Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.

An Automated Decision Support System to Analyze Malignancy Patterns of Breast Masses Employing Medically Relevant Features of Ultrasound Images

An automated computer-aided approach might aid radiologists in diagnosing breast cancer at a primary stage. This study proposes a novel decision support system to classify breast tumors into benign and malignant based on clinically important features, using ultrasound images. Nine handcrafted features, which align with the clinical markers used by radiologists, are extracted from the region of interest (ROI) of ultrasound images. To validate that these elected clinical markers have a significant impact on predicting the benign and malignant classes, ten machine learning (ML) models are experimented with resulting in test accuracies in the range of 96 to 99%. In addition, four feature selection techniques are explored where two features are eliminated according to the feature ranking score of each feature selection method. The Random Forest classifier is trained with the resultant four feature sets. Results indicate that even when eliminating only two features, the performance of the model is reduced for each feature selection technique. These experiments validate the efficiency and effectiveness of the clinically important features. To develop the decision support system, a probability density function (PDF) graph is generated for each feature in order to find a threshold range to distinguish benign and malignant tumors. Based on the threshold range of particular features, a decision support system is developed in such a way that if at least eight out of nine features are within the threshold range, the image will be denoted as true predicted. With this algorithm, a test accuracy of 99.38% and an F1 Score of 99.05% is achieved, which means that our decision support system outperforms all the previously trained ML models. Moreover, after calculating individual class-based test accuracies, for the benign class, a test accuracy of 99.31% has been attained where only three benign instances are misclassified out of 437 instances, and for the malignant class, a test accuracy of 99.52% has been attained where only one malignant instance is misclassified out of 210 instances. This system is robust, time-effective, and reliable as the radiologists' criteria are followed and may aid specialists in making a diagnosis.

Can illness representations be used to understand pain experienced in breast cancer survivorship-a cross-sectional study

BACKGROUND

Chronic pain is a recognised long-term consequence associated with breast cancer and its treatment; however, it is often underdiagnosed and undertreated. This study aims to explore the associations between illness representations, chronic cancer pain, and HRQoL in women who have survived breast cancer.

DESIGN AND METHODS

A cross-sectional online survey design was used. Data from 182 women who participated in the survey were analysed.

RESULTS

Chronic cancer pain was reported by 66% of respondents. Using the BPI-SF (score 0-10), participants were categorised into one of four pain categories: no chronic pain (BPI score 0; 34.1%), mild pain (BPI score 1-3; 35.7%), moderate pain (BPI score 4-6; 25.3%), and severe pain (BPI score 7 + ; 4.9%). The main findings were that having a strong illness identity (IPQ-R subscale which assesses the number of symptoms an individual attributes to their illness) was found to be a significant predictor of pain severity (OR 1.21 (95% CI 1.07-1.37), p = 0.003). Furthermore, HRQoL was significantly associated and predictive of pain severity (OR 0.97 (95% CI 0.95-0.99), p < 0.001). An additional finding was that not being in paid work was strongly associated with being in a higher pain category (OR 5.92 (95% CI 1.84-19.05), p = 0.003).

CONCLUSIONS

The findings of this study highlight the high prevalence of chronic cancer pain experienced by this population. Results show that dimensions of illness representations are associated with chronic cancer pain experienced by breast cancer survivors. Furthermore, having a strong illness identity and HRQoL were found to be independent, significant predictors of pain severity.

IMPLICATIONS FOR CANCER SURVIVORS

These findings demonstrate that chronic pain is an unmet clinical need experienced by breast cancer survivors, which is associated with reduced overall HRQoL. Therefore, consideration is needed regarding the assessment and management of chronic pain experienced by this population.

Breast cancer detection employing stacked ensemble model with convolutional features

Breast cancer is a major cause of female deaths, especially in underdeveloped countries. It can be treated if diagnosed early and chances of survival are high if treated appropriately and timely. For timely and accurate automated diagnosis, machine learning approaches tend to show better results than traditional methods, however, accuracy lacks the desired level. This study proposes the use of an ensemble model to provide accurate detection of breast cancer. The proposed model uses the random forest and support vector classifier along with automatic feature extraction using an optimized convolutional neural network (CNN). Extensive experiments are performed using the original, as well as, CNN-based features to analyze the performance of the deployed models. Experimental results involving the use of the Wisconsin dataset reveal that CNN-based features provide better results than the original features. It is observed that the proposed model achieves an accuracy of 99.99% for breast cancer detection. Performance comparison with existing state-of-the-art models is also carried out showing the superior performance of the proposed model.

Advancing Colorectal Cancer Diagnosis with AI-Powered Breathomics: Navigating Challenges and Future Directions

Early detection of colorectal cancer is crucial for improving outcomes and reducing mortality. While there is strong evidence of effectiveness, currently adopted screening methods present several shortcomings which negatively impact the detection of early stage carcinogenesis, including low uptake due to patient discomfort. As a result, developing novel, non-invasive alternatives is an important research priority. Recent advancements in the field of breathomics, the study of breath composition and analysis, have paved the way for new avenues for non-invasive cancer detection and effective monitoring. Harnessing the utility of Volatile Organic Compounds in exhaled breath, breathomics has the potential to disrupt colorectal cancer screening practices. Our goal is to outline key research efforts in this area focusing on machine learning methods used for the analysis of breathomics data, highlight challenges involved in artificial intelligence application in this context, and suggest possible future directions which are currently considered within the framework of the European project ONCOSCREEN.

Epidemiological Analysis of Global and Regional Lung Cancer Mortality: Based on 30-Year Data Analysis of Global Burden Disease Database

The objective of this study was to understand dynamic global and regional lung cancer fatality trends and provide a foundation for effective global lung cancer prevention and treatment strategies. Data from 1990 to 2019 were collected from the Global Burden Disease (GBD) database and statistical analysis was conducted using Excel 2010. Standardization was based on the GBD's world population structure, and the Average Annual Percentage Change (AAPC) was calculated using Joinpoint 4.8.0.1 software. Bayesian age-period-cohort analysis (BAPC) predicted global lung cancer mortality from 2020 to 2030. In 2019, worldwide lung cancer deaths reached 2,042,600, a 91.75% increase from 1990 (1,065,100). The standardized age-specific death rate in 2019 was 25.18 per 100,000. Males had a rate of 37.38 while females had 14.99. Men saw a decreasing trend while women experienced an increase. High- and medium-high-SDI regions had declining rates (-0.3 and -0.8 AAPCs) whereas middle-, low-, and low-middle-SDI regions had increased mortality rates (AAPC = 0.1, AAPC = 0.37, AAPC = 0.13). Several regions, including Oceania, South Asia, East Asia, Western Sub-Saharan Africa, Southeast Asia, and Eastern Sub-Saharan Africa, witnessed rising global lung cancer mortality rates (p < 0.01). The global standardized mortality rate for lung cancer is expected to decrease from 2020 to 2030, but predictions indicate increasing female mortality and decreasing male mortality. Despite overall declines, rising female mortality remains a concern. Effective measures are essential to reduce mortality rates and improve patients' quality of life in the global fight against lung cancer.

Potential roles of gut microbiota in metal mixture and bone mineral density and osteoporosis risk association: an epidemiologic study in Wuhan

Few studies have focused on the effects of multiple metal mixtures on bone health and the underlying mechanisms related to alterations in the gut microbiota. This study aimed to examine the potential roles of gut microbiota alterations in metal mixtures and their association with osteoporosis traits. Adults aged ≥ 55 years were recruited from two community healthcare centers in Wuhan City during 2016-2019. The plasma concentrations of six metals (zinc, iron, selenium, lead, cadmium, and arsenic) were measured using an inductively coupled plasma mass spectrometer. The k-means clustering method was employed to explore the exposure profiles of metal mixtures for all participants. 16S rRNA gene sequencing was used to profile the gut microbiota of participants. Combining these results with those of our previous study, we identified overlapping taxa and evaluated their potential roles. A total of 806 participants (516 females), with an average age of 67.36 years were included. The participants were grouped into three clusters using k-means clustering: Cluster 1 (n = 458), Cluster 2 (n = 199), and Cluster 3 (n = 149). The high-exposure group for iron, zinc, lead, and cadmium (Cluster 3) showed a negative association with lumbar spine 1-4 bone mineral density (BMD). A total of 201 individuals (121 females) underwent sequencing of the gut microbiota. Both alpha and beta diversities were statistically different among the three groups. Bacteroidaceae, Lachnospiraceae, Bifidobacteriaceae, Bacteroides, and Lachnospiraceae_incertae_sedis were identified as overlapping taxa associated with the metal mixtures and BMD. Interaction analysis revealed that Cluster 3 interacted with Bacteroidaceae/Bacteroides, resulting in a positive effect on LS1-4 BMD (β = 0.358 g/cm2, 95% CI: 0.047 to 0.669, P = 0.025). Our findings indicate associations between multiple metal mixtures and BMD as well as gut microbiota alterations. Exploring the interaction between metal mixtures and the gut microbiota provides new perspectives for the precise prevention and treatment of osteoporosis.

