Early detection of lung cancer in a real-world cohort via tumor-associated immune autoantibody and imaging combination

Liu, Zhong; Zhang, Feng; Jiang, Jianwen; Zhao, Chenzhao; Zhu, Lu; Liu, Chenbing; Li, Nan; Qiu, Lihong; Shen, Chao; Di, Sheng; Zeng, Qiang

doi:10.3389/fonc.2023.1166894

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…In a trial of more than 15,000 people [ 28 ], researchers found that the 7-TAAb panel demonstrated its potential as a powerful diagnostic tool for LC detection in a real-world cohort, particularly when combined with LDCT, and it showed a greater sensitivity for detecting ground-glass nodules. They highlight the clinical utility of the 7-TAAb panel in facilitating early detection of LC and that it may have significant implications for improving patient outcomes in this population.…”

Section: Discussionmentioning

confidence: 99%

The role of seven tumor-associated autoantibodies in the diagnosis, staging and treatment guidance of lung cancer

Ma,

Wu,

Zhang

et al. 2024

BMC Pulm Med

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Background This study assessed the diagnosis, staging and treatment guidance of lung cancer (LC) based on seven tumor-associated autoantibodies (TAAbs) —p53, PGP9.5, SOX2, GBU4-5, MAGE A1, CAGE, and GAGE7. Methods ELISA was used to determine the TAAb serum levels in 433 patients diagnosed with LC (161 surgical patients) and 76 patients with benign lung disease (16 surgical patients). The statistical characteristic of the TAAbs was compared among patients with different clinicopathological features. Pre- to postoperative changes in TAAb levels were analyzed to determine their value of LC. Results Among all patients, the positive rate of the seven TAAbs was 23.4%, sensitivity was 26.3%, accuracy was 36.3%, specificity was 93.4%, positive predictive value was 95.8%, and negative predictive value was 18.2%; the positive rate for the LC group (26.3%) was significantly higher than that for the benign group (6.6%; P < 0.001). Significant differences in the positive rate of the seven autoantibodies according to age (P < 0.001), smoking history (P = 0.009) and clinical LC stage (P < 0.001) were found. Smoking was positively associated with the positive of TAAbs (Τ = 0.118, P = 0.008). The positive rates of the seven TAAbs for squamous carcinoma (54.5%), other pathological types (44.4%) and poorly differentiated LC (57.1%) were significantly higher than those for the other types. The positive rate of GBU4-5 was highest among all TAAbs, and the SOX2 level in stage III-IV patients was much higher than that in other stages. For patients undergoing surgery, compared with the preoperative levels, the postoperative levels of the 7 markers, particularly p53 (P = 0.027), PGP9.5 (P = 0.007), GAGE7 (P = 0.014), and GBU4-5 (P = 0.002), were significantly different in the malignant group, especially in stage I-II patients, while no clear pre- to postoperative difference was observed in the benign group. Conclusions When the seven TAAbs was positive, it was very helpful for the diagnosis of LC. The 7 TAAbs was valuable for staging and guiding treatment of LC in surgical patients.

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Section: Discussionmentioning

confidence: 99%

The role of seven tumor-associated autoantibodies in the diagnosis, staging and treatment guidance of lung cancer

Ma,

Wu,

Zhang

et al. 2024

BMC Pulm Med

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Autoantibodies in cancer: a systematic review of their clinical role in the most prevalent cancers

Montero-Calle,

Garranzo-Asensio,

Moreno-Casbas

et al. 2024

Front. Immunol.

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Although blood autoantibodies were initially associated with autoimmune diseases, multiple evidence have been accumulated showing their presence in many types of cancer. This has opened their use in clinics, since cancer autoantibodies might be useful for early detection, prognosis, and monitoring of cancer patients. In this review, we discuss the different techniques available for their discovery and validation. Additionally, we discuss here in detail those autoantibody panels verified in at least two different reports that should be more likely to be specific of each of the four most incident cancers. We also report the recent developed kits for breast and lung cancer detection mostly based on autoantibodies and the identification of novel therapeutic targets because of the screening of the cancer humoral immune response. Finally, we discuss unsolved issues that still need to be addressed for the implementation of cancer autoantibodies in clinical routine for cancer diagnosis, prognosis, and/or monitoring.

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Application value of the automated machine learning model based on modified CT index combined with serological indices in the early prediction of lung cancer

Meng,

Zhu,

Xia

2024

Front. Public Health

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Background and objectiveAccurately predicting the extent of lung tumor infiltration is crucial for improving patient survival and cure rates. This study aims to evaluate the application value of an improved CT index combined with serum biomarkers, obtained through an artificial intelligence recognition system analyzing CT features of pulmonary nodules, in early prediction of lung cancer infiltration using machine learning models.Patients and methodsA retrospective analysis was conducted on clinical data of 803 patients hospitalized for lung cancer treatment from January 2020 to December 2023 at two hospitals: Hospital 1 (Affiliated Changshu Hospital of Soochow University) and Hospital 2 (Nantong Eighth People’s Hospital). Data from Hospital 1 were used for internal training, while data from Hospital 2 were used for external validation. Five algorithms, including traditional logistic regression (LR) and machine learning techniques (generalized linear models [GLM], random forest [RF], gradient boosting machine [GBM], deep neural network [DL], and naive Bayes [NB]), were employed to construct models predicting early lung cancer infiltration and were analyzed. The models were comprehensively evaluated through receiver operating characteristic curve (AUC) analysis based on LR, calibration curves, decision curve analysis (DCA), as well as global and individual interpretative analyses using variable feature importance and SHapley additive explanations (SHAP) plots.ResultsA total of 560 patients were used for model development in the training dataset, while a dataset comprising 243 patients was used for external validation. The GBM model exhibited the best performance among the five algorithms, with AUCs of 0.931 and 0.99 in the validation and test sets, respectively, and accuracies of 0.857 and 0.955 in the validation and test groups, respectively, outperforming other models. Additionally, the study found that nodule diameter and average CT value were the most significant features for predicting lung cancer infiltration using machine learning models.ConclusionThe GBM model established in this study can effectively predict the risk of infiltration in early-stage lung cancer patients, thereby improving the accuracy of lung cancer screening and facilitating timely intervention for infiltrative lung cancer patients by clinicians, leading to early diagnosis and treatment of lung cancer, and ultimately reducing lung cancer-related mortality.

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Early detection of lung cancer in a real-world cohort via tumor-associated immune autoantibody and imaging combination

Cited by 3 publications

References 36 publications

The role of seven tumor-associated autoantibodies in the diagnosis, staging and treatment guidance of lung cancer

The role of seven tumor-associated autoantibodies in the diagnosis, staging and treatment guidance of lung cancer

Autoantibodies in cancer: a systematic review of their clinical role in the most prevalent cancers

Application value of the automated machine learning model based on modified CT index combined with serological indices in the early prediction of lung cancer

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