Lung cancer is responsible for more fatalities than any other cancer worldwide, with 1.76 million associated deaths reported in 2018. The key issue in the fight against this disease is the detection ...and diagnosis of all pulmonary nodules at an early stage. Artificial intelligence (AI) algorithms play a vital role in the automated detection, segmentation, and computer-aided diagnosis of malignant lesions. Among the existing algorithms, radiomics and deep-learning-based types appear to show the most promise. Radiomics is a growing field related to the extraction of a set of features from an image, which allows for automated classification of medical images into a predefined group. The process comprises a series of consecutive steps including image acquisition and pre-processing, segmentation of the desired region of interest, calculation of defined features, feature engineering, and construction of the classification model. The features calculated in this process are mainly shape features, as well as first- and higher-order texture features. To date, more than 100 features have been defined, although this number varies depending on the application. The greatest challenge in radiomics is building a cross-validated model based on a selected set of calculated features known as the radiomic signature. Numerous radiomic signatures have successfully been developed; however, reproducibility and clinical validity of the results obtained constitutes a considerable challenge of modern radiomics. Deep learning algorithms are another rapidly evolving technique and are recognized as a valuable tool in the field of medical image analysis for the detection, characterization, and assessment of lesions. Such an approach involves the design of artificial neural network architecture while upholding the goal of high classification accuracy. This paper illuminates the evolution and current state of artificial intelligence methods in lung imaging and the detection and diagnosis of pulmonary nodules, with a particular emphasis on radiomics and deep learning methods.
The video-assisted thoracoscopic surgery (VATS) approach has become a standard for the treatment of early-stage non-small-cell lung cancer (NSCLC). Recently published meta-analyses proved the benefit ...of VATS versus thoracotomy for overall survival (OS) and reduction of postoperative complications. The aim of this study was to compare early outcomes, long-term survival and rate of postoperative complications of the VATS approach versus thoracotomy.
In this retrospective cohort study, we analysed 982 individuals who underwent surgical resection for Stage I-IIA NSCLC between 2007 and 2015. Thirty- and 90-day mortality rates, length of hospital stay, rate of complications and OS were assessed. Propensity score matching was performed to compare 2 groups of patients. Two hundred and twenty-five individuals from the thoracotomy group and 225 patients from the VATS group were matched regarding pTNM, sex, the Charlson comorbidity index, type of resection and histological diagnosis.
In the propensity score-matched patient group, the VATS approach was associated with a significant benefit regarding OS (P = 0.042). Although no significant difference was observed (P = 0.14) in the 3-year survival rate of patients who had a thoracotomy versus VATS, the 5-year survival rate among patients with VATS increased significantly (61% vs 78%, P = 0.0081). The adjusted VATS-related hazard ratio for pTNM, sex and age was 0.63 (95% confidence interval 0.40-0.98). The VATS surgical approach also reduced both the rate of postoperative atelectasis (4% for VATS vs 10% for open thoracotomy; P = 0.0052) and the need for blood transfusions (4% vs 12% respectively, P = 0.0054) and significantly shortened the postoperative length of stay (mean 7.25 vs 9.34 days, P < 0.0001). No significant differences in the 30-day mortality (1% vs 1%, P = 0.66) and 90-day mortality (1% vs 1%, P = 0.48) rates were observed.
Patients with early-stage NSCLC operated on with VATS had fewer complications, shorter postoperative length of stay and better OS compared to those who were operated on by thoracotomy.
We propose a new method that employs transfer learning techniques to effectively correct sampling selection errors introduced by sparse annotations during supervised learning for automated tumor ...segmentation. The practicality of current learning-based automated tissue classification approaches is severely impeded by their dependency on manually segmented training databases that need to be recreated for each scenario of application, site, or acquisition setup. The comprehensive annotation of reference datasets can be highly labor-intensive, complex, and error-prone. The proposed method derives high-quality classifiers for the different tissue classes from sparse and unambiguous annotations and employs domain adaptation techniques for effectively correcting sampling selection errors introduced by the sparse sampling. The new approach is validated on labeled, multi-modal MR images of 19 patients with malignant gliomas and by comparative analysis on the BraTS 2013 challenge data sets. Compared to training on fully labeled data, we reduced the time for labeling and training by a factor greater than 70 and 180 respectively without sacrificing accuracy. This dramatically eases the establishment and constant extension of large annotated databases in various scenarios and imaging setups and thus represents an important step towards practical applicability of learning-based approaches in tissue classification.
