Secure and interpretable lung cancer prediction model using mapreduce private blockchain federated learning and XAI

Exosomal lipids for classifying early and late stage non-small cell lung cancer

Artificial intelligence (AI) has, in the recent past, experienced a rebirth with the growth of generative AI systems such as ChatGPT and Bard. These systems are trained with billions of parameters and have enabled widespread accessibility and understanding of AI among different user groups. Widespread adoption of AI has led to the need for understanding how machine learning (ML) models operate to build trust in them. An understanding of how these models generate their results remains a huge challenge that explainable AI seeks to solve. Federated learning (FL) grew out of the need to have privacy-preserving AI by having ML models that are decentralized but still share model parameters with a global model. This study sought to examine the extent of development of the explainable AI field within the FL environment in relation to the main contributions made, the types of FL, the sectors it is applied to, the models used, the methods applied by each study, and the databases from which sources are obtained. A systematic search in 8 electronic databases, namely, Web of Science Core Collection, Scopus, PubMed, ACM Digital Library, IEEE Xplore, Mendeley, BASE, and Google Scholar, was undertaken. A review of 26 studies revealed that research on explainable FL is steadily growing despite being concentrated in Europe and Asia. The key determinants of FL use were data privacy and limited training data. Horizontal FL remains the preferred approach for federated ML, whereas post hoc explainability techniques were preferred. There is potential for development of novel approaches and improvement of existing approaches in the explainable FL field, especially for critical areas. OSF Registries 10.17605/OSF.IO/Y85WA; https://osf.io/y85wa.

In recent years, lung cancer-prediction models have become popular. However, few bibliometric analyses have been performed in this field. This study aimed to reveal the scientific output and trends in lung cancer-prediction models from a global perspective. In this study, publications were retrieved and extracted from the Web of Science Core Collection (WoSCC) database. CiteSpace 6.1.R3 and VOSviewer 1.6.18 were used to analyze hotspots and theme trends. A marked increase in the number of publications related to lung cancer-prediction models was observed. A total of 2711 institutions from in 64 countries/regions published 2139 documents in 566 academic journals. China and the United States were the leading country in the field of lung cancer-prediction models. The institutions represented by Fudan University had significant academic influence in the field. Analysis of keywords revealed that lncRNA, tumor microenvironment, immune, cancer statistics, The Cancer Genome Atlas, nomogram, and machine learning were the current focus of research in lung cancer-prediction models. Over the last two decades, research on risk-prediction models for lung cancer has attracted increasing attention. Prognosis, machine learning, and multi-omics technologies are both current hotspots and future trends in this field. In the future, in-depth explorations using different omics should increase the sensitivity and accuracy of lung cancer-prediction models and reduce the global burden of lung cancer.

8553 Background: Lung cancer early detection improves survival, but risk-based low-dose CT screening (LDCT) only identifies a minority of patients. We implemented an ILNP in a community healthcare system, and evaluated its risks and benefits. Methods: Patients with lung lesions on routinely-performed radiologic studies were flagged by radiologists and triaged using evidence-based guidelines. We tracked demographics, clinical characteristics, procedures, complications, and health outcomes. We analyzed ILNP subjects’ eligibility for LDCT by National Lung Screening Trial (NLST), Center for Medicaid Services (CMS), NEderlands Leuvens Screening ONderzoek (NELSON), National Comprehensive Cancer Network (NCCN) Risk Groups 1 and 2 (screening recommended), NCCN Risk Group 3 (screening not currently recommended), and US Preventive Services Task Force (USPSTF) criteria from 2013 and 2020. Statistical analysis used the chi-square test and Kaplan Meier method. Results: From 2015-2020, 13,710 patients were evaluated in the ILNP program: median age, 64 years; 42% male; 65% White, 29% Black; 667 (4.9%) were diagnosed with lung cancer. Lung cancers diagnosed from ILNP were 39% adenocarcinoma / 20% Squamous Cell with clinical stage distribution 49% I, 8% II, 17% III, and 16% IV. 832 (6.1%) had invasive diagnostic testing- CT-guided biopsy (50%), bronchoscopy (30%), and/or EBUS (26%); 11% of the 832 had >1 invasive diagnostic test. The most common complications from invasive testing were pneumothorax and chest tube placement. Only 11%-20% of all ILNP patients would have been eligible for LDCT. In ILNP patients diagnosed with lung cancer, only 33% were eligible for screening by NLST criteria; the proportion increased substantially when USPSTF 2020 or NCCN Group 2 criteria were applied (Table). Compared to NLST, NCCN Group 2 criteria increased screening eligibility among cancer patients by 22% (from 33% to 55%), while only increasing screening eligibility by 6% (from 8% to 14%) in non-cancer patients. Aggregate 1-year and 3-year survival rates for lung cancer patient diagnosed through ILNP were 76% (95% CI: 73, 80) and 64% (95% CI: 59, 69). Conclusions: The ILNP identified early-stage lung cancer more frequently than most LDCT programs, with promising survival rates. The majority of subjects with lung cancer were not eligible for LDCT, we still need to optimize risk-based screening criteria. Even with new, expanded criteria for LDCT, structured ILNP is necessary to expand early detection of lung cancer.[Table: see text]

