Recent advances in lung cancer research: unravelling the future of treatment

Bertolaccini, Luca; Casiraghi, Monica; Uslenghi, Clarissa; Maiorca, Sebastiano; Spaggiari, Lorenzo

doi:10.1007/s13304-024-01841-3

Cited by 6 publications

(1 citation statement)

References 62 publications

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“…The pathology of lung cancer is very important in personalized and precise treatment, 1,2 but in clinical practice, we inevitably encounter a challenge, which is that sometimes only a small number of tumor cells can be found after biopsy, and even necrotic cells can only be seen. Even after immunohistochemistry, the specific pathological type cannot be determined.…”

Section: Introductionmentioning

confidence: 99%

Application of Chest CT Imaging Feature Model in Distinguishing Squamous Cell Carcinoma and Adenocarcinoma of the Lung

Liu,

He,

Luo

2024

CMAR

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In situations where pathological acquisition is difficult, there is a lack of consensus on distinguishing between adenocarcinoma and squamous cell carcinoma from imaging images, and each doctor can only make judgments based on their own experience. This study aims to extract imaging features of chest CT, extract sensitive factors through logistic univariate and multivariate analysis, and model to distinguish between lung squamous cell carcinoma and lung adenocarcinoma. Methods: We downloaded chest CT scans with clear diagnosis of adenocarcinoma and squamous cell carcinoma from The Cancer Imaging Archive (TCIA), extracted 19 imaging features by a radiologist and a thoracic surgeon, including location, spicule, lobulation, cavity, vacuolar sign, necrosis, pleural traction sign, vascular bundle sign, air bronchogram sign, calcification, enhancement degree, distance from pulmonary hilum, atelectasis, pulmonary hilum and bronchial lymph nodes, mediastinal lymph nodes, interlobular septal thickening, pulmonary metastasis, adjacent structures invasion, pleural effusion. Firstly, we apply the glm function of R language to perform logistic univariate analysis on all variables to select variables with P < 0.1. Then, perform logistic multivariate analysis on the selected variables to obtain a predictive model. Next, use the roc function in R language to calculate the AUC value and draw the ROC curve, use the val.prob function in R language to draw the Calibrat curve, and use the rmda package in R language to draw the DCA curve and clinical impact curve. At the same time, 45 patients diagnosed with lung squamous cell carcinoma and lung adenocarcinoma through surgery or biopsy in the Radiotherapy Department and Thoracic Surgery Department of our hospital from 2023 to 2024 were included in the validation group. The chest CT features were jointly determined and recorded by the two doctors mentioned above and included in the validation group. The included image feature data are complete and does not require preprocessing, so directly entering statistical calculations. Perform ROC curves, calibration curves, DCA, and clinical impact curves in the validation group to further validate the predictive model. If the predictive model performs well in the validation group, further draw a nomogram to demonstrate. Results: This study extracted 19 imaging features from the chest CT scans of 75 patients downloaded from TCIA and finally selected 18 complete data for analysis. First, univariate analysis and multivariate analysis were performed, and a total of 5 variables were obtained: spicule, necrosis, air bronchogram Sign, atelectasis, pulmonary hilum and bronchial lymph nodes. After conducting modeling analysis with AUC = 0.887, a validation group was established using clinical cases from our hospital, Draw ROC curve with AUC = 0.865 in the validation group, evaluate the accuracy of the model through Calibrate calibration curve, evaluate the reliability of the model in clinical practice through DCA curve, and further evaluate the practica...

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Section: Introductionmentioning

confidence: 99%

Application of Chest CT Imaging Feature Model in Distinguishing Squamous Cell Carcinoma and Adenocarcinoma of the Lung

Liu,

He,

Luo

2024

CMAR

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Symptom clusters and impact on quality of life in lung cancer patients undergoing chemotherapy

Luo,

Zhang,

Mao

et al. 2024

Qual Life Res

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Purpose To identify symptom clusters (SCs) in lung cancer patients undergoing chemotherapy and explore their impact on health-related quality of life (HRQoL). Methods Patients were invited to complete the Chinese version of the M.D. Anderson Symptom Inventory with the Lung Cancer Module and the Quality of Life Questionnaire-core 30. Network analysis was employed to identify SCs. The associations between SCs and each function of HRQoL were examined using the Pearson correlation matrix. Multiple linear regression was applied to analyze the influencing factors of each function of HRQoL. Results A total of 623 lung cancer patients who were receiving chemotherapy were recruited. The global health status of lung cancer patients was 59.71 ± 21.09, and 89.73% of patients developed symptoms. Three SCs (Somato-psychological SC, Respiratory SC, and Gastrointestinal SC) were identified, and Somato-psychological SC and Gastrointestinal SC were identified as influencing factors for HRQoL in lung cancer patients. Conclusion Most lung cancer patients who undergo chemotherapy experience a range of symptoms, which can be categorized into three SCs. The Somato-psychological SC and Gastrointestinal SC negatively impacted patients' HRQoL. Health care providers should prioritize monitoring these SCs to identify high-risk patients early and implement targeted preventive and intervention measures for each SC, aiming to alleviate symptom burden and enhance HRQoL. Supplementary Information The online version contains supplementary material available at 10.1007/s11136-024-03778-x.

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Changing Patterns of Lung Cancer Mortality in Andalusia: Insights From Age-period-cohort Analysis and Historical Smoking Trends

Cayuela,

Gaeta,

Piury-Pinzón

et al. 2025

Open Respiratory Archives

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Recent advances in lung cancer research: unravelling the future of treatment

Cited by 6 publications

References 62 publications

Application of Chest CT Imaging Feature Model in Distinguishing Squamous Cell Carcinoma and Adenocarcinoma of the Lung

Application of Chest CT Imaging Feature Model in Distinguishing Squamous Cell Carcinoma and Adenocarcinoma of the Lung

Symptom clusters and impact on quality of life in lung cancer patients undergoing chemotherapy

Changing Patterns of Lung Cancer Mortality in Andalusia: Insights From Age-period-cohort Analysis and Historical Smoking Trends

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