Recent advancements in machine learning and deep learning-based breast cancer detection using mammograms

OBJECTIVE

Mammogram-based automatic breast cancer detection has a primary role in accurate cancer diagnosis and treatment planning to save valuable lives. Mammography is one basic yet efficient test for screening breast cancer. Very few comprehensive surveys have been presented to briefly analyze methods for detecting breast cancer with mammograms. In this article, our objective is to give an overview of recent advancements in machine learning (ML) and deep learning (DL)-based breast cancer detection systems.

METHODS

We give a structured framework to categorize mammogram-based breast cancer detection techniques. Several publicly available mammogram databases and different performance measures are also mentioned.

RESULTS

After deliberate investigation, we find most of the works classify breast tumors either as normal-abnormal or malignant-benign rather than classifying them into three classes. Furthermore, DL-based features are more significant than hand-crafted features. However, transfer learning is preferred over others as it yields better performance in small datasets, unlike classical DL techniques.

SIGNIFICANCE AND CONCLUSION

In this article, we have made an attempt to give recent advancements in artificial intelligence (AI)-based breast cancer detection systems. Furthermore, a number of challenging issues and possible research directions are mentioned, which will help researchers in further scopes of research in this field.

A topographical analysis of encephalocele locations: generation of a standardised atlas and cluster analysis

OBJECTIVE

Encephaloceles are considered to result from defects in the developing skull through which meninges, and potentially brain tissue, herniate. The pathological mechanism underlying this process is incompletely understood. We aimed to describe the location of encephaloceles through the generation of a group atlas to determine whether they occur at random sites or clusters within distinct anatomical regions.

METHODS

Patients diagnosed with cranial encephaloceles or meningoceles were identified from a prospectively maintained database between 1984 and 2021. Images were transformed to atlas space using non-linear registration. The bone defect, encephalocele and herniated brain contents were manually segmented allowing for a 3-dimensional heat map of encephalocele locations to be generated. The centroids of the bone defects were clustered utilising a K-mean clustering machine learning algorithm in which the elbow method was used to identify the optimal number of clusters.

RESULTS

Of the 124 patients identified, 55 had volumetric imaging in the form of MRI (48/55) or CT (7/55) that could be used for atlas generation. Median encephalocele volume was 14,704 (IQR 3655-86,746) mm3 and the median surface area of the skull defect was 679 (IQR 374-765) mm2. Brain herniation into the encephalocele was found in 45% (25/55) with a median volume of 7433 (IQR 3123-14,237) mm3. Application of the elbow method revealed 3 discrete clusters: (1) anterior skull base (22%; 12/55), (2) parieto-occipital junction (45%; 25/55) and (3) peri-torcular (33%; 18/55). Cluster analysis revealed no correlation between the location of the encephalocele with gender (χ2 (2, n = 91) = 3.86, p = 0.15). Compared to expected population frequencies, encephaloceles were relatively more common in Black, Asian and Other compared to White ethnicities. A falcine sinus was identified in 51% (28/55) of cases. Falcine sinuses were more common (χ2 (2, n = 55) = 6.09, p = 0.05) whilst brain herniation was less common (χ2 (2, n = 55) = .16.24, p < 0.0003) in the parieto-occipital location.

CONCLUSION

This analysis revealed three predominant clusters for the location of encephaloceles, with the parieto-occipital junction being the most common. The stereotypic location of encephaloceles into anatomically distinct clusters and the coexistence of distinct venous malformations at certain sites suggests that their location is not random and raises the possibility of distinct pathogenic mechanisms unique to each of these regions.

Breast cancer diagnosis using the fast learning network algorithm

The use of machine learning (ML) and data mining algorithms in the diagnosis of breast cancer (BC) has recently received a lot of attention. The majority of these efforts, however, still require improvement since either they were not statistically evaluated or they were evaluated using insufficient assessment metrics, or both. One of the most recent and effective ML algorithms, fast learning network (FLN), may be seen as a reputable and efficient approach for classifying data; however, it has not been applied to the problem of BC diagnosis. Therefore, this study proposes the FLN algorithm in order to improve the accuracy of the BC diagnosis. The FLN algorithm has the capability to a) eliminate overfitting, b) solve the issues of both binary and multiclass classification, and c) perform like a kernel-based support vector machine with a structure of the neural network. In this study, two BC databases (Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC)) were used to assess the performance of the FLN algorithm. The results of the experiment demonstrated the great performance of the suggested FLN method, which achieved an average of accuracy 98.37%, precision 95.94%, recall 99.40%, F-measure 97.64%, G-mean 97.65%, MCC 96.44%, and specificity 97.85% using the WBCD, as well as achieved an average of accuracy 96.88%, precision 94.84%, recall 96.81%, F-measure 95.80%, G-mean 95.81%, MCC 93.35%, and specificity 96.96% using the WDBC database. This suggests that the FLN algorithm is a reliable classifier for diagnosing BC and may be useful for resolving other application-related problems in the healthcare sector.

An Unsupervised Machine Learning Approach to Evaluating the Association of Symptom Clusters With Adverse Outcomes Among Older Adults With Advanced Cancer: A Secondary Analysis of a Randomized Clinical Trial

Importance

Older adults with advanced cancer who have high pretreatment symptom severity often experience adverse events during cancer treatments. Unsupervised machine learning may help stratify patients into different risk groups.

Objective

To evaluate whether clusters identified from baseline patient-reported symptom severity were associated with adverse outcomes.

Design, Setting, and Participants

This secondary analysis of the Geriatric Assessment Intervention for Reducing Toxicity in Older Patients With Advanced Cancer (GAP70+) Trial (2014-2019) included patients who completed the National Cancer Institute Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) before starting a new cancer treatment regimen and received care at community oncology sites across the United States. An unsupervised machine learning algorithm (k-means with Euclidean distance) clustered patients based on similarities of baseline symptom severities. Clustering variables included severity items of 24 PRO-CTCAE symptoms (range, 0-4; corresponding to none, mild, moderate, severe, and very severe). Total severity score was calculated as the sum of 24 items (range, 0-96). Whether the clusters were associated with unplanned hospitalization, death, and toxic effects was then examined. Analyses were conducted in January and February 2022.

Exposures

Symptom severity.

Main Outcomes and Measures

Unplanned hospitalization over 3 months (primary), all-cause mortality over 1 year, and any clinician-rated grade 3 to 5 toxic effect over 3 months.

Results

Of 718 enrolled patients, 706 completed baseline PRO-CTCAE and were included (mean [SD] age, 77.2 [5.5] years, 401 [56.8%] male patients; 51 [7.2%] Black and 619 [87.8%] non-Hispanic White patients; 245 [34.7%] with gastrointestinal cancer; 175 [24.8%] with lung cancer; mean [SD] impaired Geriatric Assessment domains, 4.5 [1.6]). The algorithm classified 310 (43.9%), 295 (41.8%), and 101 (14.3%) into low-, medium-, and high-severity clusters (within-cluster mean [SD] severity scores: low, 6.3 [3.4]; moderate, 16.6 [4.3]; high, 29.8 [7.8]; P < .001). Controlling for sociodemographic variables, clinical factors, study group, and practice site, compared with patients in the low-severity cluster, those in the moderate-severity cluster were more likely to experience hospitalization (risk ratio, 1.36; 95% CI, 1.01-1.84; P = .046). Moderate- and high-severity clusters were associated with a higher risk of death (moderate: hazard ratio, 1.31; 95% CI, 1.01-1.69; P = .04; high: hazard ratio, 2.00; 95% CI, 1.43-2.78; P < .001), but not toxic effects.

Conclusions and Relevance

In this study, unsupervised machine learning partitioned patients into distinct symptom severity clusters; patients with higher pretreatment severity were more likely to experience hospitalization and death.

Trial Registration

ClinicalTrials.gov Identifier: NCT02054741.

