Support vector machine-based nomogram predicts postoperative distant metastasis for patients with oesophageal squamous cell carcinoma

Ethical Dilemmas of Using Artificial Intelligence in Medicine

BACKGROUND

Artificial intelligence (AI) is considered the fourth industrial revolution that will change the evolution of humanity technically and relationally. Although the term has been around since 1956, it has only recently become apparent that AI can revolutionize technologies and has many applications in the medical field.

AREAS OF UNCERTAINTY

The ethical dilemmas posed by the use of AI in medicine revolve around issues related to informed consent, respect for confidentiality, protection of personal data, and last but not least the accuracy of the information it uses.

DATA SOURCES

A literature search was conducted through PubMed, MEDLINE, Plus, Scopus, and Web of Science (2015-2022) using combinations of keywords, including: AI, future in medicine, and machine learning plus ethical dilemma.

ETHICS AND THERAPEUTIC ADVANCES

The ethical analysis of the issues raised by AI used in medicine must mainly address nonmaleficence and beneficence, both in correlation with patient safety risks, ability versus inability to detect correct information from inadequate or even incorrect information. The development of AI tools that can support medical practice can increase people's access to medical information, to obtain a second opinion, for example, but it is also a source of concern among health care professionals and especially bioethicists about how confidentiality is maintained and how to maintain cybersecurity. Another major risk may be related to the dehumanization of the medical act, given that, at least for now, empathy and compassion are accessible only to human beings.

CONCLUSIONS

AI has not yet managed to overcome certain limits, lacking moral subjectivity, empathy, the level of critical thinking is still insufficient, but no matter who will practice preventive or curative medicine in the next period, they will not be able to ignore AI, which under human control can be an important tool in medical practice.

Development and External Validation of a Radiomics Model Derived from Preoperative Gadoxetic Acid-Enhanced MRI for Predicting Histopathologic Grade of Hepatocellular Carcinoma

Histopathologic grade of hepatocellular carcinoma (HCC) is an important predictor of early recurrence and poor prognosis after curative treatments. This study aims to develop a radiomics model based on preoperative gadoxetic acid-enhanced MRI for predicting HCC histopathologic grade and to validate its predictive performance in an independent external cohort. Clinical and imaging data of 403 consecutive HCC patients were retrospectively collected from two hospitals (265 and 138, respectively). Patients were categorized into poorly differentiated HCC and non-poorly differentiated HCC groups. A total of 851 radiomics features were extracted from the segmented tumor at the hepatobiliary phase images. Three classifiers, logistic regression (LR), support vector machine, and Adaboost were adopted for modeling. The areas under the curve of the three models were 0.70, 0.67, and 0.61, respectively, in the external test cohort. Alpha-fetoprotein (AFP) was the only significant clinicopathological variable associated with HCC grading (odds ratio: 2.75). When combining AFP, the LR+AFP model showed the best performance, with an AUC of 0.71 (95%CI: 0.59-0.82) in the external test cohort. A radiomics model based on gadoxetic acid-enhanced MRI was constructed in this study to discriminate HCC with different histopathologic grades. Its good performance indicates a promise in the preoperative prediction of HCC differentiation levels.

Colorectal cancer subtype identification from differential gene expression levels using minimalist deep learning

Background

Cancer molecular subtyping plays a critical role in individualized patient treatment. In previous studies, high-throughput gene expression signature-based methods have been proposed to identify cancer subtypes. Unfortunately, the existing ones suffer from the curse of dimensionality, data sparsity, and computational deficiency.

Methods

To address those problems, we propose a computational framework for colorectal cancer subtyping without any exploitation in model complexity and generality. A supervised learning framework based on deep learning (DeepCSD) is proposed to identify cancer subtypes. Specifically, based on the differentially expressed genes under cancer consensus molecular subtyping, we design a minimalist feed-forward neural network to capture the distinct molecular features in different cancer subtypes. To mitigate the overfitting phenomenon of deep learning as much as possible, L₁ and L₂ regularization and dropout layers are added.

Results

For demonstrating the effectiveness of DeepCSD, we compared it with other methods including Random Forest (RF), Deep forest (gcForest), support vector machine (SVM), XGBoost, and DeepCC on eight independent colorectal cancer datasets. The results reflect that DeepCSD can achieve superior performance over other algorithms. In addition, gene ontology enrichment and pathology analysis are conducted to reveal novel insights into the cancer subtype identification and characterization mechanisms.

