogram integrating rad-score and clinically independent risk factors had a better diagnostic performance than the mp-MRI and clinical models. These results were further confirmed by the validation group.ConclusionThe mixed model was developed and preliminarily validated to distinguish PC from MFCP, which may benefit the formulation of treatment strategies and nonsurgical procedures.     

A 16-mRNA signature optimizes recurrence-free survival prediction of Stages II and III gastric cancer

High-throughput messenger RNA (mRNA) analysis has become a powerful tool for exploring tumor recurrence or metastasis mechanisms. Here, we constructed a signature to predict the recurrence risk of Stages II and III gastric cancer (GC) patients. A least absolute shrinkage and selection operator method Cox regression model was utilized to construct the signature. Using this method, a 16-mRNA signature was identified to be associated with the relapse-free survival of Stages II and III GCs in training dataset GSE62254 (n = 194). Then this signature was validated in an independent Gene Expression Omnibus cohort GSE26253 (n = 297) and a dataset of The Cancer Genome Atlas (TCGA; n = 235). This classifier could successfully screen out the high-risk Stages II and III GCs in the training cohort (hazard ratio [HR] = 40.91; 95% confidence interval [CI] = 5.58–299.7; p < .0001). Analysis in two independent validation cohorts yielded consistent results (GSE26253: HR = 1.69, 95% CI = 1.17–2.43,; p = .0045; TCGA: HR = 2.01, 95% CI = 1.13–3.56, p = .0146). Cox regression analyses revealed that the risk score derived from this signature was an independent risk factor in Stages II and III GCs. Besides, a nomogram was constructed to serve clinical practice. Through gene set variation analysis, we found several gene sets associated with chemotherapeutic drug resistance and tumor metastasis significantly enriched in high-risk patients. In summary, this 16-mRNA signature can be used as a powerful tool for prognostic evaluation and help clinicians identify high-risk patients.     

Prognostic Nomogram for Thoracic Esophageal Squamous Cell Carcinoma after Radical Esophagectomy

Nomogram has demonstrated its capability in individualized estimates of survival in diverse cancers. Here we retrospectively investigated 1195 patients with esophageal squamous-cell carcinoma (ESCC) who underwent radical esophagectomy at Zhejiang Cancer Hospital in Hangzhou, China. We randomly assigned two-thirds of the patients to a training cohort (n = 797) and one-third to a validation cohort (n = 398). Cox proportional hazards regression analyses were performed using the training cohort, and a nomogram was developed for predicting 3-year and 5-year overall survival rates. Multivariate analysis identified tumor length, surgical approach, number of examined lymph node, number of positive lymph node, extent of positive lymph node, grade, and depth of invasion as independent risk factors for survival. The discriminative ability of the nomogram was externally determined using the validation cohort, showing that the nomogram exhibited a sufficient level of discrimination according to the C-index (0.715, 95% CI 0.671–0.759). The C-index of the nomogram was significantly higher than that of the sixth edition (0.664, P-value<0.0001) and the seventh edition (0.696, P-value<0.0003) of the TNM classification. This study developed the first nomogram for ESCC, which can be applied in daily clinical practice for individualized survival prediction.     

Intravoxel incoherent motion radiomics nomogram for predicting tumor treatment responses in nasopharyngeal carcinoma

BackgroundIntravoxel incoherent motion (IVIM) plays an important role in predicting treatment responses in patient with nasopharyngeal carcinoma (NPC). The goal of this study was to develop and validate a radiomics nomogram based on IVIM parametric maps and clinical data for the prediction of treatment responses in NPC patients.MethodsEighty patients with biopsy-proven NPC were enrolled in this study. Sixty-two patients had complete responses and 18 patients had incomplete responses to treatment. Each patient received a multiple b-value diffusion-weighted imaging (DWI) examination before treatment. Radiomics features were extracted from IVIM parametric maps derived from DWI image. Feature selection was performed by the least absolute shrinkage and selection operator method. Radiomics signature was generated by support vector machine based on the selected features. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) values were used to evaluate the diagnostic performance of radiomics signature. A radiomics nomogram was established by integrating the radiomics signature and clinical data.ResultsThe radiomics signature showed good prognostic performance to predict treatment response in both training (AUC = 0.906, P<0.001) and testing (AUC = 0.850, P<0.001) cohorts. The radiomic nomogram established by integrating the radiomic signature with clinical data significantly outperformed clinical data alone (C-index, 0.929 vs 0.724; P<0.0001).ConclusionsThe IVIM-based radiomics nomogram provided high prognostic ability to treatment responses in patients with NPC. The IVIM-based radiomics signature has the potential to be a new biomarker in prediction of the treatment responses and may affect treatment strategies in patients with NPC.     