Breast tumor localization and segmentation using machine learning techniques: Overview of datasets, findings, and methods

The Global Cancer Statistics 2020 reported breast cancer (BC) as the most common diagnosis of cancer type. Therefore, early detection of such type of cancer would reduce the risk of death from it. Breast imaging techniques are one of the most frequently used techniques to detect the position of cancerous cells or suspicious lesions. Computer-aided diagnosis (CAD) is a particular generation of computer systems that assist experts in detecting medical image abnormalities. In the last decades, CAD has applied deep learning (DL) and machine learning approaches to perform complex medical tasks in the computer vision area and improve the ability to make decisions for doctors and radiologists. The most popular and widely used technique of image processing in CAD systems is segmentation which consists of extracting the region of interest (ROI) through various techniques. This research provides a detailed description of the main categories of segmentation procedures which are classified into three classes: supervised, unsupervised, and DL. The main aim of this work is to provide an overview of each of these techniques and discuss their pros and cons. This will help researchers better understand these techniques and assist them in choosing the appropriate method for a given use case.

Impact of coronavirus disease 2019 on lung cancer patients: A meta-analysis

Background

The coronavirus disease 2019 (COVID-19) pandemic poses a great challenge to the treatment of lung cancer patients.

Materials and methods

The PubMed, Embase, and Web of Science databases were searched for studies published before March 15, 2022, and Stata 14.0 software was used to perform a meta-analysis with a random-effects model. The odds ratio (OR) along with the corresponding 95% confidence interval (CI) was reported.

Results

Our meta-analysis included 80 articles with 318,352 patients involved. The proportion of lung cancer patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was 2.4% (95% CI: 0.02-0.03) prior to the Omicron variant outbreak. Among COVID-19 patients, those with lung cancer showed a higher mortality rate than those with other types of malignant solid tumors (OR = 1.82, 95% CI: 1.61-2.06) and non-cancer patients (OR = 4.67, 95% CI: 3.61-6.05); however, no significant difference was observed in the mortality rate between patients with lung cancer and those with hematologic malignancies (OR = 1.07, 95% CI: 0.85-1.33). SARS-CoV-2 infection significantly increased the mortality rate in lung cancer patients (OR = 8.94, 95% CI: 6.50-12.31). By contrast, the all-cause mortality rate in lung cancer patients (OR = 1.04, 95% CI: 0.69-1.57) and the proportion of patients diagnosed with advanced lung cancer (OR = 1.04, 95% CI: 0.85-1.27) did not significantly change before and after the pandemic.

Conclusions

More attention should be paid on improving the health of lung cancer patients during the COVID-19 pandemic.

Breast Cancer Detection Using Convoluted Features and Ensemble Machine Learning Algorithm

Breast cancer is a common cause of female mortality in developing countries. Screening and early diagnosis can play an important role in the prevention and treatment of these cancers. This study proposes an ensemble learning-based voting classifier that combines the logistic regression and stochastic gradient descent classifier with deep convoluted features for the accurate detection of cancerous patients. Deep convoluted features are extracted from the microscopic features and fed to the ensemble voting classifier. This idea provides an optimized framework that accurately classifies malignant and benign tumors with improved accuracy. Results obtained using the voting classifier with convoluted features demonstrate that the highest classification accuracy of 100% is achieved. The proposed approach revealed the accuracy enhancement in comparison with the state-of-the-art approaches.

An Effective Ensemble Machine Learning Approach to Classify Breast Cancer Based on Feature Selection and Lesion Segmentation Using Preprocessed Mammograms

Background: Breast cancer, behind skin cancer, is the second most frequent malignancy among women, initiated by an unregulated cell division in breast tissues. Although early mammogram screening and treatment result in decreased mortality, differentiating cancer cells from surrounding tissues are often fallible, resulting in fallacious diagnosis. Method: The mammography dataset is used to categorize breast cancer into four classes with low computational complexity, introducing a feature extraction-based approach with machine learning (ML) algorithms. After artefact removal and the preprocessing of the mammograms, the dataset is augmented with seven augmentation techniques. The region of interest (ROI) is extracted by employing several algorithms including a dynamic thresholding method. Sixteen geometrical features are extracted from the ROI while eleven ML algorithms are investigated with these features. Three ensemble models are generated from these ML models employing the stacking method where the first ensemble model is built by stacking ML models with an accuracy of over 90% and the accuracy thresholds for generating the rest of the ensemble models are >95% and >96. Five feature selection methods with fourteen configurations are applied to notch up the performance. Results: The Random Forest Importance algorithm, with a threshold of 0.045, produces 10 features that acquired the highest performance with 98.05% test accuracy by stacking Random Forest and XGB classifier, having a higher than >96% accuracy. Furthermore, with K-fold cross-validation, consistent performance is observed across all K values ranging from 3−30. Moreover, the proposed strategy combining image processing, feature extraction and ML has a proven high accuracy in classifying breast cancer.

Adaptive radiation therapy (art) for patients with limited-stage small cell lung cancer (LS-SCLC): A dosimetric evaluation

Background

Adaptive radiation therapy (ART) refers to redesigning of radiation therapy (RT) treatment plans with respect to dynamic changes in tumor size and location throughout the treatment course. In this study, we performed a comparative volumetric and dosimetric analysis to investigate the impact of ART for patients with limited-stage small cell lung cancer (LS-SCLC).

Methods

Twenty-four patients with LS-SCLC receiving ART and concomitant chemotherapy were included in the study. ART was performed by replanning of patients based on a mid-treatment computed tomography (CT)-simulation which was routinely scheduled for all patients 20-25 days after the initial CT-simulation. While the first 15 RT fractions were planned using the initial CT-simulation images, the latter 15 RT fractions were planned using the mid-treatment CT-simulation images acquired 20-25 days after the initial CT-simulation. In order to document the impact of ART, target and critical organ dose-volume parameters acquired from this adaptive radiation treatment planning (RTP) were compared with the RTP based solely on the initial CT-simulation to deliver the whole RT dose of 60 Gy.

Results

Statistically significant reduction was detected in gross tumor volume (GTV) and planning target volume (PTV) during the conventionally fractionated RT course along with statistically significant reduction in critical organ doses with incorporation of ART.

Conclusion

One-third of the patients in our study who were otherwise ineligible for curative intent RT due to violation of critical organ dose constraints could be treated with full dose irradiation by use of ART. Our results suggest significant benefit of ART for patients with LS-SCLC.

Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies

Recently, anomaly scores have been formulated using reconstruction loss of the adversarially learned generators and/or classification loss of discriminators. Unavailability of anomaly examples in the training data makes optimization of such networks challenging. Attributed to the adversarial training, performance of such models fluctuates drastically with each training step, making it difficult to halt the training at an optimal point. In the current study, we propose a robust anomaly detection framework that overcomes such instability by transforming the fundamental role of the discriminator from identifying real vs. fake data to distinguishing good vs. bad quality reconstructions. For this purpose, we propose a method that utilizes the current state as well as an old state of the same generator to create good and bad quality reconstruction examples. The discriminator is trained on these examples to detect the subtle distortions that are often present in the reconstructions of anomalous data. In addition, we propose an efficient generic criterion to stop the training of our model, ensuring elevated performance. Extensive experiments performed on six datasets across multiple domains including image and video based anomaly detection, medical diagnosis, and network security, have demonstrated excellent performance of our approach.

The Association Between Genetic Polymorphisms of Transporter Genes and Prognosis of Platinum-Based Chemotherapy in Lung Cancer Patients

Objective

Platinum-based chemotherapy is the first-line treatment of lung cancer. However, different individual and genetic variation effect therapy for lung cancer. The purpose of this study was to evaluate the association between transport genes genetic polymorphisms and the prognosis of platinum-based chemotherapy in lung cancer patients.

Methods

A series of 593 patients with treatment of platinum-based chemotherapy were recruited for this study. A total of 21 single-nucleotide polymorphisms in nine transporter genes were selected to investigate their associations with platinum-based chemotherapy prognosis.

Results

Patients with ABCG2 rs1448784 CC genotype had a significantly shorter PFS than CT or TT genotypes (Additive model: HR = 1.54, 95% CI = 1.02–2.35, P = 0.040). In stratification analysis, SLC22A2 rs316003, SLC2A1 rs4658 were related to PFS and AQP9 rs1867380, SLC2A1 rs3820589, SLC22A2 rs316003 indicated were related to OS of platinum-based chemotherapy prognosis.

Conclusion

Genetic polymorphisms of rs1448784 in ABCG2 might be potential clinical marker for predicting the prognosis of lung cancer patients treated with platinum-based chemotherapy.