Conclusions

DeepCSD considers all subtype-specific genes as input, which is pathologically necessary for its completeness. At the same time, DeepCSD shows remarkable robustness in handling cross-platform gene expression data, achieving similar performance on both training and test data without significant model overfitting or exploitation of model complexity.

Supplementary Information

The online version contains supplementary material available at (10.1186/s13040-022-00295-w).

A scoping review of artificial intelligence applications in thoracic surgery

OBJECTIVES

Machine learning (ML) has great potential, but there are few examples of its implementation improving outcomes. The thoracic surgeon must be aware of pertinent ML literature and how to evaluate this field for the safe translation to patient care. This scoping review provides an introduction to ML applications specific to the thoracic surgeon. We review current applications, limitations and future directions.

METHODS

A search of the PubMed database was conducted with inclusion requirements being the use of an ML algorithm to analyse patient information relevant to a thoracic surgeon and contain sufficient details on the data used, ML methods and results. Twenty-two papers met the criteria and were reviewed using a methodological quality rubric.

RESULTS

ML demonstrated enhanced preoperative test accuracy, earlier pathological diagnosis, therapies to maximize survival and predictions of adverse events and survival after surgery. However, only 4 performed external validation. One demonstrated improved patient outcomes, nearly all failed to perform model calibration and one addressed fairness and bias with most not generalizable to different populations. There was a considerable variation to allow for reproducibility.

CONCLUSIONS

There is promise but also challenges for ML in thoracic surgery. The transparency of data and algorithm design and the systemic bias on which models are dependent remain issues to be addressed. Although there has yet to be widespread use in thoracic surgery, it is essential thoracic surgeons be at the forefront of the eventual safe introduction of ML to the clinic and operating room.

Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology

Artificial intelligence (AI) is an umbrella term used to describe a cluster of interrelated fields. Machine learning (ML) refers to a model that learns from past data to predict future data. Medicine and particularly gastroenterology and hepatology, are data-rich fields with extensive data repositories, and therefore fruitful ground for AI/ML-based software applications. In this study, we comprehensively review the current applications of AI/ML-based models in these fields and the opportunities that arise from their application. Specifically, we refer to the applications of AI/ML-based models in prevention, diagnosis, management, and prognosis of gastrointestinal bleeding, inflammatory bowel diseases, gastrointestinal premalignant and malignant lesions, other nonmalignant gastrointestinal lesions and diseases, hepatitis B and C infection, chronic liver diseases, hepatocellular carcinoma, cholangiocarcinoma, and primary sclerosing cholangitis. At the same time, we identify the major challenges that restrain the widespread use of these models in healthcare in an effort to explore ways to overcome them. Notably, we elaborate on the concerns regarding intrinsic biases, data protection, cybersecurity, intellectual property, liability, ethical challenges, and transparency. Even at a slower pace than anticipated, AI is infiltrating the healthcare industry. AI in healthcare will become a reality, and every physician will have to engage with it by necessity.

Magnetic resonance radiomics signatures for predicting poorly differentiated hepatocellular carcinoma: A SQUIRE-compliant study

Radiomics contributes to the extraction of undetectable features with the naked eye from high-throughput quantitative images. In this study, 2 predictive models were constructed, which allowed recognition of poorly differentiated hepatocellular carcinoma (HCC). In addition, the effectiveness of the as-constructed signature was investigated in HCC patients.A retrospective study involving 188 patients (age, 29-85 years) enrolled from November 2010 to April 2018 was carried out. All patients were divided randomly into 2 cohorts, namely, the training cohort (n = 141) and the validation cohort (n = 47). The MRI images (DICOM) were collected from PACS before ablation; in addition, the radiomics features were extracted from the 3D tumor area on T1-weighted imaging (T1WI) scans, T2-weighted imaging (T2WI) scans, arterial images, portal images and delayed phase images. In total, 200 radiomics features were extracted. t test and Mann-Whitney U test were performed to exclude some radiomics signatures. Afterwards, a radiomics signature model was built through LASSO regression by RStudio Software. We constructed 2 support vector machine (SVM)-based models: 1 with a radiomics signature only (model 1) and 1 that integrated clinical and radiomics signatures (model 2). Then, the diagnostic performance of the radiomics signature was evaluated through receiver operating characteristic (ROC) analysis.The classification accuracy in the training and validation cohorts was 80.9% and 72.3%, respectively, for model 1. In the training cohort, the area under the ROC curve (AUC) was 0.623, while it was 0.576 in the validation cohort. The classification accuracy in the training and validation cohorts were 79.4% and 74.5%, respectively, for model 2. In the training cohort, the AUC was 0.721, while it was 0.681 in the validation cohort.The MRI-based radiomics signature and clinical model can distinguish HCC patients that belong in a low differentiation group from other patients, which helps in the performance of personal medical protocols.