•The value of MALDI-based profiling of serum lipidome as a tool for the early detection of lung cancer was investigated.•Several components with abundances different between early lung cancer ...patients and healthy high-risk smokers were revealed.•Discriminatory components allowed for the development of an effective cancer classifier with AUC=0.88.•Reduced level of a few lysophosphatidylcholines, including LPC18:2, was confirmed in samples from cancer patients.
The role of a low-dose computed tomography lung cancer screening remains a matter of controversy due to its low specificity and high costs. Screening complementation with blood-based biomarkers may allow a more efficient pre-selection of candidates for imaging tests or discrimination between benign and malignant chest abnormalities detected by low-dose computed tomography (LD-CT). We searched for a molecular signature based on a serum lipid profile distinguishing individuals with early lung cancer from healthy participants of the lung cancer screening program.
Blood samples were collected from 100 patients with early stage lung cancer (including 31 screen-detected cases) and from a matched group of 300 healthy participants of the lung cancer screening program. MALDI-ToF mass spectrometry was used to analyze the molecular profile of lipid-containing organic extract of serum samples in the 320–1000Da range.
Several components of the serum lipidome were detected, with abundances discriminating patients with early lung cancer from high-risk smokers. An effective cancer classifier was built with an area under the curve of 0.88. Corresponding negative predictive value was 98% and a positive predictive value was 42% when the classifier was tuned for maximum negative predictive value. Furthermore, the downregulation of a few lysophosphatidylcholines (LPC18:2, LPC18:1 and LPC18:0) in samples from cancer patients was confirmed using a complementary LC–MS approach (a reasonable cancer discrimination was possible based on LPC18:2 alone with 25% total weighted error of classification).
Lipid-based serum signature showed potential usefulness in discriminating early lung cancer patients from healthy individuals.
Nuclear Magnetic Resonance (NMR) spectroscopy is a popular medical diagnostic technique. NMR is also the favourite tool of chemists/biochemists to elucidate the molecular structure of small or big ...molecules; it is also a widely used tool in material science, in food science etc. In the case of medical diagnosis it allows for determining a metabolic composition of analysed tissue which may support the identification of tumour cells. Precession signal, that is a crucial part of MR phenomenon, contains distortions that must be filtered out before signal analysis. One of such distortions is phase error. Five popular algorithms: Automics, Shanon’s entropy minimization, Ernst’s method, Dispa and eDispa are presented and discussed. A novel adaptive tuning algorithm for Automics method was developed and numerically optimal solutions to automatic tuning of the other four algorithms were proposed. To validate the performance of the proposed techniques, two experiments were performed - the first one was done with the use of in silico generated data. For all presented methods, the fine tuning strategies significantly increased the correction accuracy. The highest improvement was observed for Automics algorithm, independently of noise level, with relative phase error dropping by average from 10.25% to 2.40% for low noise level and from 12.45% to 2.66% for high noise level. The second validation experiment, done with the use of phantom data, confirmed the in silico results. The obtained accuracy of the estimation of metabolite concentration was at 99.5%.
The proposed strategies for optimizing the phase correction algorithms significantly improve the accuracy of Nuclear Magnetic Resonance spectroscopy signal analysis.