New Benzimidazoles Targeting Breast Cancer: Synthesis, Pin1 Inhibition, 2D NMR Binding, and Computational Studies

Benzimidazole derivatives are known to be key players in the development of novel anticancer agents. Herein, we aimed to synthesize novel derivatives to target breast cancer. A new series of benzimidazole derivatives conjugated with either six- and five-membered heterocyclic ring or pyrazanobenzimidazoles and pyridobenzimidazole linkers were synthesized yielding compounds 5–8 and 10–14, respectively. Structure elucidation of the newly synthesized compounds was achieved through microanalytical analyses and different spectroscopic techniques (¹H, ¹³C-APT and ¹H–¹H COSY and IR) in addition to mass spectrometry. A biological study for the newly synthesized compounds was performed against breast cancer cell lines (MCF-7), and the most active compounds were further subjected to normal Human lung fibroblast (WI38) which indicates their safety. It was found that most of them exhibit high cytotoxic activity against breast cancer (MCF-7) and low cytotoxic activity against normal (WI38) cell lines. Compounds 5, 8, and 12, which possess the highest anti-breast cancer activity against the MCF-7 cell line, were selected for Pin1 inhibition assay using tannic acid as a reference drug control. Compound 8 was examined for its effect on cell cycle progression and its ability to apoptosis induction. Mechanistic evaluation of apoptosis induction was demonstrated by triggering intrinsic apoptotic pathways via inducing ROS accumulation, increasing Bax, decreasing Bcl-2, and activation of caspases 6, 7, and 9. Binding to 15N-labeled Pin1 enzyme was performed using state-of-the-art ¹⁵N–¹H HSQC NMR experiments to describe targeting breast cancer on a molecular level. In conclusion, the NMR results demonstrated chemical shift perturbation (peak shifting or peak disappearance) upon adding compound 12 indicating potential binding. Molecular docking using ‘Molecular Operating Environment’ software was extremely useful to elucidate the binding mode of active derivatives via hydrogen bonding.

Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques

Breast cancer is one of the leading causes of increasing deaths in women worldwide. The complex nature (microcalcification and masses) of breast cancer cells makes it quite difficult for radiologists to diagnose it properly. Subsequently, various computer-aided diagnosis (CAD) systems have previously been developed and are being used to aid radiologists in the diagnosis of cancer cells. However, due to intrinsic risks associated with the delayed and/or incorrect diagnosis, it is indispensable to improve the developed diagnostic systems. In this regard, machine learning has recently been playing a potential role in the early and precise detection of breast cancer. This paper presents a new machine learning-based framework that utilizes the Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, and Multilayer Perception approaches to efficiently predict breast cancer from the patient data. For this purpose, the Wisconsin Diagnostic Breast Cancer (WDBC) dataset has been utilized and classified using a hybrid Multilayer Perceptron Model (MLP) and 5-fold cross-validation framework as a working prototype. For the improved classification, a connection-based feature selection technique has been used that also eliminates the recursive features. The proposed framework has been validated on two separate datasets, i.e., the Wisconsin Prognostic dataset (WPBC) and Wisconsin Original Breast Cancer (WOBC) datasets. The results demonstrate improved accuracy of 99.12% due to efficient data preprocessing and feature selection applied to the input data.

Stress Estimation Model for the Sustainable Health of Cancer Patients

Good health is the most important and very necessary characteristic for stress-free, skillful, and hardworking people with a cooperative environment to create a sustainable society. Validating two algorithms, namely, sequential minimal optimization for regression (SMOreg) using vector machine and linear regression (LR) and using their predicted cancer patients' cases, this study presents a patient's stress estimation model (PSEM) to forecast their families' stress for patients' sustainable health and better care with early management by under-study cancer hospitals. The year-wise predictions (1998-2010) by LR and SMOreg are verified by comparing with observed values. The statistical difference between the predictions (2021-2030) by these models is analyzed using a statistical t-test. From the data of 217067 patients, patients' stress-impacting factors are extracted to be used in the proposed PSEM. By considering the total population of under-study areas and getting the predicted population (2021-2030) of each area, the proposed PSEM forecasts overall stress for expected cancer patients (2021-2030). Root mean square error (RMSE) (1076.15.46) for LR is less than RSME for SMOreg (1223.75); hence, LR remains better than SMOreg in forecasting (2011-2020). There is no significant statistical difference between values (2021-2030) predicted by LR and SMOreg (p value = 0.767 > 0.05). The average stress for a family member of a cancer patient is 72.71%. It is concluded that under-study areas face a minimum of 2.18% stress, on average 30.98% stress, and a maximum of 94.81% overall stress because of 179561 expected cancer patients of all major types from 2021 to 2030.

Anisomycin inhibits angiogenesis, growth, and survival of triple-negative breast cancer through mitochondrial dysfunction, AMPK activation, and mTOR inhibition

Aberrant upregulation of mitochondrial biogenesis is observed in breast cancer and holds potential therapeutic option. In our work, we showed that inhibition of mitochondrial function by anisomycin is effective against triple-negative breast cancer (TNBC). Anisomycin inhibits growth and induces caspase-dependent apoptosis in a panel of TNBC cell lines. Of note, anisomycin at a tolerable dose remarkably suppresses growth of TNBC in mice. In addition, anisomycin effectively targets breast cancer angiogenesis through inhibiting capillary network formation, migration, proliferation, and survival. Mechanistic studies show that although anisomycin activates p38 and JNK, their activations are not required for anisomycin's action. In contrast, anisomycin inhibits mitochondrial respiration, and decreases mitochondrial membrane potential and adenosine triphosphate (ATP) level. The inhibitory effect of anisomycin is significantly reversed in mitochondria respiration-deficient ρ0 cells. As a consequence, anisomycin activates AMPK and inhibits mammalian target-of-rapamycin signaling pathways. Our work demonstrated that anisomycin is a useful addition to the treatment armamentarium for TNBC.

Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.

Unsupervised Hierarchical Classification Approach for Imprecise Data in the Breast Cancer Detection

(1) Background: in recent years, a lot of the research of statistical methods focused on the classification problem in presence of imprecise data. A particular case of imprecise data is the interval-valued data. Following this research line, in this work a new hierarchical classification technique for multivariate interval-valued data is suggested for diagnosis of the breast cancer; (2) Methods: an unsupervised hierarchical classification method for imprecise multivariate data (called HC-ID) is performed for diagnosis of breast cancer (i.e., to discriminate between benign or malignant masses) and the results have been compared with the conventional (unsupervised) hierarchical classification approach (HC); (3) Results: the application on real data shows that the HC-ID procedure performs better HC procedure in terms of accuracy (HC-ID = 0.80, HC = 0.66) and sensitivity (HC-ID = 0.61, HC = 0.08). In the results obtained by the usual procedure, there is a high degree of false-negative (i.e., benign cancer diagnosis in malignant status) affected by the high degree of variability (i.e., uncertainty) characterizing the worst data.

Vessel filtering and segmentation of coronary CT angiographic images

PURPOSE

Coronary artery segmentation in coronary computed tomography angiography (CTA) images plays a crucial role in diagnosing cardiovascular diseases. However, due to the complexity of coronary CTA images and coronary structure, it is difficult to automatically segment coronary arteries accurately and efficiently from numerous coronary CTA images.

METHOD

In this study, an automatic method based on symmetrical radiation filter (SRF) and D-means is presented. The SRF, which is applied to the three orthogonal planes, is designed to filter the suspicious vessel tissue according to the features of gradient changes on vascular boundaries to segment coronary arteries accurately and reduce computational cost. Additionally, the D-means local clustering is proposed to be embedded into vessel segmentation to eliminate noise impact in coronary CTA images.

RESULTS

The results of the proposed method were compared against the manual delineations in 210 coronary CTA data sets. The average values of true positive, false positive, Jaccard measure, and Dice coefficient were [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], respectively. Moreover, comparing the delineated data sets and public data sets showed that the proposed method is better than the related methods.

CONCLUSION

The experimental results indicate that the proposed method can perform complete, robust, and accurate segmentation of coronary arteries with low computational cost. Therefore, the proposed method is proven effective in vessel segmentation of coronary CTA images without extensive training data and can meet clinical applications.