Prognostic and predictive role of a metabolic rate‐limiting enzyme signature in hepatocellular carcinoma

ObjectivesAbnormal expression of metabolic rate‐limiting enzymes drives the occurrence and progression of hepatocellular carcinoma (HCC). This study aimed to elucidate the comprehensive model of metabolic rate‐limiting enzymes associated with the prognosis of HCC.Materials and MethodsHCC animal model and TCGA project were used to screen out differentially expressed metabolic rate‐limiting enzyme. Cox regression, least absolute shrinkage and selection operation (LASSO) and experimentally verification were performed to identify metabolic rate‐limiting enzyme signature. The area under the receiver operating characteristic curve (AUC) and prognostic nomogram were used to assess the efficacy of the signature in the three HCC cohorts (TCGA training cohort, internal cohort and an independent validation cohort).ResultsA classifier based on three rate‐limiting enzymes (RRM1, UCK2 and G6PD) was conducted and serves as independent prognostic factor. This effect was further confirmed in an independent cohort, which indicated that the AUC at year 5 was 0.715 (95% CI: 0.653‐0.777) for clinical risk score, whereas it was significantly increased to 0.852 (95% CI: 0.798‐0.906) when combination of the clinical with signature risk score. Moreover, a comprehensive nomogram including the signature and clinicopathological aspects resulted in significantly predict the individual outcomes.ConclusionsOur results highlighted the prognostic value of rate‐limiting enzymes in HCC, which may be useful for accurate risk assessment in guiding clinical management and treatment decisions.     

A Novel Scoring System for the Risk of Papillary Thyroid Cancer Metastases in Lymph Nodes Posterior to the Right of the Recurrent Laryngeal Nerve

ObjectiveSome surgeons believe that dissection posterior to the right recurrent laryngeal nerve lymph node (PRRLN-LN) is unnecessary for the low metastasis rate and high complication risk. However, persistent metastatic lymph nodes may have a higher recurrence rate, surgical risk, and complications. Thus, it is important to distinguish patients who require PRRLN-LN dissection. To identify the risk factors for lymph nodes posterior to the right recurrent laryngeal nerve metastasis (LN-prRLN) and establish a scoring system to help determine whether PRRLN-LN dissection is required in patients with papillary thyroid carcinoma.Methods821 participants were randomly allocated to the development and validation cohorts in a 2:1 ratio. A nomogram-based predictive model for LN-prRLN was established based on the risk factors identified in the development cohort.ResultsLN-prRLN was diagnosed pathologically in 124 of 821 patients (15.1%) from the entire cohort. Multivariate analysis identified age (odds ratio [OR], 0.964; 95% CI, 0.945-0.983; P < .001), tumor size (OR, 1.536; 95% CI, 1.135-2.079; P = .005), extrathyroidal extension (OR 2.271, 95% CI, 1.368-3.770; P = .002), clinically involved right central compartment lymph node metastasis (OR 1.643, 95% CI, 1.055-2.559; P = .028), and right lateral lymph node metastasis (OR 4.271, 95% CI, 2.325-7.844; P < .001) as the predictors of LN-prRLN. A risk model was established and well validated. Calibration curves to evaluate the nomogram in both the development and validation cohorts revealed a concordance index of 0.756 ± 0.058 and 0.745 ± 0.042, respectively.ConclusionOur scoring system may be useful for helping the surgeons decide which patients should undergo the dissection of PRRLN-LN.     

2D and 3D CT Radiomics Features Prognostic Performance Comparison in Non-Small Cell Lung Cancer