Although there are several data analysis frameworks, both commercial and open source, supporting the detection of tumours on nuclear magnetic resonance (NMR) sequences, none of them gives ...satisfactory results in the case of low volume tumors. The majority of the frameworks require the detailed analysis of at least two sequences of the examined sample, or give sample specific thresholds distinguishing between the tumor and subtypes of healthy tissue. In this paper, we present a novel algorithm for the automated estimation of tumor specific cut-off values in the domain of the apparent diffusion coefficient (ADC). Once the cut-off characteristics for a particular type of tumor is estimated, their further usage on other independent samples does not require any calculations except for an easy thresholding. The proposed methodology is a combination of classical decomposition of ADC distribution into a Gaussian mixture model (GMM) with k-means clustering subsequently performed on the parameters of mixture model components, leading to the identification of ADC distributions for every tissue type. The maximum conditional probability criterion gives the final threshold estimate. The developed signal analysis pipeline was applied to the problem of Glioblastoma Multiforme grade IV brain tumor segmentation, with a dataset of 119 randomly chosen ADC maps and Leave-One-Out cross-validation procedure for population error estimate. Additionally, a comparison to standard GMM based tumor segmentation algorithms as well as to three other automated segmentation methods was performed and the obtained tumor regions were referenced to the segmentation done by a human expert. The results demonstrate the average MiMSeg similarity to the expert-curated decision measured by the Dice coefficient as equal to 89.2% (with 95% confidence interval 87.7 ÷ 90.6). The MiMSeg algorithm significantly outperforms other techniques in the case of small tumors (of volume less than 10%), obtaining similarity to the expert-curated decision at the level 86.7%, with 44.9% obtained by standard GMM, 79.0% by Self-Organising-Maps algorithm, 68.7% by Murakami’s algorithm, and 78.2% by Kang’s method.
Esophageal cancer is the sixth leading cause of cancer-related death worldwide, and is associated with a poor prognosis. Stromal tumor infiltrating lymphocytes (sTIL) and certain single nucleotide ...polymorphisms (SNPs) have been found to be predictive of patient survival. In this study, we explored the association between SNPs and sTIL regarding the predictability of disease-free survival in patients with esophageal squamous cell carcinoma (ESCC).
We collected 969 pathologically confirmed ESCC patients from 2010 to 2013 and genotyped 101 SNPs from 59 genes. The number of sTIL for each patient was determined using an automatic algorithm. A Kruskal-Wallis test was used to determine the association between genotype and sTIL. The genotypes and clinical factors related to survival were analyzed using a Kaplan-Meier curve, Cox proportional hazards model, and log-rank test.
The median age of the patients was 67 (42-85 years), there was a median follow-up of 851.5 days and 586 patients died. The univariable analysis showed that 10 of the 101 SNPs were associated with sTIL. Six SNPs were also associated with disease-free survival. A multivariable analysis revealed that sTIL, rs1801131, rs25487, and rs8030672 were independent prognostic markers for ESCC patients. The model combining SNPs, clinical characteristics and sTIL outperformed the model with clinical characteristics alone for predicting outcomes in ESCC patients.
We discovered 10 SNPs associated with sTIL in ESCC and we built a model of sTIL, SNPs and clinical characteristics with improved prediction of survival in ESCC patients.
We propose a new method that employs transfer learning techniques to effectively correct sampling selection errors introduced by sparse annotations during supervised learning for automated tumor ...segmentation. The practicality of current learning-based automated tissue classification approaches is severely impeded by their dependency on manually segmented training databases that need to be recreated for each scenario of application, site, or acquisition setup. The comprehensive annotation of reference datasets can be highly labor-intensive, complex, and error-prone. The proposed method derives high-quality classifiers for the different tissue classes from sparse and unambiguous annotations and employs domain adaptation techniques for effectively correcting sampling selection errors introduced by the sparse sampling. The new approach is validated on labeled, multi-modal MR images of 19 patients with malignant gliomas and by comparative analysis on the BraTS 2013 challenge data sets. Compared to training on fully labeled data, we reduced the time for labeling and training by a factor greater than 70 and 180 respectively without sacrificing accuracy. This dramatically eases the establishment and constant extension of large annotated databases in various scenarios and imaging setups and thus represents an important step towards practical applicability of learning-based approaches in tissue classification.
Nuclear Magnetic Resonance (NMR) is widely used technique in cancer diagnosis and treatment planning. It is employed to search for the high concentration regions of particular metabolites, which are ...directly related to the concentration of cancer cells. NMR signal maybe be characterized by a set of peaks which are representation of every distinct metabolite. Area under peak must be calculated in order to obtain proper information about metabolite amount. Commercially available software allows for the analysis of one-peak-in-time only. The proposed technique, based on Gaussian Mixture Model (GMM), allows for modeling all-peaks-in-time, and corrects after the neighboring peaks giving more accurate estimates of metabolite concentration. The resulting software processes NMR signal from the very beginning up to the final result, which is given in a form of so called metabolite map.
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