Application of artificial intelligence in the diagnosis of multiple primary lung cancer
TL;DR: AI may represent a relevant diagnostic aid that shows more accurate and objective results in the diagnosis of multiple pulmonary nodules, reducing the time required for interpretation of results by directly displaying visual information to doctors and patients and together with the clinical conditions of MPLC patients, offering plans for follow-up and treatment that may be more beneficial and reasonable for patients.
Abstract: Artificial intelligence (AI) based on deep learning, convolutional neural networks and big data has been increasingly effective in the diagnosis and treatment of multiple primary pulmonary nodules. In comparison to previous imaging systems, AI measures more objective parameters such as three-dimensional (3D) volume, probability of malignant nodules, and possible pathological patterns, making the access to the properties of nodules more objective. In our retrospective study, a total of 53 patients with synchronous and metachronous multiple pulmonary nodules were enrolled of which 33 patients were confirmed by pathological tests to have primary binodules, and nine to have primary trinodules. A total of 15 patients had only one focus removed. The statistical results showed that the agreement in the AI diagnosis and postoperative pathological tests was 88.8% in identifying benign or malignant lesions. In addition, the probability of malignancy of benign lesions, preinvasive lesions (AAH, AIS) and invasive lesions (MIA, IA) was totally different (49.40±38.41% vs 80.22±13.55% vs 88.17±17.31%). The purpose of our study was to provide references for the future application of AI in the diagnosis and follow-up of multiple pulmonary nodules. AI may represent a relevant diagnostic aid that shows more accurate and objective results in the diagnosis of multiple pulmonary nodules, reducing the time required for interpretation of results by directly displaying visual information to doctors and patients and together with the clinical conditions of MPLC patients, offering plans for follow-up and treatment that may be more beneficial and reasonable for patients. Despite the great application potential in pneumosurgery, further research is needed to verify the accuracy and range of the application of AI.
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TL;DR: The development of artificial intelligence technology such as deep learning and machine learning in anticancer drug research is reviewed and the future of AI is looked forward to.
70 citations
TL;DR: The role of the immune system in the development of adenocarcinoma spectrum lesions has been discussed in this paper, where the authors examine the molecular changes that occur at each stage of spectrum lesion development, including the effects of the driver mutations.
64 citations
TL;DR: A survey of state-of-the-art deep-learning-based lung nodule screening and analysis techniques focusing on their performance and clinical applications is presented to help better understand the current performance, the limitation, and the future trends of lung nodules analysis.
40 citations
26 Mar 2020
TL;DR: Various machine learning classifiers techniques to classify available lung cancer data in UCI machine learning repository in to benign and malignant are analyzed and the proposed RBF classifier has resulted with a great accuracy of 81.25% and considered as the effective classifier technique for Lung cancer data prediction.
Abstract: Lung cancer generally occurs in both male and female due to uncontrollable growth of cells in the lungs. This causes a serious breathing problem in both inhale and exhale part of chest. Cigarette smoking and passive smoking are the principal contributor for the cause of lung cancer as per world health organization. The mortality rate due to lung cancer is increasing day by day in youths as well as in old persons as compared to other cancers. Even though the availability of high tech Medical facility for careful diagnosis and effective medical treatment, the mortality rate is not yet controlled up to a good extent. Therefore it is highly necessary to take early precautions at the initial stage such that it’s symptoms and effect can be found at early stage for better diagnosis. Machine learning now days has a great influence to health care sector because of its high computational capability for early prediction of the diseases with accurate data analysis. In our paper we have analyzed various machine learning classifiers techniques to classify available lung cancer data in UCI machine learning repository in to benign and malignant. The input data is prepossessed and converted in to binary form followed by use of some well known classifier technique in Weka tool to classify the data set in to cancerous and non cancerous. The comparison technique reveals that the proposed RBF classifier has resulted with a great accuracy of 81.25% and considered as the effective classifier technique for Lung cancer data prediction.
20 citations
TL;DR: In this paper, a review of the state-of-the-art in the field of Yarrowia lipolytica is presented, including the fatty acid metabolism, including fatty acid biosynthesis, transportation, and degradation.
Abstract: The microbial fermentation process has been used as an alternative pathway to the production of value-added natural products. Of the microorganisms, Yarrowia lipolytica, as an oleaginous platform, is able to produce fatty acid-derived biofuels and biochemicals. Nowadays, there are growing progresses on the production of value-added fatty acid-based bioproducts in Y. lipolytica. However, there are fewer reviews performing the metabolic engineering strategies and summarizing the current production of fatty acid-based bioproducts in Y. lipolytica. To this end, we briefly provide the fatty acid metabolism, including fatty acid biosynthesis, transportation, and degradation. Then, we introduce the various metabolic engineering strategies for increasing bioproduct accumulation in Y. lipolytica. Further, the advanced progress in the production of fatty acid-based bioproducts by Y. lipolytica, including nutraceuticals, biofuels, and biochemicals, is summarized. This review will provide attractive thoughts for researchers working in the field of Y. lipolytica.