OBJECTIVE: To compare 2D and 3D radiomics features prognostic performance differences in CT images of non-small cell lung cancer (NSCLC). METHOD: We enrolled 588 NSCLC patients from three independent cohorts. Two sets of 463 patients from two different institutes were used as the training cohort. The remaining cohort with 125 patients was set as the validation cohort. A total of 1014 radiomics features (507 2D features and 507 3D features correspondingly) were assessed. Based on the dichotomized survival data, 2D and 3D radiomics indicators were calculated for each patient by trained classifiers. We used the area under the receiver operating characteristic curve (AUC) to assess the prediction performance of trained classifiers (the support vector machine and logistic regression). Kaplan–Meier and Cox hazard survival analyses were also employed. Harrell's concordance index (C-Index) and Akaike's information criteria (AIC) were applied to assess the trained models. RESULTS: Radiomics indicators were built and compared by AUCs. In the training cohort, 2D_AUC = 0.653, 3D_AUC = 0.671. In the validation cohort, 2D_AUC = 0.755, 3D_AUC = 0.663. Both 2D and 3D trained indicators achieved significant results (P < .05) in the Kaplan-Meier analysis and Cox regression. In the validation cohort, 2D Cox model had a C-Index = 0.683 and AIC = 789.047; 3D Cox model obtained a C-Index = 0.632 and AIC = 799.409. CONCLUSION: Both 2D and 3D CT radiomics features have a certain prognostic ability in NSCLC, but 2D features showed better performance in our tests. Considering the cost of the radiomics features calculation, 2D features are more recommended for use in the current study.     

Development and verification of a nomogram for prediction of recurrence‐free survival in clear cell renal cell carcinoma

Nowadays, gene expression profiling has been widely used in screening out prognostic biomarkers in numerous kinds of carcinoma. Our studies attempt to construct a clinical nomogram which combines risk gene signature and clinical features for individual recurrent risk assessment and offer personalized managements for clear cell renal cell carcinoma. A total of 580 differentially expressed genes (DEGs) were identified via microarray. Functional analysis revealed that DEGs are of fundamental importance in ccRCC progression and metastasis. In our study, 338 ccRCC patients were retrospectively analysed and a risk gene signature which composed of 5 genes was obtained from a LASSO Cox regression model. Further analysis revealed that identified risk gene signature could usefully distinguish the patients with poor prognosis in training cohort (hazard ratio [HR] = 3.554, 95% confidence interval [CI] 2.261‐7.472, P < .0001, n = 107). Moreover, the prognostic value of this gene‐signature was independent of clinical features (P = .002). The efficacy of risk gene signature was verified in both internal and external cohorts. The area under receiver operating characteristic curve of this signature was 0.770, 0.765 and 0.774 in the training, testing and external validation cohorts, respectively. Finally, a nomogram was developed for clinicians and did well in the calibration plots. This nomogram based on risk gene signature and clinical features might provide a practical way for recurrence prediction and facilitating personalized managements of ccRCC patients after surgery.     

Prognostic value of a gene signature in clear cell renal cell carcinoma

Renal cancer is a common urogenital system malignance. Novel biomarkers could provide more and more critical information on tumor features and patients’ prognosis. Here, we performed an integrated analysis on the discovery set and established a three-gene signature to predict the prognosis for clear cell renal cell carcinoma (ccRCC). By constructing a LASSO Cox regression model, a 3-messenger RNA (3-mRNA) signature was identified. Based on the 3-mRNA signature, we divided patients into high- and low-risk groups, and validated this by using three other data sets. In the discovery set, this signature could successfully distinguish between the high- and low-risk patients (hazard ratio (HR), 2.152; 95% confidence interval (CI),1.509–3.069; p < 0.0001). Analysis of internal and two external validation sets yielded consistent results (internal: HR, 2.824; 95% CI, 1.601–4.98; p < 0.001; GSE29609: HR, 3.002; 95% CI, 1.113–8.094; p = 0.031; E-MTAB-3267: HR, 2.357; 95% CI, 1.243–4.468; p = 0.006). Time-dependent receiver operating characteristic (ROC) analysis indicated that the area under the ROC curve at 5 years was 0.66 both in the discovery and internal validation set, while the two external validation sets also suggested good performance of the 3-mRNA signature. Besides that, a nomogram was built and the calibration plots and decision curve analysis indicated the good performance and clinical utility of the nomogram. In conclusion, this 3-mRNA classifier proved to be a useful tool for prognostic evaluation and could facilitate personalized management of ccRCC patients.     