Artificial intelligence-assisted esophageal cancer management: Now and future

Esophageal cancer poses diagnostic, therapeutic and economic burdens in high-risk regions. Artificial intelligence (AI) has been developed for diagnosis and outcome prediction using various features, including clinicopathologic, radiologic, and genetic variables, which can achieve inspiring results. One of the most recent tasks of AI is to use state-of-the-art deep learning technique to detect both early esophageal squamous cell carcinoma and esophageal adenocarcinoma in Barrett’s esophagus. In this review, we aim to provide a comprehensive overview of the ways in which AI may help physicians diagnose advanced cancer and make clinical decisions based on predicted outcomes, and combine the endoscopic images to detect precancerous lesions or early cancer. Pertinent studies conducted in recent two years have surged in numbers, with large datasets and external validation from multi-centers, and have partly achieved intriguing results of expert’s performance of AI in real time. Improved pre-trained computer-aided diagnosis algorithms in the future studies with larger training and external validation datasets, aiming at real-time video processing, are imperative to produce a diagnostic efficacy similar to or even superior to experienced endoscopists. Meanwhile, supervised randomized controlled trials in real clinical practice are highly essential for a solid conclusion, which meets patient-centered satisfaction. Notably, ethical and legal issues regarding the black-box nature of computer algorithms should be addressed, for both clinicians and regulators.

Comparison of the predictive outcomes for anti-tuberculosis drug-induced hepatotoxicity by different machine learning techniques

BACKGROUND

The study compared the predictive outcomes of artificial neural network, support vector machine and random forest on the occurrence of anti-tuberculosis drug-induced hepatotoxicity.

METHODS

The clinical and genomic data of patients treated with anti-tuberculosis drugs at Taipei Medical University-Wanfang Hospital were used as training sets, and those at Taipei Medical University-Shuang Ho Hospital served as test sets. Features were selected through a univariate risk factor analysis and literature evaluation. The accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve were calculated to compare the traditional, genomic, and combined models of the three techniques.

RESULTS

Nine models were created with 7 clinical factors and 4 genotypes. Artificial neural network with clinical and genomic factors exhibited the best performance, with an accuracy of 88.67%, a sensitivity of 80%, and a specificity of 90.4% for the test set. The area under the receiver operating characteristic curve of this best model reached 0.894 for training set and 0.898 for test set, which was significantly better than 0.801 for training set and 0.728 for test set by support vector machine and 0.724 for training set and 0.718 for test set by random forest.

CONCLUSIONS

Artificial neural network with clinical and genomic data can become a clinical useful tool in predicting anti-tuberculosis drug-induced hepatotoxicity. The machine learning technique can be an innovation to predict and prevent adverse drug reaction.

Artificial Intelligence in Medicine: Today and Tomorrow

Artificial intelligence-powered medical technologies are rapidly evolving into applicable solutions for clinical practice. Deep learning algorithms can deal with increasing amounts of data provided by wearables, smartphones, and other mobile monitoring sensors in different areas of medicine. Currently, only very specific settings in clinical practice benefit from the application of artificial intelligence, such as the detection of atrial fibrillation, epilepsy seizures, and hypoglycemia, or the diagnosis of disease based on histopathological examination or medical imaging. The implementation of augmented medicine is long-awaited by patients because it allows for a greater autonomy and a more personalized treatment, however, it is met with resistance from physicians which were not prepared for such an evolution of clinical practice. This phenomenon also creates the need to validate these modern tools with traditional clinical trials, debate the educational upgrade of the medical curriculum in light of digital medicine as well as ethical consideration of the ongoing connected monitoring. The aim of this paper is to discuss recent scientific literature and provide a perspective on the benefits, future opportunities and risks of established artificial intelligence applications in clinical practice on physicians, healthcare institutions, medical education, and bioethics.