Influenza Vaccination and Risk of Lung Cancer in Patients with Chronic Kidney Disease: A Nationwide, Population-Based Cohort Study

Chronic kidney disease (CKD) is significantly associated with lung cancer incidence. The aim of this study was to elucidate whether influenza vaccination reduces the incidence of lung cancer in patients with CKD. This cohort study enrolled patients with a record of CKD diagnosis from 2000 to 2012 in Taiwan’s National Health Insurance Research Database. Included patients were divided into vaccinated and unvaccinated groups. In total 12,985 patients with CKD were enrolled. Among these patients, 5495 were vaccinated and 7490 were unvaccinated. The risk of lung cancer was significantly lower in the influenza vaccination group after adjusting for age, sex, dialysis status, lung diseases, comorbidities, level of urbanization, and monthly income (adjusted hazard ratio (HR): 0.50, 95% confidence interval (CI; 0.38−0.65), p < 0.05). Lower risk of lung cancer was observed in both sexes, all age groups, dialysis status and co-existed lung diseases. The association between the risk of lung cancer and vaccination appeared to be dose-dependent (adjusted HRs: 0.91 (0.66−1.25), 0.49 (0.34−0.71), and 0.25 (0.17−0.38) for patients who received 1, 2 or 3, and ≥4 vaccinations during the follow-up period, respectively). In conclusion, Influenza vaccination decreased the risk of lung cancer in patients diagnosed with CKD. This potentially protective effect against lung cancer appeared to be dose dependent.

Lung Cancer Patients' Characteristics and Comorbidities Using the Korean National Hospital Discharge In-depth Injury Survey Data

Background

The aim of this study was to assess the incidence of lung cancer and comorbidities in Korea and analyze the lung cancer patient’s characteristics and their comorbidities over the past 12 years. This study also aimed to investigate factors related to death as treatment outcome in discharged lung cancer patients.

Methods

This study analyzed the data obtained from the Korean National Hospital Discharge In-depth Injury Survey from 2006 to 2017. The quantity of discharged lung cancer patients was assessed by year. Comorbidities were limited to those included in the Elixhauser Comorbidity Index (ECI). A Chi-square test was performed to determine statistically significant differences in the distributions of the ECI and ECI scores according to the presence or absence of metastatic cancer. Logistic regression analysis was used to analyze factors related to death as treatment outcome.

Results

From 2006 to 2017, the number of discharged male and female patients with lung cancer increased from 31,720 to 42,016 and 10,897 to 18,197, respectively. The increase in the number of lung cancer patients was greater in women than in men (67.0% vs. 32.5%, respectively). The most common associated comorbidities were hypertension, diabetes, and chronic pulmonary disease. The factors related to death as treatment outcome were found to include sex, admission route, number of hospital beds, length of stay, presence or absence of metastatic cancer, and ECI score.

Conclusion

The number of lung cancer patients in Korea has increased, and a high proportion of these patients have chronic diseases, which negatively would impact the treatment and outcome of lung cancer patients as well as their quality of life. Thus, the management of chronic diseases needs to be prioritized in patients with lung cancer.

ElGamal Homomorphic Encryption-Based Privacy Preserving Association Rule Mining on Horizontally Partitioned Healthcare Data

In today’s world, life-threatening diseases have become a pre-eminent issue in healthcare due to the higher mortality rate. It is possible to lower this mortality rate by utilizing healthcare intelligence to detect diseases early. Patient’s medical data is stored in the EHR system, which is kept up to date by the healthcare provider. Data mining techniques like Association Rule Mining can detect a patient’s disease from their symptoms using digital healthcare data stored in the EHR system. Association rule mining’s efficacy can be improved by using global data from various EHR systems. It mandates that all EHR systems exchange healthcare records to a central server. When personal health information is made available on an untrusted server, several privacy laws may be violated. As a result, the challenge of privacy preserving distributed healthcare data mining has become a well-known study field in the healthcare industry. This research uses an efficient ElGamal homomorphic encryption technique to protect privacy in a distributed association rule mining. The proposed approach to discover the risk factor of most life-threatening diseases like breast cancer and heart disease with its symptoms and discuss the scope for combating COVID-19. Theoretical analysis of the proposed approach shows that it is efficient and maintains privacy in an insecure communication environment. An experimental study with a real dataset shows the proposed approach’s benefit compared to the local single EHR system results.

Breast cancer incidence and predictions (Monastir, Tunisia: 2002–2030): A registry-based study

Introduction

Breast cancer is a major public health problem worldwide. It is the leading cause of cancer deaths in females. In developing countries like Tunisia, the frequency of this cancer is still growing. The aim of this study was to determine the crude and standardized incidence rates, trends and predictions until 2030 of breast cancer incidence rates in a Tunisian governorate.

Methods

This is a descriptive study including all female patients diagnosed with breast cancer in Monastir between 2002 and 2013. The data were collected from the cancer register of the center. Tumors were coded according to the 10^th version of international classification of disease (ICD-10). Trends and predictions until 2030 were calculated using Poisson linear regression.

Results

A total of 1028 cases of female breast cancer were recorded. The median age of patients was 49 years (IQR: 41–59 years) with a minimum of 16 years and a maximum of 93 years. The age-standardized incidence rate (ASR) was of 39.12 per 100000 inhabitants. It increased significantly between 2002 and 2013 with APC of 8.4% (95% CI: 4.9; 11.9). Prediction until 2030 showed that ASR would reach 108.77 (95% CI: 57.13–209.10) per 100000 inhabitants.

Conclusion

The incidence and the chronological trends of breast cancer highlighted that this disease is of a serious concern in Tunisia. Strengthening preventive measures is a primary step to restrain its burden.

Medulla oblongata volume as a promising predictor of survival in amyotrophic lateral sclerosis

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Brainstem volumes reflect the disease severity expressed as ALSFRS-r (total score and its bulbar and spinal subscores).

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Medulla oblongata volume demonstrated a significant accuracy to discriminate long and short survivors ALS patients.

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Brainstem volumes may reflect the impairment of corticospinal and corticobulbar tracts as well as lower bulbar motor neurons. Furthermore, medulla oblongata could be used as an early predictor of survival in ALS patients.

Background

Unconventional magnetic resonance imaging studies of the brainstem have recently acquired a growing interest in amyotrophic lateral sclerosis (ALS) pathology since they provide a unique opportunity to evaluate motor tract degeneration and bulbar lower motor neuron involvement. The aim of this study was to investigate the role of brainstem structures as accurate biomarkers of disease severity and predictors of survival.

Materials and Methods

A total of 60 ALS patients and 30 healthy controls subjects (CS) were recruited in this study. Patients were divided in two subgroups according to the onset of the disease: 42 spinal (S-ALS) and 18 bulbar (B-ALS). All subjects underwent 3D-structural MRI. Brainstem volume both of the entire cohort of ALS patients and S-ALS and B-ALS onset were compared with those of CS. In addition the two ALS subgroups were tested for differences in brainstem volumes.

Volumetric, vertex-wise, and voxel-based approaches were implemented to assess correlations between MR structural features and clinical characteristics expressed as ALSFRS-r and its bulbar (ALSFSR-r-B) and spinal subscores (ALSFSR-r-S). ROC curves were performed to test the accuracy of midbrain, pons, and medulla oblongata volumes able to discriminate patients dichotomized into long and short survivors by using Two-Steps cluster analysis. Univariate and multivariate survival analyses were carried out to test the prognostic role of brainstem structures’ volume, trichotomized by applying a k-means clustering algorithm.

Results

Both the entire cohort of ALS patients and B-ALS and S-ALS showed significant lower volumes of both medulla oblongata and pons compared to CS. Furthermore, B-ALS showed a significant lower volume of medulla oblongata, compared to S-ALS. Lower score of ALSFRS-r correlated to atrophy in the anterior compartment of midbrain, pons, and medulla oblongata, as well as in the posterior portion of only this latter region. ALSFSR-r-S positively correlated with shape deformation and density reduction of the anterior portion of the entire brainstem, along the corticospinal tracts. ALSFSR-r-B instead showed a positive correlation with shape deformation of the floor of the fourth ventricle in the medulla oblongata and the crus cerebri in the midbrain. Only medulla oblongata volume demonstrated a significant accuracy to discriminate long and short survivors ALS patients (ROC AUC 0.76, p < 0.001). Univariate and multivariate analysis confirmed the survival predictive role of the medulla oblongata (log rank test p: 0.003).

Discussions

Our findings suggest that brainstem volume may reflect the impairment of corticospinal and corticobulbar tracts as well as lower bulbar motor neurons. Furthermore, medulla oblongata could be used as an early predictor of survival in ALS patients.

