The lymph node status and histologic subtypes influenced the effect of postoperative radiotherapy on patients with N2 positive IIIA non–small cell lung cancer

Yuan, Chongze; Tao, Xiaoting; Zheng, Deyou; Pan, Yunjian; Ye, Ting; Hu, Hong; Xiang, Jiaqing; Zhang, Yawei; Chen, Haiquan; Sun, Yihua

doi:10.1002/jso.25308

Cited by 31 publications

(38 citation statements)

References 28 publications

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“…Adenocarcinoma (ADC) and squamous cell carcinoma (SCC) are the most common subtypes of NSCLC [2]. Different pathological subtypes have distinct phenotypic and biological characteristics, which are directly related to the clinical treatment and outcome [3][4][5]. With advances in targeted therapies, molecularly targeted agents that inhibit epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) can significantly improve the efficacy and reduce the toxicity of NSCLC, as almost all these gene mutations are found in ADC [6,7].…”

Section: Introductionmentioning

confidence: 99%

Machine learning based on clinico-biological features integrated 18F-FDG PET/CT radiomics for distinguishing squamous cell carcinoma from adenocarcinoma of lung

Ren

Zhang

et al. 2020

Eur J Nucl Med Mol Imaging

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Purpose To develop and validate a clinico-biological features and 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) radiomic-based nomogram via machine learning for the pretherapy prediction of discriminating between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) in non-small cell lung cancer (NSCLC). Methods A total of 315 NSCLC patients confirmed by postoperative pathology between January 2017 and June 2019 were retrospectively analyzed and randomly divided into the training (n = 220) and validation (n = 95) sets. Preoperative clinical factors, serum tumor markers, and PET, and CT radiomic features were analyzed. Prediction models were developed using the least absolute shrinkage and selection operator (LASSO) regression analysis. The performance of the models was evaluated and compared by the area under receiver-operator characteristic (ROC) curve (AUC) and DeLong test. The clinical utility of the models was determined via decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. Results In total, 122 SCC and 193 ADC patients were enrolled in this study. Four independent prediction models were separately developed to differentiate SCC from ADC using clinical factors-tumor markers, PET radiomics, CT radiomics, and their combination. The DeLong test and DCA showed that the Combined Model, consisting of 2 clinical factors, 2 tumor markers, 7 PET radiomics, and 3 CT radiomic parameters, held the highest predictive efficiency and clinical utility in predicting the NSCLC subtypes compared with the use of these parameters alone in both the training and validation sets (AUCs (95% CIs) = 0.932 (0.900–0.964), 0.901 (0.840–0.957), respectively) (p < 0.05). A quantitative nomogram was subsequently constructed using the independently risk factors from the Combined Model. The calibration curves indicated a good consistency between the actual observations and nomogram predictions. Conclusion This study presents an integrated clinico-biologico-radiological nomogram that can be accurately and noninvasively used for the individualized differentiation SCC from ADC in NSCLC, thereby assisting in clinical decision making for precision treatment.

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Section: Introductionmentioning

confidence: 99%

Machine learning based on clinico-biological features integrated 18F-FDG PET/CT radiomics for distinguishing squamous cell carcinoma from adenocarcinoma of lung

Ren

Zhang

et al. 2020

Eur J Nucl Med Mol Imaging

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“…These results indicate the effectiveness and significance of surgical intervention in combination with chemotherapy [3,19]. A study revealed that patients with PPA also benefited from postoperative radiotherapy (PORT) with an increase in OS (HR: 0.350, 95% CI: 0.126 to 0.972, P = 0.033) [20].…”

Section: Discussionmentioning

confidence: 95%

The characteristics and nomogram for primary lung papillary adenocarcinoma

et al. 2020

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AbstractBackgroundPrimary pulmonary papillary adenocarcinoma (PA) is a specific and rare subtype of invasive pulmonary adenocarcinoma (ADC). The knowledge concerning the clinicopathologic features and prognosis of patients with primary pulmonary PA has not been clarified because of its rarity.MethodsThe clinical data of a total of 3391 patients with primary pulmonary PA were retrospectively analyzed to confirm their clinical characteristics and factors influencing prognosis and were in comparison with 3236 patients with non- PA pulmonary adenocarcinoma. All patients were histologically diagnosed between 1988 and 2015 in The Surveillance Epidemiology and End Results (SEER) database. A nomogram with satisfactory predictive performance was established to visually predict long-term survival of these patients.Results and conclusionCollectively, primary pulmonary PA is a rare pathological cancer and its prognosis is analogous to that of non-PA pulmonary adenocarcinoma. Older age, larger lesions, distant metastases, lymph node invasion, and poor pathological differentiation are correlative with unacceptable prognosis. Surgical intervention is conducive to reaping favorable prognosis. Unfortunately, radiotherapy or chemotherapy results of no significant effects on patient survival. In our study, a nomogram with prognostic function is formulated to confer individual prediction of overall survival (OS).

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“…In a subset analysis of patients classified by the number of positive lymph nodes (n < 3 and n > 3), they found that the use of PORT significantly improves survival in patients with >3 lymph nodes, while no benefit in survival was registered in patients with <3 positive lymph nodes. Another recent study by Yuan et al [78] compared stage IIIA(N2) patients treated with or without PORT, demonstrating that PORT could improve OS in single N2 station involved patients (65.7% vs 54.1%, p = 0.04), but not the multiple ones (35.3% vs 32.7%, p = 0.93). It has been well known that patients with pathologic single station N2 diseases are those who could get more benefit in survival from upfront surgery followed by adjuvant therapy [137].…”

Section: Adjuvant Therapy: Should Radiation Be Included?mentioning

confidence: 99%

Resectable IIIA-N2 Non-Small-Cell Lung Cancer (NSCLC): In Search for the Proper Treatment

Brascia

Iaco

Schiavone

et al. 2020

Cancers

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Locally advanced non-small cell lung cancer accounts for one third of non-small cell lung cancer (NSCLC) at the time of initial diagnosis and presents with a wide range of clinical and pathological heterogeneity. To date, the combined multimodality approach involving both local and systemic control is the gold standard for these patients, since occult distant micrometastatic disease should always be suspected. With the rapid increase in treatment options, the need for an interdisciplinary discussion involving oncologists, surgeons, radiation oncologists and radiologists has become essential. Surgery should be recommended to patients with non-bulky, discrete, or single-level N2 involvement and be included in the multimodality treatment. Resectable stage IIIA patients have been the subject of a number of clinical trials and retrospective analysis, discussing the efficiency and survival benefits on patients treated with the available therapeutic approaches. However, most of them have some limitations due to their retrospective nature, lack of exact pretreatment staging, and the involvement of heterogeneous populations leading to the awareness that each patient should undergo a tailored therapy in light of the nature of his tumor, its extension and his performance status.

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The lymph node status and histologic subtypes influenced the effect of postoperative radiotherapy on patients with N2 positive IIIA non–small cell lung cancer

Cited by 31 publications

References 28 publications

Machine learning based on clinico-biological features integrated 18F-FDG PET/CT radiomics for distinguishing squamous cell carcinoma from adenocarcinoma of lung

Machine learning based on clinico-biological features integrated 18F-FDG PET/CT radiomics for distinguishing squamous cell carcinoma from adenocarcinoma of lung

The characteristics and nomogram for primary lung papillary adenocarcinoma

Resectable IIIA-N2 Non-Small-Cell Lung Cancer (NSCLC): In Search for the Proper Treatment

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