Development and validation of a nomogram to predict overall survival of T1 esophageal squamous cell carcinoma patients with lymph node metastasis

PurposeTo develop a nomogram for predicting the prognosis of T1 esophageal squamous cell carcinoma (ESCC) patients with positive lymph node.MethodsT1 ESCC patients with lymph node metastasis diagnosed between 2010 and 2015 were selected from the Surveillance, Epidemiology, and Final Results (SEER) database. The entire cohort was randomly divided in the ratio of 7:3 into a training group (n=457) and validation group (n=192), respectively. Prognostic factors were identified by univariate and multivariate Cox regression models. Harrell''s concordance index (C-index), receiver operating characteristic (ROC) curve, and calibration curve were used to evaluate the discrimination and calibration of the nomogram. The accuracy and clinical net benefit of the nomogram compared with the 7^th AJCC staging system were evaluated using net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA).ResultsThe nomogram consisted of eight factors: insurance, T stage, summary stage, primary site, radiation code, chemotherapy, surgery, and radiation sequence with surgery. In the training and validation cohorts, the AUCs exceeded 0.700, and the C-index scores were 0.749 and 0.751, respectively, indicating that the nomogram had good discrimination. The consistency between the survival probability predicted by the nomogram and the actual observed probability was indicated by the calibration curve in the training and validation cohorts. For NRI>0 and IDI>0, the predictive power of the nomogram was more accurate than that of the 7^th AJCC staging system. Furthermore, the DCA curve indicated that the nomogram achieved better clinical utility than the traditional system.ConclusionsUnlike the 7^th AJCC staging system, the developed and validated nomogram can help clinical staff to more accurately, personally and comprehensively predict the 1-year and 3-year OS probability of T1 ESCC patients with lymph node metastasis.     

Identification of a 4‐mRNA metastasis‐related prognostic signature for patients with breast cancer

Metastasis‐related mRNAs have showed great promise as prognostic biomarkers in various types of cancers. Therefore, we attempted to develop a metastasis‐associated gene signature to enhance prognostic prediction of breast cancer (BC) based on gene expression profiling. We firstly screened and identified 56 differentially expressed mRNAs by analysing BC tumour tissues with and without metastasis in the discovery cohort (GSE102484, n = 683). We then found 26 of these differentially expressed genes were associated with metastasis‐free survival (MFS) in the training set (GSE20685, n = 319). A metastasis‐associated gene signature built using a LASSO Cox regression model, which consisted of four mRNAs, can classify patients into high‐ and low‐risk groups in the training cohort. Patients with high‐risk scores in the training cohort had shorter MFS (hazard ratio [HR] 3.89, 95% CI 2.53‐5.98; P < 0.001), disease‐free survival (DFS) (HR 4.69, 2.93‐7.50; P < 0.001) and overall survival (HR 4.06, 2.56‐6.45; P < 0.001) than patients with low‐risk scores. The prognostic accuracy of mRNAs signature was validated in the two independent validation cohorts (GSE21653, n = 248; GSE31448, n = 246). We then developed a nomogram based on the mRNAs signature and clinical‐related risk factors (T stage and N stage) that predicted an individual's risk of disease, which can be assessed by calibration curves. Our study demonstrated that this 4‐mRNA signature might be a reliable and useful prognostic tool for DFS evaluation and will facilitate tailored therapy for BC patients at different risk of disease.     

Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy

ObjectivesBreast cancers show different regression patterns after neoadjuvant chemotherapy. Certain regression patterns are associated with more reliable margins in breast-conserving surgery. Our study aims to establish a nomogram based on radiomic features and clinicopathological factors to predict regression patterns in breast cancer patients.MethodsWe retrospectively reviewed 144 breast cancer patients who received neoadjuvant chemotherapy and underwent definitive surgery in our center from January 2016 to December 2019. Tumor regression patterns were categorized as type 1 (concentric regression + pCR) and type 2 (multifocal residues + SD + PD) based on pathological results. We extracted 1158 multidimensional features from 2 sequences of MRI images. After feature selection, machine learning was applied to construct a radiomic signature. Clinical characteristics were selected by backward stepwise selection. The combined prediction model was built based on both the radiomic signature and clinical factors. The predictive performance of the combined prediction model was evaluated.ResultsTwo radiomic features were selected for constructing the radiomic signature. Combined with two significant clinical characteristics, the combined prediction model showed excellent prediction performance, with an area under the receiver operating characteristic curve of 0.902 (95% confidence interval 0.8343–0.9701) in the primary cohort and 0.826 (95% confidence interval 0.6774–0.9753) in the validation cohort.ConclusionsOur study established a unique model combining a radiomic signature and clinicopathological factors to predict tumor regression patterns prior to the initiation of NAC. The early prediction of type 2 regression offers the opportunity to modify preoperative treatments or aids in determining surgical options.     