PIK3CA Mutation as Potential Poor Prognostic Marker in Asian Female Breast Cancer Patients Who Received Adjuvant Chemotherapy

Background: The prognostic relevance of the PIK3CA mutation together with PD-L1, c-Met, and mismatch repair deficiency (dMMR) have not been fully investigated in Asian women with breast cancer (BC) who have undergone postoperative adjuvant chemotherapy. Methods: We analyzed PIK3CA mutations via peptide nucleic acid (PNA)-mediated real-time PCR assay, PD-L1/c-Met expression via immunohistochemistry (IHC), and microsatellite instability (MSI) status using PCR and IHC, in 191 resected BCs from 2008 to 2011. The Cancer Genome Atlas (TCGA) dataset for the involvement of the PIK3CA mutation with PD-L1/c-Met/MMR was explored. Results: The PNA clamp-mediated assay was able to detect the PIK3CA mutation in 1% of the mutant population in the cell line validation. Using this method, the PIK3CA mutation was found in 78 (49.4%) of 158 samples. c-Met and PD-L1 positivity were identified in 31.4 and 21.8% of samples, respectively, which commonly correlated with high histologic grade and triple-negative subtype. MSI/dMMR was observed in 8.4% of patients, with inconsistency between MMR IHC and the MSI PCR. The PIK3CA mutation exhibited a poor prognostic association regarding recurrence-free survival (RFS) in both overall and triple-negative BCs. In subgroup analyses, the PIK3CA-mutated tumors showed poorer RFS than the PIK3CA-wildtype within the c-Met-positive, MSS, triple-negative, or age onset <50 years subgroups, which showed a similar trend of association in TCGA data. Conclusions: PIK3CA mutation together with c-Met or dMMR/MSI status might be relevant to poor prognosis in BC subsets, especially in Asian women.

Diagnosis of Breast Cancer Pathology on the Wisconsin Dataset with the Help of Data Mining Classification and Clustering Techniques

Breast cancer must be addressed by a multidisciplinary team aiming at the patient's comprehensive treatment. Recent advances in science make it possible to evaluate tumor staging and point out the specific treatment. However, these advances must be combined with the availability of resources and the easy operability of the technique. This study is aimed at distinguishing and classifying benign and malignant cells, which are tumor types, from the data on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset by applying data mining classification and clustering techniques with the help of the Weka tool. In addition, various algorithms and techniques used in data mining were measured with success percentages, and the most successful ones on the dataset were determined and compared with each other.

The Fight against Cancer by Microgravity: The Multicellular Spheroid as a Metastasis Model

Cancer is a disease exhibiting uncontrollable cell growth and spreading to other parts of the organism. It is a heavy, worldwide burden for mankind with high morbidity and mortality. Therefore, groundbreaking research and innovations are necessary. Research in space under microgravity (µg) conditions is a novel approach with the potential to fight cancer and develop future cancer therapies. Space travel is accompanied by adverse effects on our health, and there is a need to counteract these health problems. On the cellular level, studies have shown that real (r-) and simulated (s-) µg impact survival, apoptosis, proliferation, migration, and adhesion as well as the cytoskeleton, the extracellular matrix, focal adhesion, and growth factors in cancer cells. Moreover, the µg-environment induces in vitro 3D tumor models (multicellular spheroids and organoids) with a high potential for preclinical drug targeting, cancer drug development, and studying the processes of cancer progression and metastasis on a molecular level. This review focuses on the effects of r- and s-µg on different types of cells deriving from thyroid, breast, lung, skin, and prostate cancer, as well as tumors of the gastrointestinal tract. In addition, we summarize the current knowledge of the impact of µg on cancerous stem cells. The information demonstrates that µg has become an important new technology for increasing current knowledge of cancer biology.

Dysregulated LINC00961 Contributes to the Vitality and Migration of NSCLC Via miR-19a-3p/miR-19b-3p/miR-125b-5p

Accumulating evidence implies that long noncoding RNAs participate in non-small cell lung cancer (NSCLC) tumorigenesis. Our current study synthetically analyzed RNA sequencing data downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We identified LINC00961 significantly downregulated in NSCLC tissues. We explored the LINC00961 expression in NSCLC tumor tissues and cell lines with reverse transcription-quantitative polymerase chain reaction analysis. Lentivirus-mediated infection upregulated the expression of LINC00961 in A549 cells. The proliferation and migration capability were also measured in A549 cells. In addition, we performed luciferase reporter gene assay to investigate whether LINC00961 directly interacts with miR-19a-3p/miR-19b-3p/miR-125b-5p. A nude mice model was used to detect the potential biological process of LINC00961 on tumor growth in vivo. The results showed that LINC00961 was significantly down-egulated in NSCLC tissues and cell lines. LV-LINC00961 effectively increased the expression of LINC00961 and decreased the expression of miR-19a-3p/miR-19b-3p/miR-125b-5p. LINC00961 upregulation remarkably inhibited cell proliferation, migration, and invasion while promoting cell apoptosis in A549 cells. Luciferase reporter gene assay revealed that LINC00961 could directly sponge miR-19a-3p/miR-19b-3p/miR-125b-5p. Moreover, overexpressed miR-19a-3p/miR-19b-3p/miR-125b-5p reversed the effect of LINC00961 on cell function of A549 cells. Western blot assays revealed that LINC00961 could partially act as a tumor suppressor via affecting PI3K-AKT/MAPK/mTOR signaling pathway. In addition, overexpressed LINC00961-inhibited tumor growth was demonstrated in vivo. Overexpression of LINC00961 inhibited cell viability, invasion, and induced apoptosis in NSCLC, potentially via suppressing the expression of miR-19a-3p/miR-19b-3p/miR-125b-5p by targeting PI3K-AKT/MAPK/mTOR signaling pathways, which might provide the potential biomarker for NSCLC diagnosis and therapies.

THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy

Although cancer patients survive years after oncologic therapy, they are plagued with long-lasting or permanent residual symptoms, whose severity, rate of development, and resolution after treatment vary largely between survivors. The analysis and interpretation of symptoms is complicated by their partial co-occurrence, variability across populations and across time, and, in the case of cancers that use radiotherapy, by further symptom dependency on the tumor location and prescribed treatment. We describe THALIS, an environment for visual analysis and knowledge discovery from cancer therapy symptom data, developed in close collaboration with oncology experts. Our approach leverages unsupervised machine learning methodology over cohorts of patients, and, in conjunction with custom visual encodings and interactions, provides context for new patients based on patients with similar diagnostic features and symptom evolution. We evaluate this approach on data collected from a cohort of head and neck cancer patients. Feedback from our clinician collaborators indicates that THALIS supports knowledge discovery beyond the limits of machines or humans alone, and that it serves as a valuable tool in both the clinic and symptom research.

Aging Immune System and Its Correlation With Liability to Severe Lung Complications

Aging is considered to be a decline in physical and physiological events that extensively affect the body's immunity, and is linked with deterioration in both innate and adaptive immune responses. The immune system exhibits profound age-associated variations, known as immunosenescence, comprising a significantly low production of B and T lymphocytes in bone marrow and thymus, a decreased function of mature lymphocytes in secondary lymphoid tissues, a decrease in the synthesis of fresh naïve T cells, and reduced activation of T cells. Elderly individuals face a greater risk for many diseases particularly respiratory diseases due to their poor response to immune challenges as vigorously as the young. The current review explored the aging immune system, highlight the mortality rates of severe lung complications, such as pneumonia, COVID-19, asthma, COPD, lung cancer, IPF, and acute lung injury, and their correlation with aging immunity. This study can be helpful in better understanding the pathophysiology of aging, immune responses, and developing new approaches to improve the average age of the elderly population.

Exosomal miR-338-3p suppresses non-small-cell lung cancer cells metastasis by inhibiting CHL1 through the MAPK signaling pathway

Globally, lung cancer remains one of the most prevalent malignant cancers. However, molecular mechanisms and functions involved in its pathogenesis have not been clearly elucidated. This study aimed to evaluate the specific regulatory mechanisms of exosomal miR-338-3p/CHL1/MAPK signaling pathway axis in non-small-cell lung cancer. Western blotting and qRT-PCR (reverse transcription-polymerase chain reaction) were used to determine the expression levels of CHL1 and exosomal miR-338-3p in NSCLC (non-small-cell lung cancer). The CHL1 gene was upregulated and downregulated to evaluate its functions in NSCLC progression. In vitro MTS and apoptotic assays were used to investigate the functions of CHL1 and exosomal miR-338-3p in NSCLC progression. The high-throughput sequencing was used to explore differently expressed exosomal miRNAs. The biological relationships between MAPK signaling pathway and CHL1 and exosomal miR-338-3p in NSCLC were predicted through bioinformatics analyses and verified by western blotting. Elevated CHL1 levels were observed in NSCLC tissues and cells. Upregulated CHL1 expression enhanced NSCLC cells’ progression by promoting tumor cells proliferation while suppressing their apoptosis. Conversely, the downregulation of the CHL1 gene inhibited NSCLC cells’ growth and promoted tumor cells’ apoptotic rate. Additionally, CHL1 activated the MAPK signaling pathway. Besides, we confirmed that miR-338-3p directly sponged with CHL1 to mediate tumor cells progression. Moreover, exosomal miR-338-3p serum levels in NSCLC patients were found to be low. BEAS-2B cells can transfer exosomal miR-338-3p to A549 cells and SK-MES-1 cells. In addition, elevated exosomal miR-338-3p levels significantly inhibited tumor cells proliferation and promoted their apoptosis by suppressing activation of the MAPK signaling pathway. Exosomal miR-338-3p suppresses tumor cells' metastasis by downregulating the expression of CHL1 through MAPK signaling pathway inactivation.