Application of artificial intelligence in gastroenterology

Artificial intelligence (AI) using deep-learning (DL) has emerged as a breakthrough computer technology. By the era of big data, the accumulation of an enormous number of digital images and medical records drove the need for the utilization of AI to efficiently deal with these data, which have become fundamental resources for a machine to learn by itself. Among several DL models, the convolutional neural network showed outstanding performance in image analysis. In the field of gastroenterology, physicians handle large amounts of clinical data and various kinds of image devices such as endoscopy and ultrasound. AI has been applied in gastroenterology in terms of diagnosis, prognosis, and image analysis. However, potential inherent selection bias cannot be excluded in the form of retrospective study. Because overfitting and spectrum bias (class imbalance) have the possibility of overestimating the accuracy, external validation using unused datasets for model development, collected in a way that minimizes the spectrum bias, is mandatory. For robust verification, prospective studies with adequate inclusion/exclusion criteria, which represent the target populations, are needed. DL has its own lack of interpretability. Because interpretability is important in that it can provide safety measures, help to detect bias, and create social acceptance, further investigations should be performed.

Prediction models for patients with esophageal or gastric cancer: A systematic review and meta-analysis

Background

Clinical prediction models are increasingly used to predict outcomes such as survival in cancer patients. The aim of this study was threefold. First, to perform a systematic review to identify available clinical prediction models for patients with esophageal and/or gastric cancer. Second, to evaluate sources of bias in the included studies. Third, to investigate the predictive performance of the prediction models using meta-analysis.

Methods

MEDLINE, EMBASE, PsycINFO, CINAHL, and The Cochrane Library were searched for publications from the year 2000 onwards. Studies describing models predicting survival, adverse events and/or health-related quality of life (HRQoL) for esophageal or gastric cancer patients were included. Potential sources of bias were assessed and a meta-analysis, pooled per prediction model, was performed on the discriminative abilities (c-indices).

Results

A total of 61 studies were included (45 development and 16 validation studies), describing 47 prediction models. Most models predicted survival after a curative resection. Nearly 75% of the studies exhibited bias in at least 3 areas and model calibration was rarely reported. The meta-analysis showed that the averaged c-index of the models is fair (0.75) and ranges from 0.65 to 0.85.

Conclusion

Most available prediction models only focus on survival after a curative resection, which is only relevant to a limited patient population. Few models predicted adverse events after resection, and none focused on patient’s HRQoL, despite its relevance. Generally, the quality of reporting is poor and external model validation is limited. We conclude that there is a need for prediction models that better meet patients’ information needs, and provide information on both the benefits and harms of the various treatment options in terms of survival, adverse events and HRQoL.

Cyclin D1 expression predicts postoperative distant metastasis and survival in resectable esophageal squamous cell carcinoma

Purpose

We aim to identify esophageal squamous cell carcinoma patients with increased risk of postoperative metastases.

Results

A high level of cyclin D1 expression, together with poor tumor cell differentiation and advanced tumor stages, increased risk of postoperative metastasis and decreased distant metastasis-free survival in ESCC in both cohorts. A high level of cyclin D1 expression also decreased overall survival in the training cohort (p < 0.01) but not in the validation cohort (p = 0.415). However, when the two cohorts of patients were pooled to obtain a larger case number, a high level of cyclin D1 expression was again demonstrated as an independent predictor that decreased overall survival (p < 0.01).

Methods

We used data from two institutions to establish training (n = 319) and validation (n = 164) cohorts. Tissue microarrays were generated for immunohistochemical evaluation. The correlation among cyclin D1 expression, clinicopathologic variables, postoperative distant metastases, overall survival, and distant metastasis-free survival were analyzed. Multivariate analyses were used to test the independent factors impacting postoperative distant metastases and survival. The outcomes generated from the training cohort were then tested using the validation cohort and pooled dataset.

Conclusions

High level of cyclin D1 expression increased distant metastasis, decreased overall survival and distant metastasis-free survival in resectable ESCC. Using a combination of cyclin D1 expression, tumor cell differentiation grade, and tumor stages, identifying patients with increased risk of postoperative metastases becomes possible.

