Application of machine learning for lung cancer survival prognostication-A systematic review and meta-analysis.

Introduction: Machine learning (ML) techniques have gained increasing attention in the field of healthcare, including predicting outcomes in patients with lung cancer. ML has the potential to enhance prognostication in lung cancer patients and improve clinical decision-making. In this systematic review and meta-analysis, we aimed to evaluate the performance of ML models compared to logistic regression (LR) models in predicting overall survival in patients with lung cancer. Methods: We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. A comprehensive search was conducted in Medline, Embase, and Cochrane databases using a predefined search query. Two independent reviewers screened abstracts and conflicts were resolved by a third reviewer. Inclusion and exclusion criteria were applied to select eligible studies. Risk of bias assessment was performed using predefined criteria. Data extraction was conducted using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS) checklist. Meta-analytic analysis was performed to compare the discriminative ability of ML and LR models. Results: The literature search resulted in 3,635 studies, and 12 studies with a total of 211,068 patients were included in the analysis. Six studies reported confidence intervals and were included in the meta-analysis. The performance of ML models varied across studies, with C-statistics ranging from 0.60 to 0.85. The pooled analysis showed that ML models had higher discriminative ability compared to LR models, with a weighted average C-statistic of 0.78 for ML models compared to 0.70 for LR models. Conclusion: Machine learning models show promise in predicting overall survival in patients with lung cancer, with superior discriminative ability compared to logistic regression models. However, further validation and standardization of ML models are needed before their widespread implementation in clinical practice. Future research should focus on addressing the limitations of the current literature, such as potential bias and heterogeneity among studies, to improve the accuracy and generalizability of ML models for predicting outcomes in patients with lung cancer. Further research and development of ML models in this field may lead to improved patient outcomes and personalized treatment strategies.

Emerging Immunotherapeutic and Diagnostic Modalities in Carcinoid Tumors.

Evasion of innate immunity represents a frequently employed method by which tumor cells survive and thrive. Previously, the development of immunotherapeutic agents capable of overcoming this evasion has realized pronounced clinical utility across a variety of cancer types. More recently, immunological strategies have been investigated as potentially viable therapeutic and diagnostic modalities in the management of carcinoid tumors. Classic treatment options for carcinoid tumors rely upon surgical resection or non-immune pharmacology. Though surgical intervention can be curative, tumor characteristics, such as size, location, and spread, heavily limit success. Non-immune pharmacologic treatments can be similarly limited, and many demonstrate problematic side effects. Immunotherapy may be able to overcome these limitations and further improve clinical outcomes. Similarly, emerging immunologic carcinoid biomarkers may improve diagnostic capabilities. Recent developments in immunotherapeutic and diagnostic modalities of carcinoid management are summarized here.

Novel 2-phenylquinolin-7-yl-derived imidazo[1,5-a]pyrazines as potent insulin-like growth factor-I receptor (IGF-IR) inhibitors.

A series of novel, potent quinolinyl-derived imidazo[1,5-a]pyrazine IGF-IR (IGF-1R) inhibitors--most notably, cis-3-(3-azetidin-1-ylmethylcyclobutyl)-1-(2-phenylquinolin-7-yl)imidazo[1,5-a]pyrazin-8-ylamine (AQIP)--is described. Synthetic details, structure-activity relationships, and in vitro biological activity are reported for the series. Key in vitro and in vivo biological results for AQIP are reported, including: inhibition of ligand-stimulated autophosphorylation of IGF-IR and downstream pathways in 3T3/huIGFIR cells; inhibition of proliferation and induction of DNA fragmentation in human tumor cell lines; a pharmacokinetic profile suitable for once-per-day oral dosing; antitumor activity in a 3T3/huIGFIR xenograft model; and effects on insulin and glucose levels.

1,3-Disubstituted-imidazo[1,5-a]pyrazines as insulin-like growth-factor-I receptor (IGF-IR) inhibitors.

A series of novel 8-amino-1,3-disubstituted-imidazo[1,5-a]pyrazines was designed and synthesized as IGF-IR inhibitors.

3-[6-(2-Dimethylamino-1-imidazol-1-yl-butyl)-naphthalen-2-yloxy]-2,2-dimethyl-propionic acid as a highly potent and selective retinoic acid metabolic blocking agent.

3-[6-(2-Dimethylamino-1-imidazol-1-yl-butyl)-naphthalen-2-yloxy]-2,2-dimethyl-propionic acid and analogs were designed and synthesized as highly potent and selective CYP26 inhibitors, serving as retinoic acid metabolic blocking agents (RAMBAs), with demonstrated in vivo efficacy to increase the half-life of exogenous atRA.

Potent and selective [2-imidazol-1-yl-2-(6-alkoxy-naphthalen-2-yl)-1-methyl-ethyl]-dimethyl-amines as retinoic acid metabolic blocking agents (RAMBAs).

A series of [2-imidazol-1-yl-2-(6-alkoxy-naphthalen-2-yl)-1-methyl-ethyl]-dimethyl-amines were designed and synthesized as CYP26 inhibitors, serving as retinoic acid metabolic blocking agents (RAMBA's).

Regio- and stereospecific syntheses of syn- and anti-1,2-imidazolylpropylamines from the reaction of 1,1'-carbonyldiimidazole with syn- and anti-1,2-amino alcohols.

The regio- and stereospecific conversion of syn- and anti-1,2-amino alcohols to their respective syn- and anti-1,2-imidazolylpropylamines via treatment with 1,1'-carbonyldiimidazole is described. The rationale behind the regio- and stereospecific nature as well as the generality of the reaction is discussed.