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1.
Curr Oncol ; 30(10): 8865-8871, 2023 09 28.
Article in English | MEDLINE | ID: mdl-37887540

ABSTRACT

Osimertinib has become the preferred first-line therapy for epidermal growth factor receptor (EGFR) mutation-positive metastatic non-small cell lung cancer (NSCLC) in recent years. Originally, it was approved for second-line treatment after epidermal growth factor receptor EGFR tyrosine kinase inhibitors (TKIs) of the first and second generations had failed and EGFR T790M had emerged as a mode of resistance. Osimertinib itself provokes a wide array of on- and off-target molecular alterations that can limit therapeutic success. Liquid biopsy ctDNA (circulating tumor DNA) analysis by hybrid capture (HC) next-generation sequencing (NGS) can help to identify alterations in a minimally invasive way and allows for the detection of common as well as rare resistance alterations. We describe a young female patient who was initially diagnosed with metastatic EGFR L858R-positive NSCLC. She received EGFR TKI therapy at different timepoints during the course of the disease and developed sequential EGFR resistance alterations (EGFR T790M and C797S). In the course of her disease, resistance alteration became undetectable, and the tumor was successfully rechallenged with the original first-generation EGFR TKI as well as osimertinib and altogether showed prolonged response despite a prognostically negative TP53 alteration. To date, the patient has been alive for more than seven years, though initially diagnosed with a heavy metastatic burden.


Subject(s)
Carcinoma, Non-Small-Cell Lung , Lung Neoplasms , Female , Humans , Carcinoma, Non-Small-Cell Lung/drug therapy , Carcinoma, Non-Small-Cell Lung/genetics , Carcinoma, Non-Small-Cell Lung/pathology , Lung Neoplasms/drug therapy , Lung Neoplasms/genetics , Lung Neoplasms/pathology , ErbB Receptors/genetics , Mutation , Protein Kinase Inhibitors/therapeutic use , Protein Kinase Inhibitors/pharmacology
2.
Curr Oncol ; 30(2): 1692-1698, 2023 01 30.
Article in English | MEDLINE | ID: mdl-36826091

ABSTRACT

HER2-targeted therapy is currently the subject of several studies in lung cancer and other solid tumors using either tyrosine kinase inhibitors (TKI) or targeted-antibody-drug conjugates. We describe a 61-year-old female patient with HER2 mutated adenocarcinoma of the lungs who received chemo-immunotherapy, followed by trastuzumab deruxtecan (T-DXd) and third-line Ramucirumab/Docetaxel at disease progression. Plasma ctDNA monitoring was obtained at 12 timepoints during therapy and revealed HER2 mutation allele frequencies that corresponded to the clinical course of disease. HER2-targeted T-DXd therapy resulted in a profound clinical response and may be an option for NSCLC patients carrying an activated HER2 mutation. Longitudinal liquid biopsy quantification of the underlying driver alteration can serve as a powerful diagnostic tool to monitor course of therapy.


Subject(s)
Carcinoma, Non-Small-Cell Lung , Immunoconjugates , Lung Neoplasms , Female , Humans , Middle Aged , Receptor, ErbB-2/genetics , Antibodies, Monoclonal, Humanized/therapeutic use , Immunoconjugates/therapeutic use , Carcinoma, Non-Small-Cell Lung/drug therapy , Lung Neoplasms/drug therapy , Liquid Biopsy
3.
Invest Radiol ; 58(2): 126-130, 2023 02 01.
Article in English | MEDLINE | ID: mdl-35926075

ABSTRACT

OBJECTIVE: The aim of this study was to evaluate whether a 3-dimensional (3D) camera can outperform highly trained technicians in precision of patient positioning and whether this transforms into a reduction in patient exposure. MATERIALS AND METHODS: In a single-center study, 3118 patients underwent computer tomography (CT) scans of the chest and/or abdomen on a latest generation single-source CT scanner supported with an automated patient positioning system by 3D camera. One thousand five hundred fifty-seven patients were positioned laser-guided by a highly trained radiographer (camera off) and 1561 patients with 3D camera (camera on) guidance. Radiation parameters such as effective dose, organ doses, CT dose index, and dose length product were analyzed and compared. Isocenter accuracy and table height were evaluated between the 2 groups. RESULTS: Isocenter positioning was significantly improved with the 3D camera ( P < 0.001) as compared with visual laser-guided positioning. Absolute table height differed significantly ( P < 0.001), being higher with camera positioning (165.6 ± 16.2 mm) as compared with laser-guided positioning (170.0 ± 20.4 mm). Radiation exposure decreased using the 3D camera as indicated by dose length product (321.1 ± 266.6 mGy·cm; camera off: 342.0 ± 280.7 mGy·cm; P = 0.033), effective dose (3.3 ± 2.7 mSv; camera off: 3.5 ± 2.9; P = 0.053), and CT dose index (6.4 ± 4.3 mGy; camera off: 6.8 ± 4.6 mGy; P = 0.011). Exposure of radiation-sensitive organs such as colon ( P = 0.015) and red bone marrow ( P = 0.049) were also lower using the camera. CONCLUSIONS: The introduction of a 3D camera improves patient positioning in the isocenter of the scanner, which results in a lower and also better balanced dose reduction for the patients.


Subject(s)
Radiation Exposure , Tomography, X-Ray Computed , Humans , Tomography, X-Ray Computed/methods , Radiation Dosage , Computers , Patient Positioning/methods
4.
Radiologe ; 60(10): 952-958, 2020 Oct.
Article in German | MEDLINE | ID: mdl-32638030

ABSTRACT

Artificial intelligence (AI) algorithms are increasingly used in radiology. The main areas of application are, for example, the detection of lung lesions and the diagnosis of chronic obstructive and interstitial lung diseases. The aim of our study was to train and evaluate a package of algorithms that analyze data from computed tomographic (CT) images of the chest and provide quantitative measurements to the radiologist. The following algorithms were trained: lung lesion detection and measurement, lung lobe segmentation, vessel segmentation and measurement, coronary calcium scoring, measurement and density analysis of vertebral bodies. AI-supported algorithms will become part of daily routine of the radiologist in the future. Tasks that do not require medical expertise can be performed by AI. However, our results show that, based on the current accuracy, verification by an experienced radiologist is necessary.


Subject(s)
Artificial Intelligence , Lung Diseases, Interstitial , Tomography, X-Ray Computed , Algorithms , Decision Support Systems, Clinical , Humans , Lung Diseases, Interstitial/diagnostic imaging , Thorax
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