CT radiomics combined with clinical variables for predicting the overall survival of hepatocellular carcinoma patients after hepatectomy

PurposeTo establish a model for assessing the overall survival (OS) of the hepatocellular carcinoma (HCC) patients after hepatectomy based on the clinical and radiomics features.MethodsThis study recruited a total of 267 patients with HCC, which were randomly divided into the training (N = 188) and validation (N = 79) cohorts. In the training cohort, radiomic features were selected with the intra-reader and inter-reader correlation coefficient (ICC), Spearman's correlation coefficient, and the least absolute shrinkage and selection operator (LASSO). The radiomics signatures were built by COX regression analysis and compared the predictive potential in the different phases (arterial, portal, and double-phase) and regions of interest (tumor, peritumor 3 mm, peritumor 5 mm). A clinical-radiomics model (CR model) was established by combining the radiomics signatures and clinical risk factors. The validation cohort was used to validate the proposed models.ResultsA total of 267 patients 86 (45.74%) and 37 (46.84%) patients died in the training and validation cohorts, respectively. Among all the radiomics signatures, those based on the tumor and peritumor (5 mm) (AP-TP5-Signature) showed the best prognostic potential (training cohort 1–3 years AUC:0.774–0.837; validation cohort 1–3 years AUC:0.754–0.810). The CR model showed better discrimination, calibration, and clinical applicability as compared to the clinical model and radiomics features. In addition, the CR model could perform risk-stratification and also allowed for significant discrimination between the Kaplan-Meier curves in most of the subgroups.ConclusionsThe CR model could predict the OS of the HCC patients after hepatectomy.     

Radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of spread through air spaces in lung adenocarcinoma

To evaluate the clinical features and radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of the presence of spread through air spaces (STAS) in patients with lung adenocarcinoma. A total of 107 STAS-positive lung adenocarcinomas were selected and matched to 105 STAS-negative lung adenocarcinomas. Thin-slice CT imaging annotation and region of interest (ROI) segmentation were performed with semi-automatic in-house software. Radiomics features were extracted from all nodules and incremental distances of 5, 10, and 15 mm outside the lesion segmentation. A radiomics nomogram was established with multivariable logistic regression based on clinical and radiomics features. The maximum diameter of the solid component and mediastinal lymphadenectasis were selected as independent predictors of STAS. The radiomics nomogram of lung nodules showed especially good prediction in the training set [area under the curve (AUC), 0.98; 95% confidence interval (CI), 0.97–1.00] and test set (AUC, 0.99; 95% CI, 0.97–1.00). The radiomics nomogram of peritumoral regions also showed good prediction, but the fitting degrees of the calibration curves were not good. Our study may provide guidance for surgical methods in patients with lung adenocarcinoma.     

Quantitative Biomarkers for Prediction of Epidermal Growth Factor Receptor Mutation in Non-Small Cell Lung Cancer

OBJECTIVES: To predict epidermal growth factor receptor (EGFR) mutation status using quantitative radiomic biomarkers and representative clinical variables. METHODS: The study included 180 patients diagnosed as of non-small cell lung cancer (NSCLC) with their pre-therapy computed tomography (CT) scans. Using a radiomic method, 485 features that reflect the heterogeneity and phenotype of tumors were extracted. Afterwards, these radiomic features were used for predicting epidermal growth factor receptor (EGFR) mutation status by a least absolute shrinkage and selection operator (LASSO) based on multivariable logistic regression. As a result, we found that radiomic features have prognostic ability in EGFR mutation status prediction. In addition, we used radiomic nomogram and calibration curve to test the performance of the model. RESULTS: Multivariate analysis revealed that the radiomic features had the potential to build a prediction model for EGFR mutation. The area under the receiver operating characteristic curve (AUC) for the training cohort was 0.8618, and the AUC for the validation cohort was 0.8725, which were superior to prediction model that used clinical variables alone. CONCLUSION: Radiomic features are better predictors of EGFR mutation status than conventional semantic CT image features or clinical variables to help doctors to decide who need EGFR tyrosine kinase inhibitor (TKI) treatment.     