Challenges in the Setup of Large-scale Next-Generation Sequencing Analysis Workflows

While Next-Generation Sequencing (NGS) can now be considered an established analysis technology for research applications across the life sciences, the analysis workflows still require substantial bioinformatics expertise. Typical challenges include the appropriate selection of analytical software tools, the speedup of the overall procedure using HPC parallelization and acceleration technology, the development of automation strategies, data storage solutions and finally the development of methods for full exploitation of the analysis results across multiple experimental conditions. Recently, NGS has begun to expand into clinical environments, where it facilitates diagnostics enabling personalized therapeutic approaches, but is also accompanied by new technological, legal and ethical challenges. There are probably as many overall concepts for the analysis of the data as there are academic research institutions. Among these concepts are, for instance, complex IT architectures developed in-house, ready-to-use technologies installed on-site as well as comprehensive Everything as a Service (XaaS) solutions. In this mini-review, we summarize the key points to consider in the setup of the analysis architectures, mostly for scientific rather than diagnostic purposes, and provide an overview of the current state of the art and challenges of the field.

A nomogram based on phosphorylated AKT1 for predicting locoregional recurrence in patients with oesophageal squamous cell carcinoma

Background: The AKT signalling pathway controls survival and growth in many malignant tumours. However, the prognostic value of phosphorylated AKT1 (p-AKT1) for locoregional-progression free survival (LPFS) in oesophageal squamous cell carcinoma (ESCC) has not been established. Our aim was to develop a nomogram to predict local recurrence using p-AKT1 and main clinical characteristics in patients with thoracic ESCC undergoing radical three-field lymph node dissection.

Methods: Immunohistochemistry was performed to examine p-AKT1 expression in 181 thoracic ESCC patients. The Kaplan-Meier method was used to calculate LPFS. Cox regression analysis was also performed to evaluate prognostic factors. A nomogram comprising biological and clinical factors was established to predict LPFS.

Results: The 5-year LPFS rate was 63.9%. Multivariate analysis revealed that expression of p-AKT1 (p<0.001), pathologic N category (p=0.004) and number of lymph nodes retrieved (p=0.001) were independent prognostic factors for LPFS. Increased expression of p-AKT1 was associated with decreased LPFS in patients with ESCC. In addition, a nomogram was established based on all significant independent factors for locoregional recurrence risk. Harrell's c-index for predicting LPFS was 0.78.

Conclusion: Activation of AKT1 was associated with poor locoregional control in ESCC patients. The nomogram, based on p-AKT1 expression and clinically significant parameters, could be used as an accurate stratification model for predicting locoregional recurrence in patients with ESCC after radical resection.

Symbolic features and classification via support vector machine for predicting death in patients with Chagas disease

This paper introduces a technique for predicting death in patients with Chagas disease using features extracted from symbolic series and time-frequency indices of heart rate variability (HRV). The study included 150 patients: 15 patients who died and 135 who did not. The HRV series were obtained from 24-h Holter monitoring. Sequences of symbols from 5-min epochs from series of RR intervals were generated using symbolic dynamics and ordinal pattern statistics. Fourteen features were extracted from symbolic series and four derived from clinical aspects of patients. For classification, the 18 features from each epoch were used as inputs in a support vector machine (SVM) with a radial basis function (RBF) kernel. The results showed that it is possible to distinguish between the two classes, patients with Chagas disease who did or did not die, with a 95% accuracy rate. Therefore, we suggest that the use of new features based on symbolic series, coupled with classic time-frequency and clinical indices, proves to be a good predictor of death in patients with Chagas disease.

MIIP expression predicts outcomes of surgically resected esophageal squamous cell carcinomas

The migration and invasion inhibitory protein (MIIP) was shown to function as a tumor suppressor gene in gliomas by inhibiting tumor cell growth, migration, and invasion. However, its role and clinical significance in esophageal squamous cell carcinoma (ESCC) have not been elucidated. We investigated the correlation of MIIP expression and clinical outcome in a group of surgically resected ESCCs. Tissue microarrays constructed of 253 surgically resected ESCC primary tumors and paired paracancerous normal esophageal epithelia were used for MIIP evaluation by immunohistochemistry. The clinical and prognostic significance of MIIP expression was analyzed statistically. The expression of MIIP expression in cancer tissues was increased significantly in comparison with the paired paracancerous normal epithelia (P < 0.001). And, MIIP expression was associated with ESCC cells' differentiation (P < 0.001). By Kaplan-Meier analysis, patients with low MIIP expression exhibited significantly improved overall survival (OS, P = 0.039) and a tendency of improved disease-free survival (DFS, P = 0.086) than those with high MIIP expression. In addition, MIIP expression could distinguish OS or DFS of patients with tumors in stage T3-4 (P = 0.020, 0.028), N0 (P = 0.008, 0.032), and stage II (P = 0.004, 0.019), as well as at lower thoracic esophagus (P = 0.024, 0.090). Multivariate analysis showed that MIIP expression was an independent prognostic factor in ESCC OS and DFS. In conclusion, MIIP expressed higher in ESCCs than in paracancerous normal esophageal epithelia and was a positive, independent prognostic factor in resected ESCCs.

