Project description:This pilot phase II studies how well computed tomography (CT) and positron emission tomography (PET) imaging works in detecting disease in patients undergoing surgery for metastatic colorectal cancer. Diagnostic procedures, such as CT and PET scans, done before and during surgery may help find colorectal cancer and help guide surgery
Project description:The Lung3 data set consists of 89 NSCLC patients that were treated at MAASTRO Clinic, The Netherlands. For these patients pretreatment CT scans, tumour delineations and gene expression profiles were available. We used this data set to associate imaging features with gene-expression profiles.
Project description:Segal et al., 2007, Nature Biotechnology (doi:10.1038/nbt1306): Decoding global gene expression programs in liver cancer by noninvasive imaging. ABSTRACT: Paralleling the diversity of genetic and protein activities, pathologic human tissues also exhibit diverse radiographic features. Here we show that dynamic imaging traits in non-invasive computed tomography (CT) systematically correlate with the global gene expression programs of primary human liver cancer. Combinations of twenty-eight imaging traits can reconstruct 78% of the global gene expression profiles, revealing cell proliferation, liver synthetic function, and patient prognosis. Thus, genomic activity of human liver cancers can be decoded by noninvasive imaging, thereby enabling noninvasive, serial and frequent molecular profiling for personalized medicine.
Project description:Introduction: Lung cancer screening by computed tomography (CT) reduces mortality but exhibited high false-positive rates. We established a diagnostic classifier combining chest CT features with bronchial genomics. Materials and Methods: Patients with CT-detected suspected lung cancer were enrolled. The sample collected by bronchial brushing was used for RNA sequencing. R software was applied to build the model. Results: A total of 283 patients, including 183 with lung cancer and 100 with benign lesions, were included. When incorporating genomic data with radiological characteristics, the advanced model yielded 0.903 AUC with 81.1% NPV. Moreover, the classifier performed well regardless of lesion size, location, stage, histologic type, or smoking status. Pathway analysis showed enhanced epithelial differentiation, tumor metastasis, and impaired immunity were predominant in smokers with cancer, whereas tumorigenesis played a central role in non-smokers with cancer. Apoptosis and oxidative stress contributed critically in metastatic lung cancer; by contrast, immune dysfunction was pivotal in locally advanced lung cancer. Conclusions: We devised a minimal-to-noninvasive, efficient diagnostic classifier for smokers and non-smokers with lung cancer, which provides evidence for different mechanisms of cancer development and metastasis associated with smoking. A negative classifier result will help the physician make conservative diagnostic decisions.
Project description:There is a need for non-invasive imaging protocols to support early phase clinical studies for drugs targeting neutrophilic inflammation. The aim of the study was to quantify whole lung neutrophil accumulation in (i) healthy volunteers (HV) following inhaled lipopolysaccharide (LPS) or saline and (ii) stable COPD patients, using radiolabelled autologous neutrophils and single-photon-emission/computed-tomography (SPECT/CT).
Project description:Immunotherapy has improved the prognosis of patients with advanced non-small cell lung
cancer (NSCLC), but only a small subset of patients achieved clinical benefit. The purpose of our study was to integrate multidimensional data using a machine learning method to predict the therapeutic efficacy of immune checkpoint inhibitors (ICIs) monotherapy in patients with advanced NSCLC.The authors retrospectively enrolled 112 patients with stage IIIB-IV NSCLC receiving ICIs monotherapy. The random forest (RF) algorithm was used to establish efficacy prediction models based on five different input datasets, including precontrast computed tomography (CT) radiomic data, postcontrast CT radiomic data, combination of the two CT radiomic data, clinical data, and a combination of radiomic and clinical data. The 5-fold cross-validation was used to train and test the random forest classifier. The performance of the models was assessed according to the area under the curve (AUC) in the receiver operating characteristic (ROC) curve. Among these models(RF MLP LR XGBoost), our reproduced onnx models have better performance, especially for random forest. The response variable with a value (1/0) indicates the (efficacy/inefficacy) of PD-1/PD-L1 monotherapy in patients with advanced NSCLC
Project description:This early phase I trial studies how well 18F-fluoroazomycin arabinoside positron emission tomography (PET)-computed tomography (CT) works in diagnosing solid tumors. Using 18F-fluoroazomycin arabinoside with PET-CT may help doctors plan better treatment for patients with solid tumors. 18F-fluoroazomycin arabinoside may help to show how much oxygen is present in a tumor during a PET-CT scan.
Project description:Bone collagen is an important organic material for isotopic measurement, radiocarbon and paleoproteomic analyzes, to provide information on diet, dating, taxonomic identification. Current paleoproteomics methods are destructive and require from a few milligrams to several tenths of milligrams of bone for analysis. In many cultures, bones are raw materials for artefact which are conserved in museum which hampers to damage these precious objects during sampling. Here, we describe a minimal sampling method that identifies collagen, taxonomy and post-translational modifications from Holocene and Upper Pleistocene bones dated to 130,000 and 5,000 years ago using dermatological skin tape-discs for sampling. The sampled bone micro-powder were digested following our highly optimized eFASP protocol, then analyzed by MALDI FTICR MS and LC-MS/MS for identifying the genus taxa of the bones. We show that this low-invasive sampling does not deteriorate the bones and achieves results similar to those obtained by destructive sampling. Moreover, this sampling method can be performed at archaeological sites or in museums.
Project description:This pilot clinical trial studies copper Cu 64 (64Cu) tetra-azacyclododecanetetra-acetic acid (DOTA)-trastuzumab positron emission tomography (PET)/computed tomography (CT) in studying patients with gastric, or stomach cancer. Diagnostic procedures, such as copper Cu 64-DOTA-trastuzumab PET/CT, may help doctors study the characteristics of tumors and choose the best treatment.
Project description:The Lung3 data set consists of 89 NSCLC patients that were treated at MAASTRO Clinic, The Netherlands. For these patients pretreatment CT scans, tumour delineations and gene expression profiles were available. We used this data set to associate imaging features with gene-expression profiles. 89 samples from NSCLC patients. Samples were obtained by biopsies from cancerous tissue. Tumors were classified as Adenocarcinoma, Papillary, NOS; Squamous Cell Carcinoma, NOS; Non-Small Cell; Papillary Type; or Solid Type.