The incidence and mortality rates of lung cancer have been increasing in many countries. However, the true effectiveness of screening for lung cancer is still controversial. This study aimed to examine the growth pattern of lung cancer and to evaluate the efficacy of screening. The authors linked the records of annual radiologic screening to cancer registry data and conducted a retrospective follow-up study of radiographs in all patients with lung cancer arising in a population undergoing screening. Among a total of 305,934 participants, screening detected 206 lung cancers, 103 of which were Stage I disease. Seventy-one of the 131 adenocarcinomas were Stage I, and 58% of them showed evidence of cancer for 2 years on a retrospective review of radiographs. Presentation as small faint lesions overlapping the normal chest structures delayed the early detection of adenocarcinoma. The overall sensitivity of screening was 70%, 52% for squamous cell carcinoma and 50% for small cell carcinoma. Rapidly growing Stage II-IV tumors without retrospective evidence of cancer on previous radiographs accounted for most of the cancers detected during the intervals between screening. Both the low detectability of Stage I adenocarcinoma and the late recognition of rapidly growing small cell and squamous cell carcinomas reduced the effectiveness of screening. More effective imaging methods and an antismoking campaign are required to reduce lung cancer mortality.

The Center for Global Early Detection at MD Anderson has initiated two longitudinal early detection studies: LEAP (Lung Cancer Early Detection and Prevention, 2013-2019) for lung cancer screening and MERIT (Mammography Early Detection Risk Assessment and Imaging Technologies, 2017-ongoing) for breast cancer screening. Both cohorts enrolled subjects based on US standard-of-care guidelines at the time of enrollment for low-dose CT screening and mammography, respectively. The cohorts collected detailed health data from questionnaires, imaging data, and blood specimens at the time of screening. In addition to information on the screened-detected cancers, follow-up information was collected on any other types of cancers that are diagnosed after enrollment. Here, we evaluate the incidence of other cancers without available screening to assess the potential impact of multi-cancer early detection studies for these two types of screening populations. The two studies enrolled approximately 9,300 subjects (N = 2853 for LEAP; N = 6,400 for MERIT) with median follow up times of 2.99 years (IQR: 1.09-4.08) for LEAP and 2.05 years (IQR: 0.09-3.01) for MERIT. Currently, there are more than 800,000 aliquots of banked biospecimens from both cohorts. The incidence of lung cancer for the LEAP cohort undergoing low-dose CT screening was 0.91% per year. Cancers other than lung cancer were also diagnosed after enrollment including prostate, breast, bladder, head, and neck, colorectal, lymphoma, endocrine, gynecologic, and kidney cancers. Overall, the incidence of other cancers in LEAP was high for those cancers without available screening - 0.49% per year excluding breast, prostate, and colorectal cancers. The incidence of breast cancer for women in LEAP was lower than expected, 0.17% per year, likely a result of underscreening by mammography. For the MERIT cohort, the incidence of breast cancer was 0.89% per year. Cancers other than breast were diagnosed in MERIT after enrollment including gynecologic, melanoma, lung, colorectal, endocrine, bladder, and gastrointestinal cancers. The incidence of other cancers in MERIT was 0.55% per year, excluding colorectal cancer. We conclude that the incidence of cancers without a current screening modality in LEAP and MERIT is sufficiently high (0.49-0.55% per year) to justify the application of multi-cancer screening. Even for cancers with standard-of-care screening, a multi-cancer blood-based biomarker test could be useful given the possibility of underscreening, as we observed for breast cancer in the LEAP cohort. Citation Format: Jennifer Dennison, Ehsan Irajizad, Edwin Ostrin, Myrna Godoy, Olena Weaver, Jessica Leung, Samir Hanash. Application of multi-cancer early detection to lung and breast cancer screening populations. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P057.