Vascular Endothelial Growth Factor is a Useful Predictor of Postoperative Distant Metastasis and Survival Prognosis in Esophageal Squamous Cell Carcinoma

BACKGROUND

The correlation between vascular endothelial growth factor (VEGF) and prognosis for patients with esophageal squamous cell carcinoma (ESCC) is controversial. This study investigated the correlation of VEGF expression with distant metastases and prognosis in resectable ESCC to improve the identification of patients with increased risk of postoperative metastases.

METHODS

Data from two centers were used to establish a training cohort (n = 319) and a validation cohort (n = 164). Tissue microarrays were generated for immunohistochemical evaluation. The correlations among VEGF expression, clinicopathologic variables, and prognosis were analyzed. The outcomes generated from the training cohort then were tested using the validation cohort. Multivariate analyses were used to test the independent factors that had an impact on postoperative distant metastases, overall survival (OS), and distant metastasis-free survival (DMFS).

RESULTS

Tumor stages, tumor cell grade, and VEGF expression were prognostic factors independent of ESCC outcome. The data indicated that high levels of VEGF expression were correlated with a high risk of postoperative distant metastases (p = 0.013) in the training cohort. This result was confirmed by the validation cohort (p < 0.01) and logistic regression analyses. A high level of VEGF expression also was correlated with poor DMFS (p = 0.011) and OS (p = 0.033) in the training cohort, which also was confirmed by the validation cohort and Cox regression analyses.

CONCLUSIONS

Expression of VEGF is a predictor of distant metastasis, OS, and DMFS in resectable ESCC patients. Using a combination of VEGF expression, tumor stages, and tumor cell grade, identification of patients with increased risk of postoperative metastases may become possible.

Contourlet textual features: improving the diagnosis of solitary pulmonary nodules in two dimensional CT images

Objective

To determine the value of contourlet textural features obtained from solitary pulmonary nodules in two dimensional CT images used in diagnoses of lung cancer.

Materials and Methods

A total of 6,299 CT images were acquired from 336 patients, with 1,454 benign pulmonary nodule images from 84 patients (50 male, 34 female) and 4,845 malignant from 252 patients (150 male, 102 female). Further to this, nineteen patient information categories, which included seven demographic parameters and twelve morphological features, were also collected. A contourlet was used to extract fourteen types of textural features. These were then used to establish three support vector machine models. One comprised a database constructed of nineteen collected patient information categories, another included contourlet textural features and the third one contained both sets of information. Ten-fold cross-validation was used to evaluate the diagnosis results for the three databases, with sensitivity, specificity, accuracy, the area under the curve (AUC), precision, Youden index, and F-measure were used as the assessment criteria. In addition, the synthetic minority over-sampling technique (SMOTE) was used to preprocess the unbalanced data.

Results

Using a database containing textural features and patient information, sensitivity, specificity, accuracy, AUC, precision, Youden index, and F-measure were: 0.95, 0.71, 0.89, 0.89, 0.92, 0.66, and 0.93 respectively. These results were higher than results derived using the database without textural features (0.82, 0.47, 0.74, 0.67, 0.84, 0.29, and 0.83 respectively) as well as the database comprising only textural features (0.81, 0.64, 0.67, 0.72, 0.88, 0.44, and 0.85 respectively). Using the SMOTE as a pre-processing procedure, new balanced database generated, including observations of 5,816 benign ROIs and 5,815 malignant ROIs, and accuracy was 0.93.

Conclusion

Our results indicate that the combined contourlet textural features of solitary pulmonary nodules in CT images with patient profile information could potentially improve the diagnosis of lung cancer.