Lymph node ratio-based nomogram for prognosis evaluation and treatment optimization of non-metastatic oral cavity squamous cell carcinoma

BackgroundLymph node ratio (LNR) has been increasingly reported as a prognostic factor in oral cavity squamous cell carcinoma (OCSCC). This study aimed to develop and validate a prognostic nomogram integrating LNR and to further assess its role in guiding adjuvant therapy for OCSCC.MethodsA total of 8703 OCSCC patients treated primarily with surgery in the Surveillance, Epidemiology and End Results (SEER) database were retrieved and randomly divided into training and validation cohorts. The nomogram was created based on the factors identified by Cox model. The value of PORT and chemotherapy was respectively evaluated in each prognostic group according to nomogram-deduced individualized score.ResultsThe final nomogram included tumor site, grade, T stage, number of positive lymph nodes and LNR. Calibration plots demonstrated a good match between predicted and observed rates of overall survival (OS). The concordance indexes for training and validation cohorts were 0.720 (95% confidence interval (CI): 0.708, 0.732) and 0.711 (95% CI: 0.687, 0.735), both significantly higher than did TNM stage (p< 0.001). According to individualized nomogram score, patients were stratified into three subgroups with significantly distinct outcome. PORT presented survival benefit among medium- and high-risk groups whereas a near-detrimental effect in low-risk group. Chemotherapy was found to be beneficial only in high-risk group.ConclusionThis LNR-incorporated nomogram surpassed the conventional TNM stage in predicting prognosis of patients with non-metastatic OCSCC and identified sub-settings that could gain survival benefit from adjuvant thearpy.     

Deep learning for predicting subtype classification and survival of lung adenocarcinoma on computed tomography

ObjectivesThe subtype classification of lung adenocarcinoma is important for treatment decision. This study aimed to investigate the deep learning and radiomics networks for predicting histologic subtype classification and survival of lung adenocarcinoma diagnosed through computed tomography (CT) images.MethodsA dataset of 1222 patients with lung adenocarcinoma were retrospectively enrolled from three medical institutions. The anonymised preoperative CT images and pathological labels of atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive adenocarcinoma, invasive adenocarcinoma (IAC) with five predominant components were obtained. These pathological labels were divided into 2-category classification (IAC; non-IAC), 3-category and 8-category. We modeled the classification task of histological subtypes based on modified ResNet-34 deep learning network, radiomics strategies and deep radiomics combined algorithm. Then we established the prognostic models in lung adenocarcinoma patients with survival outcomes. The accuracy (ACC), area under ROC curves (AUCs) and C-index were primarily performed to evaluate the algorithms.ResultsThis study included a training set (n = 802) and two validation cohorts (internal, n = 196; external, n = 224). The ACC of deep radiomics algorithm in internal validation achieved 0.8776, 0.8061 in the 2-category, 3-category classification, respectively. Even in 8 classifications, the AUC ranged from 0.739 to 0.940 in internal set. Further, we constructed a prognosis model that C-index was 0.892(95% CI: 0.846–0.937) in internal validation set.ConclusionsThe automated deep radiomics based triage system has achieved the great performance in the subtype classification and survival predictability in patients with CT-detected lung adenocarcinoma nodules, providing the clinical guide for treatment strategies.     

A predictive model with a risk-classification system for cancer-specific survival in patients with primary osteosarcoma of long bone

BackgroundOsteosarcoma (OS), most commonly occurring in long bone, is a group of malignant tumors with high incidence in adolescents. No individualized model has been developed to predict the prognosis of primary long bone osteosarcoma (PLBOS) and the current AJCC TNM staging system lacks accuracy in prognosis prediction. We aimed to develop a nomogram based on the clinicopathological factors affecting the prognosis of PLBOS patients to help clinicians predict the cancer-specific survival (CSS) of PLBOS patients.MethodWe studied 1199 PLBOS patients from the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2015 and randomly divided the dataset into training and validation cohorts at a proportion of 7:3. Independent prognostic factors determined by stepwise multivariate Cox analysis were included in the nomogram and risk-stratification system. C-index, calibration curve, and decision curve analysis (DCA) were used to verify the performance of the nomogram.ResultsAge, Histological type, Surgery of primary site, Tumor size, Local extension, Regional lymph node (LN) invasion, and Distant metastasis were identified as independent prognostic factors. C-indexes, calibration curves and DCAs of the nomogram indicating that the nomogram had good discrimination and validity. The risk-stratification system based on the nomogram showed significant differences (P < 0.05) in CSS among different risk groups.ConclusionWe established a nomogram with risk-stratification system to predict CSS in PLBOS patients and demonstrated that the nomogram had good performance. This model can help clinicians evaluate prognoses, identify high-risk individuals, and give individualized treatment recommendation of PLBOS patients.