Comments: This publication provides long-term followup in a group of 32 patients who underwent bronchoscopic treatment (BT) for the treatment of intraluminal microinvasive radiographically occult lung cancer (ROLC). These patients were not eligible for surgical resection because of comorbidities and other reasons. These 32 patients (28 men and 4 women) met the following inclusion criteria for the study: surgically nonresectable candidates because of poor lung function or the presence of multiple primaries; a strictly ≤ 1 cm in size, intraluminal, microinvasive ROLC (carcinoma in situ excluded) with visible distal tumor margin by conventional and autofluorescence bronchoscopy; no bronchial wall invasion or extraluminal tumor growth on highresolution computed tomography. History of prior primary lung cancer was present in 13 patients, and these were surgically resected as long as 13 years before current synchronous or metachronous primaries. The TNM stages of previous resections for the non-ROLC tumors were stage IB in seven patients, stage IIB in four patients, stage IIIA in one patient, and stage IIIB in one patient. There was no sign of recurrence or metastatic disease of the previous non-ROLC cancer before BT. In six patients, multiple ROLCs were found at different sites in both lungs (four patients with two lesions and two patients with three lesions) that met all criteria for BT as described earlier. BT was administered with intent to cure cancer, and consecutive patients were treated with photodynamic therapy (n = 5), Nd:YAG laser therapy (n = 1), electrocautery (n = 24), and argon plasma coagulation (n = 2). Follow-up evaluation at 3 to 4 months interval included high-resolution computed tomographic scans, both conventional and autofluorescence bronchoscopy, including biopsy specimens and brush cytology for histologic evaluation. The average follow-up period was 5 years (range, 2–10 years). In three patients local recurrence was again successfully treated with electrocautery. Sixteen patients (50%) died during follow-up. Eight of the nine patients who died as a result of lung cancer had a previous resection of a more advanced stage lung cancer as long as 5 years before BT of the ROLC. In the remaining seven patients, the cause of death was not related to lung cancer. Sixteen patients are still alive without any tumor recurrence. Based on these observations, the authors conclude that BT is an effective treatment modality for high-risk patients with ROLC who are not eligible for surgical resection. Early detection of lung cancer by currently available methods such as low-dose spiral computed tomography, screening sputum cytology, autofluorescence bronchoscopy, and other techniques may enable surgical cure in a number of patients. However, not all patients are surgical candidates because of various reasons. One of the main reasons is the presence of cancer in the major airways (trachea and within 2.0 cm of the main carina), which upgrades the cancer stage. More commonly, surgery and anesthesia are contraindicated because of underlying medical conditions such as severe chronic obstructive pulmonary disease, severe malnutrition, and other conditions. When such patients develop intraluminal microinvasive ROLC in the central part of the tracheabronchial tree, various types of BT described in this publication may benefit them. Nonetheless, as noted by the authors of this study, early detection of lung cancer and BT in such patients is questionable because this expensive and time-consuming exercise may have little or no impact on mortality. Death from lung cancer occurred in 9 patients (28%), and death from other causes was noted in 7 patients (22%). The positive outcome of this study is that 50% of patients were still alive after BT, with an average follow-up of 5 years (range, 2–10 years).

