Establishment of a mathematical model for predicting malignancy of lung cancer complicated with Talaromyces Marneffei infection and its chest imaging characteristics

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.

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

JMIR Medical Informatics ◽

10.2196/21033 ◽

2020 ◽

Author(s):

Yiling Feng ◽

Xiaoping Li ◽

Yanqin Wang ◽

Shar Lenepe

Keyword(s):

Lung Cancer ◽

Chest Imaging ◽

Talaromyces Marneffei ◽

Imaging Characteristics

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NIMG-07. BEYOND RANDOM AND NUMERIC LESIONS: DIFFERENT IMAGING PATTERNS OF CENTRAL NERVOUS SYSTEM METASTASIS BASED ON PRIMARY TUMOR LOCATION AND MOLECULAR PROFILE

Neuro-Oncology ◽

10.1093/neuonc/noab196.507 ◽

2021 ◽

Vol 23 (Supplement_6) ◽

pp. vi128-vi128

Author(s):

Júlia Magalhães ◽

Raquel Moreno ◽

Jorge Takahashi ◽

Leandro Lucato ◽

Carlos Silva

Keyword(s):

Lung Cancer ◽

Central Nervous System ◽

Nervous System ◽

Brain Metastasis ◽

Primary Tumor ◽

Molecular Profile ◽

Imaging Findings ◽

Central Nervous System Metastasis ◽

Cns Metastasis ◽

Imaging Characteristics

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.

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Imaging Features and Patterns of Metastasis in Non-Small Cell Lung Cancer with RET Rearrangements

Cancers ◽

10.3390/cancers12030693 ◽

2020 ◽

Vol 12 (3) ◽

pp. 693 ◽

Cited By ~ 2

Author(s):

Subba R. Digumarthy ◽

Dexter P. Mendoza ◽

Jessica J. Lin ◽

Marguerite Rooney ◽

Andrew Do ◽

...

Keyword(s):

Lung Cancer ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Small Cell ◽

Imaging Features ◽

Small Cell Lung ◽

Primary Tumors ◽

Imaging Characteristics ◽

Metastatic Patterns ◽

Pre Treatment

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.

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A mathematical model of drug resistance applied to treatment for small-cell lung cancer.

Journal of Clinical Oncology ◽

10.1200/jco.1988.6.3.457 ◽

1988 ◽

Vol 6 (3) ◽

pp. 457-461 ◽

Cited By ~ 19

Author(s):

W M Gregory ◽

B G Birkhead ◽

R L Souhami

Keyword(s):

Lung Cancer ◽

Mathematical Model ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Doubling Time ◽

Single Agent ◽

Small Cell ◽

High Dose ◽

Small Cell Lung ◽

Tumor Doubling Time

A mathematical model has been applied to patients with small-cell lung cancer (SCLC) in order to estimate the proportions of resistant and sensitive tumor at presentation, and the efficacy of the treatment, measured in terms of proportions of tumor killed with each cycle of therapy. The model uses estimates of tumor volume obtained from computed tomographic (CT) scans of the chest before each course of chemotherapy. Application of the model to a trial using single-agent high-dose cyclophosphamide (HDC) showed that HDC killed approximately 94% of the sensitive tumor on each application, but that the proportion of tumor resistant to HDC rose from an average of 1% to an average of 15% after the first cycle, assuming a 30-day tumor doubling time. These estimates proved fairly insensitive to different assumptions about tumor doubling time and inaccuracies in volume measurement and may thus provide a useful additional evaluation technique for some clinical trials.

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Systems biology in personalized medicine in oncology: A case study in non-small cell lung cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2012.30.15_suppl.e21096 ◽

2012 ◽

Vol 30 (15_suppl) ◽

pp. e21096-e21096

Author(s):

Fortunato Bianconi ◽

Katia Perruccio ◽

Vienna Ludovini ◽

Elisa Baldelli ◽

Guido Bellezza ◽

...

Keyword(s):

Lung Cancer ◽

Mathematical Model ◽

Cell Proliferation ◽

Survival Analysis ◽

Systems Biology ◽

Small Cell Lung Cancer ◽

Gene Amplification ◽

Small Cell ◽

Small Cell Lung ◽

Kras Mutations

e21096 Background: Systems biology together with translational oncology is a new approach to discover sensitive pathways in specific cancers. In this study, we propose a computational modeling technique to investigate the possible effects various alterations, such as protein overexpression, gene amplification or mutations, may have on signaling, through of the EGFR and IGF1R pathways, in non-small cell lung cancer (NSCLC). Methods: EGFR and IGF1R pathways and the downstream MAPK and PIK3 networks have been reproduced through a mathematical model. One hundred-twenty five tumors from surgical NSCLC patients were evaluated for EGFR and IGF1R protein expression, by immunohistochemistry (IHC) and gene amplification, by fluorescence in situ hybridization (FISH). KRAS mutations (exons 2 and 3) were evaluated by direct sequencing Results: To correlate EGFR and IGF1R expression levels, and KRAS mutations to tumor cell proliferation, we focused on the ERK signaling pathway, which plays a central role in several steps of cancer development including proliferation and cancer cell migration. The mathematical model predicts a relationship between a simultaneous high expression level of both receptors and a modification on ERK time behavior, implying a stronger attitude for cell proliferation. Furthermore KRAS activating mutations predict high level of active ERK and high probability to have cell proliferation. Cell growth can be closely related to disease progression and act, in survival analysis, as DFS estimator. Patients with concomitant IGF1R/EGFR FISH/IHC positivity had a worse DFS ( p=0.005). KRAS mutations have a statistically significant shorter DFS (p<0.001) as well Conclusions: We propose a Systems Biology approach, combined with Translational Oncology methodologies, to understand the interaction between EGFR, IGF1R and KRAS pathways in NSCLC. Computational model predictions confirm clinical evidences of survival analysis. Future work will validate our model with experiments on various NSCLC cell cultures and further investigate the response to drug administration. We thank AIRC and Fondazione Cassa di Risparmio for supporting the study.

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