Early response evaluation using primary tumor and nodal imaging features to predict progression-free survival of locally advanced non-small cell lung cancer

Zhang, Nasha; Liang, Ruyi; Gensheimer, Michael F.; Guo, Meiying; Zhu, Hui; J, Yu; Diehn, Maximilian; Loo, Bill W.; Li, Ruijiang; Wu, Jia

doi:10.7150/thno.50565

Cited by 37 publications

(24 citation statements)

References 42 publications

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“…Patients with early-stage LUAD have a good 5-year survival rate because they respond well to current treatments. However, the prognosis in patients with advanced disease remains poor owing to the high incidence of tumor recurrence and distant metastasis 2 , 3 . There is therefore an urgent need to understand the underlying molecular mechanisms responsible for LUAD pathogenesis and to discover new prognostic biomarkers and therapeutic targets for LUAD.…”

Section: Introductionmentioning

confidence: 99%

SAM68 promotes tumorigenesis in lung adenocarcinoma by regulating metabolic conversion via PKM alternative splicing

Zhu¹,

Chen²,

Wang³

et al. 2021

Theranostics

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Background: A metabolic “switch” from oxidative phosphorylation to glycolysis provides tumor cells with energy and biosynthetic substrates, thereby promoting tumorigenesis and malignant progression. However, the mechanisms controlling this metabolic switch in tumors is not entirely clear. Methods: Clinical specimens were used to determine the effect of SAM68 on lung adenocarcinoma (LUAD) tumorigenesis and metastasis, and mouse models and molecular biology assays were performed to elucidate the function and underlying mechanisms in vitro and in vivo . Results: SAM68 mRNA levels were higher in LUAD tissue than in normal lung tissue, indicating that SAM68 expression is upregulated in LUAD. Patients with LUAD with SAM68 high (n = 257) had a higher frequency of tumor recurrence ( p = 0.025) and recurrence-free survival ( p = 0.013) than did those with SAM68 low (n = 257). Patients with SAM68 high mRNA levels (n = 257) were at a higher risk for cancer-related death ( p = 0.006), and had shorter overall survival ( p = 0.044) than did those with SAM68 low . SAM68 promotes tumorigenesis and metastasis of LUAD cells in vitro and in vivo by regulating the cancer metabolic switch. SAM68 drives cancer metabolism by mediating alternative splicing of pyruvate kinase (PKM) pre-mRNAs, and promoting the formation of PKM2. Mechanistically, SAM68 increased the binding of the splicing repressor hnRNP A1 to exon 9 of PKM , thereby enhancing PKM2 isoform formation and PKM2-dependent aerobic glycolysis and tumorigenesis. Conclusions: SAM68 promotes LUAD cell tumorigenesis and cancer metabolic programming via binding of the 351-443 aa region of SAM68 to the RGG motif of hnRNP A1, driving hnRNP A1-dependent PKM splicing, contributing to increased oncogene PKM2 isoform formation and inhibition of PKM1 isoform formation. SAM68 is therefore a promising therapeutic target for the treatment of LUAD.

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Section: Introductionmentioning

confidence: 99%

SAM68 promotes tumorigenesis in lung adenocarcinoma by regulating metabolic conversion via PKM alternative splicing

Zhu¹,

Chen²,

Wang³

et al. 2021

Theranostics

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“…Although radiomics and deep learning have been widely used for decision making of many diseases, such as pneumonia and several tumors, lung cancer remains the most extensively studied field among them (23). By mining large databases and adopting deep learning, biopsy trauma of lung cancer patients can be avoided to the maximum.…”

Section: Radiomics and Deep Learningmentioning

confidence: 99%

Artificial intelligence-assisted decision making for prognosis and drug efficacy prediction in lung cancer patients: a narrative review

Li¹,

Wu²,

Zhao³

et al. 2021

J Thorac Dis

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In this review, we aim to present frontier studies in patients with lung cancer as it related to artificial intelligence (AI)-assisted decision-making and summarize the latest advances, challenges and future trend in this field.Background: Despite increasing survival rate in cancer patients over the last decades, lung cancer remains one of the leading causes of death worldwide. The early diagnosis, accurate evaluation and individualized treatment are vital approaches to improve the survival rate of patients with lung cancer. Thus, decision making based on these approaches requires accuracy and efficiency beyond manpower. Recent advances in AI and precision medicine have provided a fertile environment for the development of AI-based models. These models have the potential to assist radiologists and oncologists in detecting lung cancer, predicting prognosis and developing personalized treatment plans for better outcomes of the patients.Methods: We searched literature from 2000 through July 31 th , 2021 in Medline/PubMed, the Web of Science, the Cochrane Library, ACM Digital Library, INSPEC and EMBASE. Key words such as "artificial intelligence", "AI", "deep learning", "lung cancer", "NSCLC", "SCLC" were combined to identify related literatures. These literatures were then selected by two independent authors. Articles chosen by only one author will be examined by another author to determine whether this article was relative and valuable. The selected literatures were read by all authors and discussed to draw reliable conclusions. Conclusions: AI, especially for those based on deep learning and radiomics, is capable of assisting clinical decision making from many aspects, for its quantitatively interpretation of patients' information and its potential to deal with the dynamics, individual differences and heterogeneity of lung cancer. Hopefully, remaining problems such as insufficient data and poor interpretability may be solved to put AI-based models into clinical practice.

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“…Different algorithms have been tested and some promising application fields were proposed. [9,[14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] A list of the most representative studies are presented in Table 1.…”

Section: Researches Of Radiomics In Response Evaluationmentioning

confidence: 99%

Radiomics in Antineoplastic Agents Development: Application and Challenge in Response Evaluation

Jiazheng,

Lei

2021

Chinese Medical Sciences Journal

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The recent spring up of the antineoplastic agents and the prolonged survival rate bring both challenge and chance to radiological practice. Radiological methods including CT, MRI and PET play an increasingly important role in evaluation the efficacy of these antineoplastic drugs. However, different antineoplastic agents potentially induce different radiological signs, making it a challenge for radiological response evaluation, which depends mainly on one-sided morphological RECIST criteria in the status quo of clinical practice. This brings opportunities for the development of radiomics, which is promising to serve as a surrogate for response evaluations of anti-tumor treatments. In this article, we introduce the basic concepts of radiomics, review the state-of-art radiomics researches with highlights of radiomics application researches in predictions of molecular biomarkers, treatment response, and prognosis. We also provide in-depth analyses on major obstacles and future direction of this new technique in clinical researches on new antineoplastic agents. OVERVIEW OF IMAGING RESPONSE EVALUATIONThe latest few decades have witnessed great advances in antineoplastic agents, which mainly focus on chemotherapy, immunotherapy and molecular targeted therapy. [1][2][3] Following the spring up of the antineoplastic agents and the consequential prolonged survival, traditional gold standard for 

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Early response evaluation using primary tumor and nodal imaging features to predict progression-free survival of locally advanced non-small cell lung cancer

Cited by 37 publications

References 42 publications

SAM68 promotes tumorigenesis in lung adenocarcinoma by regulating metabolic conversion via PKM alternative splicing

SAM68 promotes tumorigenesis in lung adenocarcinoma by regulating metabolic conversion via PKM alternative splicing

Artificial intelligence-assisted decision making for prognosis and drug efficacy prediction in lung cancer patients: a narrative review

Radiomics in Antineoplastic Agents Development: Application and Challenge in Response Evaluation

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