Lung cancer mortality and morbidity rates are the first among malignant tumors. It is extremely crucial to pay more attention to the early diagnosis and treatment of lung cancer and to grasp and judge the progress of the patient's condition promptly. In this study, lung cancer patients' early diagnosis with tumor markers and inflammatory variables is examined. The general surgery department of our hospital treated 98 patients with lung cancer and 100 patients with benign pulmonary hyperplasia from January 2017 to February 2018. Additionally, 100 healthy subjects who completed physical examinations during this time period were included. Based on the findings of the pathological examination, 100 patients with benign pulmonary hyperplasia were chosen as the benign group, 100 patients with lung cancer were chosen as the malignant group, and 100 healthy individuals were chosen as the healthy group. The tumor markers carbohydrate antigen 125 (CA125), CA153, and carcinoembryonic antigen (CEA), as well as inflammatory factors such as tumor necrosis factor- (TNF-) and high-sensitivity C-reactive protein, were measured in the venous blood of three groups of patients (hs-CRP). There was no discernible difference in tumor marker levels between the benign and healthy groups (P > 0.05). In comparison to the benign and healthy groups, the malignant group had higher serum levels of CA153, CA125, and CEA (P 0.05). Between the benign and healthy groups, there was no discernible difference in the levels of inflammatory factors (P > 0.05). TNF- and hs-CRP serum levels were observed to be higher in the malignant group compared to the benign and healthy groups (P 0.05). The combined detection of CA153 + CA125 + CEA + TNF − +hs − CRP showed the highest sensitivity and specificity, which were, respectively, 62.22 percent and 92.00 percent, when compared to single or mixed detection of tumor markers or inflammatory factors solely. Serum levels of inflammatory agents TNF- and hs-CRP may be related to the pathophysiology and other functions of patients with lung cancer, as well as to the development and metastasis of the disease. These markers include CA153, CA125, and CEA. For the early detection of lung cancer and the evaluation of the disease's severity, the detection of tumor markers in combination with inflammatory variables has a significant reference value.

Lung cancer is the leading cause of cancer death worldwide. Survival is largely dependent on the stage of diagnosis: the localized disease has a 5-year survival greater than 55%, whereas, for spread tumors, this rate is only 4%. Therefore, the early detection of lung cancer is key for improving prognosis. In this study, we present an innovative, non-invasive, cancer detection approach based on measurements of the metabolic activity profiles of immune system cells. For each Liquid ImmunoBiopsy test, a 384 multi-well plate is loaded with freshly separated PBMCs, and each well contains 1 of the 16 selected stimulants in several increasing concentrations. The extracellular acidity is measured in both air-open and hermetically-sealed states, using a commercial fluorescence plate reader, for approximately 1.5h. Both states enable the measurement of real-time accumulation of 'soluble' versus 'volatile' metabolic products, thereby differentiating between oxidative phosphorylation and aerobic glycolysis. The metabolic activity profiles are analyzed for cancer diagnosis by machine-learning tools. We present a diagnostic accuracy study, using a multivariable prediction model to differentiate between lung cancer and control blood samples. The model was developed and tested using a cohort of 200 subjects (100 lung cancer and 100 controlsubjects), yielding 91% sensitivity and 80% specificity in a 20-fold cross-validation. Our results clearly indicate that the proposed clinical model is suitable for non-invasive early lung cancer diagnosis, and is indifferent to lung cancer stage and histological type.

Lung cancer is the leading cause of cancer death in the United States and worldwide. However, among the top 4 deadliest cancers, lung cancer is the only one not subject to routine screening. Optimism for an effective lung cancer-screening examination soared after the release of the National Lung Screening Trial results in November 2011. Since then, nearly 40 major medical societies and organizations have endorsed low-dose computed tomography (LDCT) screening. In December 2013, the United States Preventive Services Task Force also endorsed LDCT. However, the momentum for LDCT screening slowed in April 2014 when the Medicare Evidence Development and Coverage Advisory Committee (MEDCAC) panel concluded that there was not enough evidence to justify the annual use of LDCT scans for the detection of early lung cancer. This article briefly reviews the epidemiology of lung cancer, the National Lung Screening Trial study results, and the growing national endorsement of LDCT from a variety of key stakeholder organizations. We subsequently analyze and offer our evidence-based counterpoints to the major assumptions underlying the MEDCAC decision.