16 citations
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TL;DR: The goal of the World Cancer Report 2014 was to present a timely update on the state of knowledge related to cancer statistics, causes, and mechanisms and how this knowledge might be used for cancer prevention and early detection.
1,666 citations
TL;DR: In CT screening for lung cancer, the detected nodule commonly is either only part-solid or nonsolid, but such a nodule is more likely to be malignant than a solid one, even when nodule size is taken into account.
Abstract: Objective In the Early Lung Cancer Action Project (ELCAP), we found not only solid but also part-solid and nonsolid nodules in patients at both baseline and repeat CT screening for lung cancer We report the frequency and significance of part-solid and nonsolid nodules in comparison with solid nodules Materials and methods We reviewed all instances of a positive finding in patients at baseline (from one to six noncalcified nodules) and annual repeat screenings (from one to six newly detected noncalcified nodules with interim growth) to classify each of the nodules as solid, part-solid, or nonsolid We defined a solid nodule as a nodule that completely obscures the entire lung parenchyma within it Part-solid nodules are those having sections that are solid in this sense, and nonsolid nodules are those with no solid parts Chi-square statistics were used to test for differences in the malignancy rates Results Among the 233 instances of positive results at baseline screening, 44 (19%) involved a part-solid or nonsolid largest nodule (16 part-solid and 28 nonsolid) Among these 44 cases of positive findings, malignancy was diagnosed in 15 (34%) as opposed to a 7% malignancy rate for solid nodules (p = 0000001) The malignancy rate for part-solid nodules was 63% (10/16), and the rate for nonsolid nodules was 18% (5/28) Even after standardizing for nodule size, the malignancy rate was significantly higher for part-solid nodules than for either solid ones (p = 0004) or nonsolid ones (p = 003) The malignancy type in the part-solid or nonsolid nodules was predominantly bronchioloalveolar carcinoma or adenocarcinoma with bronchioloalveolar features, contrasting with other subtypes of adenocarcinoma found in the solid nodules (p = 00001) At annual repeat screenings, only 30 instances of positive test results have been obtained; seven of these involved part-solid or nonsolid nodules Conclusion In CT screening for lung cancer, the detected nodule commonly is either only part-solid or nonsolid, but such a nodule is more likely to be malignant than a solid one, even when nodule size is taken into account
849 citations
TL;DR: Heavy cancer burden and its disparities between area, sex and age group pose a major challenge to public health in China.
Abstract: Background National Central Cancer Registry of China (NCCRC) updated nationwide cancer statistics using population-based cancer registry data in 2014 collected from all available cancer registries. Methods In 2017, 449 cancer registries submitted cancer registry data in 2014, among which 339 registries' data met the criteria of quality control and were included in analysis. These cancer registries covered 288,243,347 population, accounting for about 21.07% of the national population in 2014. Numbers of nationwide new cancer cases and deaths were estimated using calculated incidence and mortality rates and corresponding national population stratified by area, sex, age group and cancer type. The world Segi's population was applied for age-standardized rates. Results A total of 3,804,000 new cancer cases were diagnosed, the crude incidence rate was 278.07/100,000 (301.67/100,000 in males, 253.29/100,000 in females) and the age-standardized incidence rate by world standard population (ASIRW) was 186.53/100,000. Calculated age-standardized incidence rate was higher in urban areas than in rural areas (191.6/100,000 vs. 179.2/100,000). South China had the highest cancer incidence rate while Southwest China had the lowest incidence rate. Cancer incidence rate was higher in female for population between 20 to 54 years but was higher in male for population younger than 20 years or over 54 years. A total of 2,296,000 cancer deaths were reported, the crude mortality rate was 167.89/100,000 (207.24/100,000 in males, 126.54/100,000 in females) and the age-standardized mortality rate by world standard population (ASMRW) was 106.09/100,000. Calculated age-standardized mortality rate was higher in rural areas than in urban areas (110.3/100,000 vs. 102.5/100,000). East China had the highest cancer mortality rate while North China had the lowest mortality rate. The mortality rate in male was higher than that in female. Common cancer types and major causes of cancer death differed between age group and sex. Conclusions Heavy cancer burden and its disparities between area, sex and age group pose a major challenge to public health in China. Nationwide cancer registry plays a crucial role in cancer prevention and control.
631 citations
TL;DR: This review analyzes the incidence, mortality and prevalent trend of cancers in China, discusses the reasons behind this status, and reviews the potential countermeasures for cancer prevention and control in China.
Abstract: Cancer is the leading cause of human deaths in the world and produces serious economic burdens. On September 12, 2018, the academic journal A Cancer Journal for Clinicians published an article about the latest statistics of cancers worldwide, which provided a status report on the global burden of 36 cancers in 185 countries worldwide. Cancer has also become a serious public health problem in China and caused more and more attention of the government and people in recent years. This review analyzes the incidence, mortality and prevalent trend of cancers in China, discusses the reasons behind this status, and reviews the potential countermeasures for cancer prevention and control in China.
457 citations