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cell, radiation, article, pubmed, data, radiotherapy, cancer, google, scholar, rsi, patients, tumor, sensitivity, set, lcdigitalrt, lung, cohort, fig, radiomics, cas, analysis, features, radiosensitivity, study, score, cells, validation, clinical, response, nonsmall, groups, differences, auc, machine, gtvp, central, signature, patient, test, group, multiple, training, singlecell, based, gene, efficacy, area, feature, predict, jxch,

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gov/ccg/research/genome-sequencing/tcga pre-lcdigital-rt radiomics signature additional information publisher' radiogenomics composite signature single-cell transcriptional diversity ct contrast-enhanced sequences sun yat-sen university lcdigital-rt radiogenetic signature subsequent lcdigital-rt phenotypes ct-based radiomics nomogram ten-fold cross validation introduction radiotherapy bmc cancer pre lcdigital-rt signatures radiomics-based multiomics combinations stage ib–iv nsclc open-source software 3dslicer radiogenomic signature lcdigital-rt data availability bulk rna sequencing project numbers tcga-luad response variable decision-making ali abd al-hameed abbreviations nsclc ct image registration develop pre lcdigital-rt glutathione-s-transferase genes squamous cell carcinoma state privacy rights privacy choices/manage cookies radiogenomics biomarker cell-cell communication feature-expression heat maps scrna-seq samples obtained single-cell atlas receiver operating characteristic medical ethics design image computing platform image features included radiogenomic single signature higher-order information external validation sets single-cell landscape single-cell level single cell landscape single-cell profiling lintong yao & haiyu zhou enhanced ct sequences gradient boosting algorithms npj genomic med

Questions {❓}

• Can quantitative peritumoral CT radiomics features predict the prognosis of patients with non-small cell lung cancer?
• Could the underlying biological basis of prognostic radiomics and deep learning signatures be explored in patients with lung cancer?
• What is a support vector machine?

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         headline:Prediction of radiosensitivity in non-small cell lung cancer based on computed tomography and tumor genomics: a multiple real world cohort study
         description:The varying degrees of radiotherapy sensitivity of tumors limit the efficacy of tumor radiotherapy. In this research, based on single cell sequence data we used radiomics to help identify and screen feature signatures to distinguish varying radiosensitivity in different regions of the target area of non-small cell lung cancer can provide a new pattern to assess sensitivity of radiotherapy and assist in clinical decision-making. This retrospective study included CT radiology data from 454 patients diagnosed with non-small cell lung cancer in multiple real-world cohorts prior to radiotherapy. The tumor primary target area was delineated on a training set (n = 154) and segmented to obtain a radiogenomic single signature. The radiogenomic signature LCDigital-RT, which can predict radiosensitivity, was developed by combining transcriptome sequencing signature radiosensitivity index and validated on two independent external validation sets (n = 74) and (n = 160). Besides, we also described the single-cell landscape of non-small cell lung cancer with different radiosensitivity, attempting to explain the potential biological mechanism at the single-cell level. By constructing solely from the single radiomics feature signature, pre LCDigital-RT can effectively identify populations with differences in radiation sensitivity in non-small cell lung cancer, with AUCs of 0.759, 0.728 and 0.745 for the training and two external validation sets, respectively. However, LCDigital-RT has a greater advantage, with a training set AUC of 0.837, which has been well validated in the JXCH cohort (AUC = 0.789) and GDPH cohort (AUC = 0.791). With the help of LCDigital-RT, patients can be divided into radiation sensitive and radiation resistant groups, and there is a significant difference in the characteristics of primary tumor lesions between the two groups. We have also enriched the interpretability of our radiogenomic features in biology at the single-cell level, demonstrating their enormous value in clinical translational research. We have developed an LCDigital RT prediction tool that will help predict populations at risk of radiation sensitivity differences. By visualizing the thermal map of the primary tumor area, we can assist in the development of radiotherapy plans, reduce the occurrence of radiation toxicity events, and improve radiotherapy efficacy. At the same time, it provides a reference basis for evaluating radiation sensitivity from imaging, genetics, and other aspects.
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      headline:Prediction of radiosensitivity in non-small cell lung cancer based on computed tomography and tumor genomics: a multiple real world cohort study
      description:The varying degrees of radiotherapy sensitivity of tumors limit the efficacy of tumor radiotherapy. In this research, based on single cell sequence data we used radiomics to help identify and screen feature signatures to distinguish varying radiosensitivity in different regions of the target area of non-small cell lung cancer can provide a new pattern to assess sensitivity of radiotherapy and assist in clinical decision-making. This retrospective study included CT radiology data from 454 patients diagnosed with non-small cell lung cancer in multiple real-world cohorts prior to radiotherapy. The tumor primary target area was delineated on a training set (n = 154) and segmented to obtain a radiogenomic single signature. The radiogenomic signature LCDigital-RT, which can predict radiosensitivity, was developed by combining transcriptome sequencing signature radiosensitivity index and validated on two independent external validation sets (n = 74) and (n = 160). Besides, we also described the single-cell landscape of non-small cell lung cancer with different radiosensitivity, attempting to explain the potential biological mechanism at the single-cell level. By constructing solely from the single radiomics feature signature, pre LCDigital-RT can effectively identify populations with differences in radiation sensitivity in non-small cell lung cancer, with AUCs of 0.759, 0.728 and 0.745 for the training and two external validation sets, respectively. However, LCDigital-RT has a greater advantage, with a training set AUC of 0.837, which has been well validated in the JXCH cohort (AUC = 0.789) and GDPH cohort (AUC = 0.791). With the help of LCDigital-RT, patients can be divided into radiation sensitive and radiation resistant groups, and there is a significant difference in the characteristics of primary tumor lesions between the two groups. We have also enriched the interpretability of our radiogenomic features in biology at the single-cell level, demonstrating their enormous value in clinical translational research. We have developed an LCDigital RT prediction tool that will help predict populations at risk of radiation sensitivity differences. By visualizing the thermal map of the primary tumor area, we can assist in the development of radiotherapy plans, reduce the occurrence of radiation toxicity events, and improve radiotherapy efficacy. At the same time, it provides a reference basis for evaluating radiation sensitivity from imaging, genetics, and other aspects.
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         Non-small cell lung cancer
         Radiosensitivity
         Radiotherapy target area
         Radiogenomics
         Single cell sequencing technology
         Pneumology/Respiratory System
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