The latest growth of storage capabilities has led to an accumulating volume of medical data stored locally by various healthcare entities. Given the recent progress observed in the domain of artificial intelligence, these data could be efficiently exploited, leading to improved and less expensive healthcare conditions. However, the common practice is medical data to be solely locally used, ending up poorly exploited, due to strict sharing restrictions stemming mainly from privacy limitation of their sensitive nature. Considering the centralized character of conventional machine learning approaches, it is apparent that they cannot reassure the privacy required. On the other hand, federated learning (FL) can be regarded as an upcoming and promising answer to efficient exploitation of medical data, considering its decentralized approach. In more details, FL can enable collaboration among various participating entities on the development and training of a common, central and fully shared model without need of sharing owned sensitive data. Thus, apparently FL approach not only can mitigate the privacy-preservation issues but can lead to the development of reliable and robust healthcare tools. Indicatively, FL can facilitate the development of a model capable of assessing the health risk, which can be a vital tool for medical sciences. To this end, in this work we present a tool capable of assessing the occurrence of different diseases or complications. This tool is based on FL technique utilizing deep neural network model. The FL model developed herein is indicatively applied to four different medical applications proving its generality in the healthcare domain. The FL approach discussed herein is compared with a corresponding centralized learning. According to the demonstrated results, FL can consist a useful health risk assessment tool exhibiting acceptable performance while preserving privacy in sensitive medical data.

In recent years, significant advances have been made in health systems with the widespread adoption of the Internet of Things (IoT) and AI-powered innovative services. IoT enables the continuous collection of health data, while Artificial Intelligence (AI) analyzes this data to enhance early disease diagnosis. However, traditional Deep Learning (DL) approaches generally rely on centralized systems that collect and process personal data, posing a risk of violating individual privacy. In this study, a Federated Learning (FL) approach is used to process health data obtained from wearable smart devices while preserving privacy and classifying individuals' health scores. To overcome the performance limitations of FL approaches, a novel approach, FedSync, is proposed to incorporate a parallel training mechanism. To demonstrate the superiority of the FedSync approach, results are compared with DL and FL approaches in terms of accuracy, precision, recall, and F1-score. According to the results, the proposed approach provides a 29.02% improvement in computational time performance compared to FL and a 21.01% improvement compared to DL. Furthermore, to address the lack of explainability in IoT data, Explainable AI (XAI) has been integrated into the experiments to identify the most influential features affecting the classification score. This study makes a significant contribution to the literature by presenting a synchronous FL approach supported by XAI for IoT-based health data analysis.

In this meta-analysis, we evaluated the usefulness of serum thymidine kinase 1 concentration (STK1c) for monitoring the outcome of extensive open surgery in patients with lung cancer. We also compared STK1c between a healthy population and patients with benign and malignant lung tumors to assess its potential value for early detection of lung cancer and for distinguishing between benign lung disease and malignant cancer. Related studies were retrieved from publications in PubMed, Cochrane, China National Knowledge Infrastructure, Wanfang databases, and Internet searches. Correlation was evaluated using weighted mean difference. Fixed or random effect models were selected for data analyses based on heterogeneity tested with the chi-square test. Publication bias was assessed using a funnel plot and Egger's test. Twenty studies were selected for analysis, which showed that STK1c was significantly (p < 0.00001) reduced by 41.7% 1 month after extensive open surgery, approximately corresponding to an STK1c half-life of 1 month. STK1c levels were significantly higher in lung cancer patients than in healthy persons (p < 0.00001) or in patients with benign lung disease (p < 0.00001). There was also a significant difference in STK1c between patients with benign and malignant lung disease (p < 0.0001). The half-life of STK1c may be an important tool in the clinical evaluation of surgical response in patients with lung cancer. STK1c may also be beneficial in the early detection of lung cancer.

Clinical application of serum seven tumour-associated autoantibodies in patients with pulmonary nodules

A review of detection methods for the early diagnosis of lung cancer