Adaptive Diagnosis of Lung Cancer by Deep Learning Classification Using Wilcoxon Gain and Generator

Cancer is a complicated worldwide health issue with an increasing death rate in recent years. With the swift blooming of the high throughput technology and several machine learning methods that have unfolded in recent years, progress in cancer disease diagnosis has been made based on subset features, providing awareness of the efficient and precise disease diagnosis. Hence, progressive machine learning techniques that can, fortunately, differentiate lung cancer patients from healthy persons are of great concern. This paper proposes a novel Wilcoxon Signed-Rank Gain Preprocessing combined with Generative Deep Learning called Wilcoxon Signed Generative Deep Learning (WS-GDL) method for lung cancer disease diagnosis. Firstly, test significance analysis and information gain eliminate redundant and irrelevant attributes and extract many informative and significant attributes. Then, using a generator function, the Generative Deep Learning method is used to learn the deep features. Finally, a minimax game (i.e., minimizing error with maximum accuracy) is proposed to diagnose the disease. Numerical experiments on the Thoracic Surgery Data Set are used to test the WS-GDL method's disease diagnosis performance. The WS-GDL approach may create relevant and significant attributes and adaptively diagnose the disease by selecting optimal learning model parameters. Quantitative experimental results show that the WS-GDL method achieves better diagnosis performance and higher computing efficiency in computational time, computational complexity, and false-positive rate compared to state-of-the-art approaches.

Successful treatment after toxic epidermal necrolysis induced by AZD-9291 in a patient with non-small cell lung cancer: A case report

BACKGROUND

Toxic epidermal necrolysis and Stevens-Johnson syndrome are acute life-threatening skin reactions. AZD9291 has been developed as a third-generation epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) with activity against T790M mutation.

CASE SUMMARY

Herein we report a 68-year-old woman who developed a large area of skin necrosis and was diagnosed with toxic epidermal necrolysis after AZD-9291 ingestion. To the best of our knowledge, this is the first case reported in patients with EGFR T790M mutation in non-small cell lung cancer (NSCLC). Cabozantinib combined with erlotinib had clinically meaningful effectiveness, with additional toxicity that was generally manageable.

CONCLUSION

Treatment with AZD-9261 is effective in regressing the growth of the NSCLC and can bring some hope to despairing patients. We hope that more research will be carried out on the association between severe rashes and EGFR-TKIs, and more safe and effective drugs can be developed.

Cancer incidence, mortality, and burden in China: a time-trend analysis and comparison with the United States and United Kingdom based on the global epidemiological data released in 2020

BACKGROUND

Cancer is one of the leading causes of death and a main economic burden in China. Investigating the differences in cancer patterns and control strategies between China and developed countries could provide reference for policy planning and contribute to improving cancer control measures. In this study, we reviewed the rates and trends of cancer incidence and mortality and disability-adjusted life year (DALY) burden in China, and compared them with those in the United States (US) and the United Kingdom (UK).

METHODS

Cancer incidence, mortality, and DALY data for China, US and UK were obtained from the GLOBOCAN 2020 online database, Global Burden of Disease (GBD) 2019 study, and Cancer Incidence in Five Continents plus database (CI5 plus). Trends of cancer incidence and mortality in China, US, and UK were analyzed using Joinpoint regression models to calculate annual percent changes (APCs) and identify the best-fitting joinpoints.

RESULTS

An estimated 4,568,754 newly diagnosed cancer cases and 3,002,899 cancer deaths occurred in China in 2020. Additionally, cancers resulted in 67,340,309 DALYs in China. Compared to the US and UK, China had lower cancer incidence but higher cancer mortality and DALY rates. Furthermore, the cancer spectrum of China was changing, with a rapid increase incidence and burden of lung, breast, colorectal, and prostate cancer in addition to a high incidence and heavy burden of liver, stomach, esophageal, and cervical cancer.

CONCLUSIONS

The cancer spectrum of China is changing from a developing country to a developed country. Population aging and increase of unhealthy lifestyles would continue to increase the cancer burden of China. Therefore, the Chinese authorities should adjust the national cancer control program with reference to the practices of cancer control which have been well-established in the developed countries, and taking consideration of the diversity of cancer types by of different regions in China at the same time.

Applications of laboratory findings in the prevention, diagnosis, treatment, and monitoring of COVID-19

The worldwide pandemic of coronavirus disease 2019 (COVID-19) presents us with a serious public health crisis. To combat the virus and slow its spread, wider testing is essential. There is a need for more sensitive, specific, and convenient detection methods of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Advanced detection can greatly improve the ability and accuracy of the clinical diagnosis of COVID-19, which is conducive to the early suitable treatment and supports precise prophylaxis. In this article, we combine and present the latest laboratory diagnostic technologies and methods for SARS-CoV-2 to identify the technical characteristics, considerations, biosafety requirements, common problems with testing and interpretation of results, and coping strategies of commonly used testing methods. We highlight the gaps in current diagnostic capacity and propose potential solutions to provide cutting-edge technical support to achieve a more precise diagnosis, treatment, and prevention of COVID-19 and to overcome the difficulties with the normalization of epidemic prevention and control.

Insulin Receptor Substrate 1 Is Involved in the Phycocyanin-Mediated Antineoplastic Function of Non-Small Cell Lung Cancer Cells

Phycocyanin, derived from marine algae, is known to have noteworthy antineoplastic properties. However, the underlying mechanism involved in phycocyanin-mediated anti-growth function on non-small cell lung cancer (NSCLC) cells is still ambiguous. Here, we investigated the mechanism of action of phycocyanin on H1299, A549, and LTEP-a2 cells. According to the results obtained, insulin receptor substrate 1 (IRS-1) expression was reduced by phycocyanin. Cell phenotype tests showed that siRNA knockdown of IRS-1 expression significantly inhibited the growth, migration, colony formation, but promoted the apoptosis of NSCLC cells. Meanwhile, phycocyanin and IRS-1 siRNA treatment both reduced the PI3K-AKT activities in NSCLC cells. Moreover, overexpression of IRS-1 accelerated the proliferation, colony formation, and migration rate of H1299, A549, and LTEP-a2 cells, which was contradicting to the knockdown results. Overall, this study uncovered a regulatory mechanism by which phycocyanin inhibited the growth of NSCLC cells via IRS-1/AKT pathway, laying the foundation for the potential target treatment of NSCLC.

Using Artificial Intelligence for High-Volume Identification of Silicosis and Tuberculosis: A Bio-Ethics Approach

Although Artificial Intelligence (AI) is being increasingly applied, considerable distrust about introducing "disruptive" technologies persists. Intrinsic and contextual factors influencing where and how such innovations are introduced therefore require careful scrutiny to ensure that health equity is promoted. To illustrate one such critical approach, we describe and appraise an AI application - the development of computer assisted diagnosis (CAD) to support more efficient adjudication of compensation claims from former gold miners with occupational lung disease in Southern Africa. In doing so, we apply a bio-ethical lens that considers the principles of beneficence, non-maleficence, autonomy and justice and add explicability as a core principle. We draw on the AI literature, our research on CAD validation and process efficiency, as well as apprehensions of users and stakeholders. Issues of concern included AI accuracy, biased training of AI systems, data privacy, impact on human skill development, transparency and accountability in AI use, as well as intellectual property ownership. We discuss ways in which each of these potential obstacles to successful use of CAD could be mitigated. We conclude that efforts to overcoming technical challenges in applying AI must be accompanied from the onset by attention to ensuring its ethical use.

