Treatment of Lung Cancer Complicated with Infection of Talaromyces Marneffei Based on Chest Imaging Characteristics Monitoring (Preprint)

10.2196/21033 ◽  
2020 ◽  
Author(s):  
Yiling Feng ◽  
Xiaoping Li ◽  
Yanqin Wang ◽  
Shar Lenepe
2020 ◽  
Author(s):  
Yiling Feng ◽  
Xiaoping Li ◽  
Yanqin Wang ◽  
Shar Lenepe

BACKGROUND Background: At present, although the infection of Talaromyces Marneffei has been known at home and abroad, there are few reports of Talaromyces Marneffei in lung cancer. OBJECTIVE Objective: The objective is to explore the diagnosis and treatment process of lung cancer patients with infection of Talaromyces Marneffei and its chest imaging characteristics, so as to improve the clinicians' realization of the disease. METHODS Method: The patients with lung cancer and infection of Talaromyces Marneffei (observation group) and the patients with infection of Talaromyces Marneffei (control group) are taken as the study objects, and the clinical characteristics and chest CT (computed tomography) imaging characteristics of the two groups are compared and summarized. RESULTS Results: The number of male patients infected with Talaromyces Marneffei is significantly higher than that of female patients (P < 0.05). The symptoms of cough and expectoration in the observation group are more than those in the control group (P < 0.05). The main imaging features of the observation group are obvious enhancement of focus enhancement scanning, strip shape and nodule, and the situation of obvious enhancement of focus enhancement scanning in the observation group is significantly higher than that in the control group (P < 0.05). CONCLUSIONS Conclusion: The clinical and imaging features of lung cancer and Talaromyces Marneffei infection overlap. When the lung lesions of patients with Talaromyces Marneffei have significant malignant signs, the possibility of lung cancer should be considered.


2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi128-vi128
Author(s):  
Júlia Magalhães ◽  
Raquel Moreno ◽  
Jorge Takahashi ◽  
Leandro Lucato ◽  
Carlos Silva

Abstract The purpose of this exhibit is to discuss different imaging patterns of central nervous system (CNS) metastasis based on their primary cancer site and to review the recent literature of the particularities of CNS metastasis distribution in the era of molecular advancement in oncology. Selected cases extracted from our institutions database will be presented. The cases will be didactically organized to illustrate the most common imaging characteristics and distribution of brain metastasis based on their organ of origin, such as lung, breast, renal, skin, testicle and gastrointestinal tract. (SCHROEDER T. et al., J Neurooncol. 2020). We will also discuss the correlation between tumor imaging findings and genetic profile. We intend to review well-known CNS metastasis imaging patterns, as preferential involvement of the posterior fossa and anatomic watershed areas in cases of lung cancer (TAKANO, K. et al. Neuro-Oncology, 2016) and the rarity of parenchyma metastasis from prostate cancer (HATZOGLOU V. et al, J Neuroimaging. 2014). We will also demonstrate newly described imaging findings in correlation with primary tumors genetic mutations, such as higher incidence of leptomeningeal involvement in triple negative breast cancer and increase in the number of brain lesions in cases of EGFR positive lung cancer. Familiarity with the most prevalent imaging characteristics of central nervous system metastasis helps oncologists and radiologists not to miss out a CNS progression in case of a known tumor, and also helps to direct systemic investigation of a primary tumor when brain metastasis is the initial presentation. The correlation between molecular profile and the most common sites of CNS involvement can help on treatment planning, including brain radiation (Yanagihara TK,et al., Tomography. 2017), and also bring to discussion the mechanisms of tumor dissemination, which can be targets for future treatments.


Cancers ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 693 ◽  
Author(s):  
Subba R. Digumarthy ◽  
Dexter P. Mendoza ◽  
Jessica J. Lin ◽  
Marguerite Rooney ◽  
Andrew Do ◽  
...  