A Comprehensive Survey on Data Utility and Privacy: Taking Indian Healthcare System as a Potential Case Study

Background: According to the renowned and Oscar award-winning American actor and film director Marlon Brando, “privacy is not something that I am merely entitled to, it is an absolute prerequisite.” Privacy threats and data breaches occur daily, and countries are mitigating the consequences caused by privacy and data breaches. The Indian healthcare industry is one of the largest and rapidly developing industry. Overall, healthcare management is changing from disease-centric into patient-centric systems. Healthcare data analysis also plays a crucial role in healthcare management, and the privacy of patient records must receive equal attention. Purpose: This paper mainly presents the utility and privacy factors of the Indian healthcare data and discusses the utility aspect and privacy problems concerning Indian healthcare systems. It defines policies that reform Indian healthcare systems. The case study of the NITI Aayog report is presented to explain how reformation occurs in Indian healthcare systems. Findings: It is found that there have been numerous research studies conducted on Indian healthcare data across all dimensions; however, privacy problems in healthcare, specifically in India, are caused by prevalent complacency, culture, politics, budget limitations, large population, and existing infrastructures. This paper reviews the Indian healthcare system and the applications that drive it. Additionally, the paper also maps that how privacy issues are happening in every healthcare sector in India. Originality/Value: To understand these factors and gain insights, understanding Indian healthcare systems first is crucial. To the best of our knowledge, we found no recent papers that thoroughly reviewed the Indian healthcare system and its privacy issues. The paper is original in terms of its overview of the healthcare system and privacy issues. Social Implications: Privacy has been the most ignored part of the Indian healthcare system. With India being a country with a population of 130 billion, much healthcare data are generated every day. The chances of data breaches and other privacy violations on such sensitive data cannot be avoided as they cause severe concerns for individuals. This paper segregates the healthcare system’s advances and lists the privacy that needs to be addressed first.

Upregulation of glucosamine-phosphate N-acetyltransferase 1 is a promising diagnostic and predictive indicator for poor survival in patients with lung adenocarcinoma

Lung adenocarcinoma, a type of non-small cell lung cancer, is the leading cause of cancer death worldwide. Great efforts have been made to identify the underlying mechanism of adenocarcinoma, especially in relation to oncogenes. The present study by integrating computational analysis with western blotting, aimed to understand the role of the upregulation of glucosamine-phosphate N-acetyltransferase 1 (GNPNAT1) in carcinogenesis. In the present study, publicly available gene expression profiles and clinical data were downloaded from The Cancer Genome Atlas to determine the role of GNPNAT1 in lung adenocarcinoma (LUAD). In addition, the association between LUAD susceptibility and GNPNAT1 upregulation were analyzed using Wilcoxon signed-rank test and logistic regression analysis. In LUAD, GNPNAT1 upregulation was significantly associated with disease stage [odds ratio (OR)=2.92, stage III vs. stage I], vital status (dead vs. alive, OR=1.89), cancer status (tumor status vs. tumor-free status, OR=1.85) and N classification (yes vs. no, OR=1.75). Cox regression analysis and the Kaplan-Meier method were utilized to evaluate the association between GNPNAT1 expression and overall survival (OS) time in patients with LUAD. The results demonstrated that patients with increased GNPNAT1 expression levels exhibited a reduced survival rate compared with those with decreased expression levels (P=8.9×10⁻⁵). In addition, Cox regression analysis revealed that GNPNAT1 upregulation was significantly associated with poor OS time [hazard ratio (HR): 1.07; 95% confidence interval (CI): 1.04–1.10; P<0.001]. The gene set enrichment analysis revealed that ‘cell cycle’, ‘oocyte meiosis’, ‘pyrimidine mediated metabolism’, ‘ubiquitin mediated proteolysis’, ‘one carbon pool by folate’, ‘mismatch repair progesterone-mediated oocyte maturation’ and ‘basal transcription factors purine metabolism’ were differentially enriched in the GNPNAT1 high-expression samples compared with GNPNAT1 low-expression samples. The aforementioned pathways are involved in the pathogenesis of LUAD. The findings of the present study suggested that GNPNAT1 upregulation may be considered as a promising diagnostic and prognostic biomarker in patients with LUAD. In addition, the aforementioned pathways may be pivotal pathways perturbed by the abnormal expression of GNPNAT1 in LUAD. The findings of the present study demonstrated the therapeutic value of the regulation of GNPNAT1 in lung adenocarcinoma.

Improved trends of lung cancer mortality-to-incidence ratios in countries with high healthcare expenditure

Background

Lung cancer stage has a significant impact on prognosis, and early detection of lung cancer relies on screenings. Despite the strong relationship between screening and lung cancer staging, the role of healthcare expenditure in lung cancer outcomes remains unknown. The aim of this study was to evaluate the relationship between economic status and clinical outcomes in lung cancer.

Methods

Data were obtained from GLOBOCAN and the World Health Organization. Mortality‐to‐incidence ratios (MIRs) and their change over time, calculated as the difference between the MIRs of 2012 and 2018 (δMIR), were used to evaluate their correlation to expenditures on healthcare and human development index (HDI) disparities via Spearman's rank correlation coefficient.

Results

Regions such as North America have relatively high crude incidence rates but low MIR values. Furthermore, countries with lower crude incidence rates spent less on healthcare. The results show significant negative associations between HDI, current health expenditure (CHE) per capita, CHE as a percentage of gross domestic product (CHE/GDP), and MIR. As for MIR and δMIR, countries with favorable MIRs also showed improving MIRs based on δMIR.

Conclusions

HDI, CHE per capita, CHE/GDP, and development status play noticeable roles in the prognosis of lung cancer, leading to large disparities in clinical outcomes.

Phenomapping of Patients with Primary Breast Cancer Using Machine Learning-Based Unsupervised Cluster Analysis

Primary breast cancer (PBC) is a heterogeneous disease at the clinical, histopathological, and molecular levels. The improved classification of PBC might be important to identify subgroups of the disease, relevant to patient management. Machine learning algorithms may allow a better understanding of the relationships within heterogeneous clinical syndromes. This work aims to show the potential of unsupervised learning techniques for improving classification in PBC. A dataset of 712 women with PBC is used as a motivating example. A set of variables containing biological prognostic parameters is considered to define groups of individuals. Four different clustering methods are used: K-means, self-organising maps, hierarchical agglomerative (HAC), and Gaussian mixture models clustering. HAC outperforms the other clustering methods. With an optimal partitioning parameter, the methods identify two clusters with different clinical profiles. Patients in the first cluster are younger and have lower values of the oestrogen receptor (ER) and progesterone receptor (PgR) than patients in the second cluster. Moreover, cathepsin D values are lower in the first cluster. The three most important variables identified by the HAC are: age, ER, and PgR. Unsupervised learning seems a suitable alternative for the analysis of PBC data, opening up new perspectives in the particularly active domain of dissecting clinical heterogeneity.

Cytotoxicity and Antibacterial Evaluation of O-Alkylated/Acylated Quinazolin-4-one Schiff Bases

A series of quinazolin-4-one Schiff bases were synthesized and tested in vitro for their cytotoxicity against two cancerous cell lines (MCF-7, Caco-2) and a human embryonic cell line (HEK-293) including their antibacterial evaluation against two Gram-positive and four Gram-negative bacterial strains. Most of the quinazoline-Schiff bases exhibited potent cytotoxicity against Caco-2. 3-[(Z)-({4-[(But-2-yn-1-yl)oxy]phenyl}methylidene)amino]-2-methylquinazolin-4(3H)-one (6f) with the O-butyne functional group displayed three-fold higher cytotoxic activity (IC₅₀ =376.8 μM) as compared to 5-fluorouracil (5-FU; IC₅₀ =1086.1 μM). However, all compounds were found to be toxic to HEK-293, except for 3-[(Z)-({4-[(2,4-difluorophenyl)methoxy]phenyl}methylidene)amino]-2-methylquinazolin-4(3H)-one (6h) that showed ∼three-fold lower toxicity and higher selectivity index than 5-FU. Structure-activity relationship (SAR) analysis revealed that O-alkylation generally increased the anticancer activity and selectivity of quinazoline-4-one Schiff bases toward Caco-2 cells. The fluorinated Schiff-base generally exhibited even more significant cytotoxic activity compared to their chlorine analogs. Surprisingly, none of the quinazoline-4-one Schiff bases displayed encouraging antibacterial activity against the bacterial strains investigated. Most of the compounds were predicted to show compliance with the Lipinski parameters and ADMET profiles, indicating their drug-like properties.