Rearranged during transfection proto-oncogene (RET) fusions represent a potentially targetable oncogenic driver in non-small cell lung cancer (NSCLC). Imaging features and metastatic patterns of advanced RET fusion-positive (RET+) NSCLC are not well established. Our goal was to compare the imaging features and patterns of metastases in RET+, ALK+ and ROS1+ NSCLC. Patients with RET+, ALK+, or ROS1+ NSCLC seen at our institution between January 2014 and December 2018 with available pre-treatment imaging were identified. The clinicopathologic features, imaging characteristics, and the distribution of metastases were reviewed and compared. We identified 215 patients with NSCLC harboring RET, ALK, or ROS1 gene fusion (RET = 32; ALK = 116; ROS1 = 67). Patients with RET+ NSCLC were older at presentation compared to ALK+ and ROS1+ patients (median age: RET = 64 years; ALK = 51 years, p < 0.001; ROS = 54 years, p = 0.042) and had a higher frequency of neuroendocrine histology (RET = 12%; ALK = 2%, p = 0.025; ROS1 = 0%, p = 0.010). Primary tumors in RET+ patients were more likely to be peripheral (RET = 69%; ALK = 47%, p = 0.029; ROS1 = 36%, p = 0.003), whereas lobar location, size, and density were comparable across the three groups. RET+ NSCLC was associated with a higher frequency of brain metastases at diagnosis compared to ROS1+ NSCLC (RET = 32%, ROS1 = 10%; p = 0.039. Metastatic patterns were otherwise similar across the three molecular subgroups, with high incidences of lymphangitic carcinomatosis, pleural metastases, and sclerotic bone metastases. RET+ NSCLC shares several distinct radiologic features and metastatic spread with ALK+ and ROS1+ NSCLC. These features may suggest the presence of RET fusions and help identify patients who may benefit from further molecular genotyping.


2021 ◽  
Vol Volume 14 ◽  
pp. 5005-5013
Author(s):  
Fanhai Lin ◽  
Zhenming Yang ◽  
Ye Qiu ◽  
Wen Zeng ◽  
Guangnan Liu ◽  
...  

2021 ◽  
Author(s):  
Tyler M Grey ◽  
Abdullah Alabousi ◽  
Mostafa Alabousi ◽  
Ehsan A Haider

Abstract Background: Lung cancer is one of the leading causes of cancer-related mortality worldwide. Its poor prognosis is associated with late detection and high recurrence rates. We aimed to determine if certain imaging characteristics of lung cancer recurrence were predictors of extra-pulmonary metastatic disease.Methods: We conducted a retrospective study of all patients at our institution with lung cancer recurrence detected on post-treatment imaging between January 2014-October 2019. Research ethics board approval was obtained. Included patients underwent pre-treatment imaging, surgical resection, and post-treatment imaging. Imaging characteristics and pathological findings of the pulmonary lesions were analyzed. Univariate logistic regression was performed to assess for potential predictors of extra-pulmonary metastatic disease. The variables evaluated were age, gender, original and recurrent lesion size and imaging characteristics, recurrence location, presence of chest wall or mediastinal invasion, lymphadenopathy, and malignancy subtype. Results: 76 patients were included (33 males; mean age 70.9, standard deviation [SD] 7.7). The primary lesions were adenocarcinoma (N=50), squamous cell carcinoma (N=21), and other (N=5). The mean time to recurrence was 24.3 months (SD=18.8) from date of surgical excision. The two significant predictors of extra-pulmonary metastatic disease were: having >1 recurrent lesion (odds ratio [OR], 8.1; p=0.004), and the presence of suspicious lymphadenopathy at the time of recurrence (OR, 14.1; p<0.001).Conclusion: In lung cancer recurrence, the presence of >1 recurrent lesion and suspicious lymphadenopathy at the time of recurrence were significant predictors of extra-pulmonary metastatic disease. These findings may help guide the risk stratification and management of patients with recurrent lung cancer.


2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Fanhai Lin ◽  
Ye Qiu ◽  
Wen Zeng ◽  
Yi Liang ◽  
Jianquan Zhang

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