Prognostic effects of preoperative obstructive pneumonitis or atelectasis and comparison with tumor size in non-small cell lung cancer

Pang, Zhaofei; Ding, Nan; Wang, Dong; Ni, Yang; Zhang, Tiehong; Qu, Xiao; Du, Jiajun; Liu, Qi

doi:10.21037/jtd.2017.02.88

Cited by 11 publications

(11 citation statements)

References 26 publications

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“…We also demonstrated that the presence of atelectasis could significantly affect OS (HR 2.047, 95 % CI 1.029− 4.071, P = 0.041), which is the same conclusion as the Pang study [30], in which 1177 preoperative patients were divided into subgroups by atelectasis and tumour size; in patients within the same tumour size subgroup, the OS was significantly shorter when atelectasis was absent (P = 0.033). However, for PFS, the results were not significant, suggesting the need for a larger sample size.…”

Section: Discussionsupporting

confidence: 84%

COV is a readily available quantitative indicator of metabolic heterogeneity for predicting survival of patients with early and locally advanced NSCLC manifesting as central lung cancer

2020

European Journal of Radiology

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The aim of our study was to investigate the value of a simple metabolic heterogeneity parameter, COV (coefficient of variation), by 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) in the prognosis prediction of central lung cancer in early and locally advanced non-small-cell lung cancer (NSCLC). Methods: Seventy-three patients with NSCLC manifesting as central lung cancer were included retrospectively, and we used the COV to evaluate metabolic heterogeneity. Univariate and multivariate analyses were used to evaluate the predictive value in terms of overall survival (OS) and progression-free survival (PFS). Result: For all 73 patients with pathologically confirmed NSCLC, 69.9 % had SCC, and 30.1 % had ADC or other types of NSCLC. The COV was a statistically significant factor in the univariate analysis for the OS rate. The optimal cut-off value was 23.1366, with sensitivity = 0.737 and specificity = 0.771. The COV values were dichotomized by this value and included with atelectasis in the Cox multivariate analysis. Both COV and atelectasis were independent risk factors for OS as follows: for COV (HR, 3.162, P = 0.0002), the 2-year OS rate was 62.5 % and 26.9 % in the low and high COV groups, respectively. For atelectasis (HR 2.047, P = 0.041), the 2year OS rate was 30.6 % and 65.2 % in the groups with and without atelectasis, respectively (P = 0.017). For PFS, only COV (HR, 2.636, P = 0.001) was a significant predictor. The 2-year PFS rate was 29.7 % in the low COV group and 8% in the high COV group. Conclusion:The pre-treatment metabolic heterogeneity parameter COV is a simple and easy way to predict the OS and PFS of patients with NSCLC manifesting as central lung cancer. Therefore, COV plays an important role in prognostic risk classification in NSCLC. The presence of atelectasis could also be a risk factor for poor prognosis of OS.

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Section: Discussionsupporting

confidence: 84%

COV is a readily available quantitative indicator of metabolic heterogeneity for predicting survival of patients with early and locally advanced NSCLC manifesting as central lung cancer

2020

European Journal of Radiology

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“…Since immunotherapy is a systematic treatment modality, changes indicating response are not limited to one location but can occur all over the body. This is particularly relevant in patients treated with anti PD-1 blockade where lymphadenopathy ( 14 , 15 ), parenchymal inflammations, edema ( 16 , 17 ), and compression atelectasis ( 18 ), can be observed. Ideally, during image response evaluation these conditions, together with tumor growth, should be monitored and quantified as they might hold valuable prognostic information.…”

Section: Introductionmentioning

confidence: 99%

Prognostic Value of Deep Learning-Mediated Treatment Monitoring in Lung Cancer Patients Receiving Immunotherapy

et al. 2021

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BackgroundCheckpoint inhibitors provided sustained clinical benefit to metastatic lung cancer patients. Nonetheless, prognostic markers in metastatic settings are still under research. Imaging offers distinctive advantages, providing whole-body information non-invasively, while routinely available in most clinics. We hypothesized that more prognostic information can be extracted by employing artificial intelligence (AI) for treatment monitoring, superior to 2D tumor growth criteria.MethodsA cohort of 152 stage-IV non-small-cell lung cancer patients (NSCLC) (73 discovery, 79 test, 903CTs), who received nivolumab were retrospectively collected. We trained a neural network to identify morphological changes on chest CT acquired during patients’ follow-ups. A classifier was employed to link imaging features learned by the network with overall survival.ResultsOur results showed significant performance in the independent test set to predict 1-year overall survival from the date of image acquisition, with an average area under the curve (AUC) of 0.69 (p < 0.01), up to AUC 0.75 (p < 0.01) in the first 3 to 5 months of treatment, and 0.67 AUC (p = 0.01) for durable clinical benefit (6 months progression-free survival). We found the AI-derived survival score to be independent of clinical, radiological, PDL1, and histopathological factors. Visual analysis of AI-generated prognostic heatmaps revealed relative prognostic importance of morphological nodal changes in the mediastinum, supraclavicular, and hilar regions, lung and bone metastases, as well as pleural effusions, atelectasis, and consolidations.ConclusionsOur results demonstrate that deep learning can quantify tumor- and non–tumor-related morphological changes important for prognostication on serial imaging. Further investigation should focus on the implementation of this technique beyond thoracic imaging.

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“…Tumor size, given as the maximum diameter of the tumor, was one of independent prognostic factors of many solid tumors [1][2][3][4][5] . As for gastrointestinal carcinoma, several studies [6][7][8][9] a rmed that tumor size positively correlated with important prognostic factors and negatively impacted survival.…”

Section: Discussionmentioning

confidence: 99%

“…Tumor size has been veri ed to be associated with overall survival (OS) in many types of malignancy, and it is regarded as "T" stage of many solid tumors including breast, lung and liver cancers in the tumor-nodemetastasis (TNM) staging system of the Union for International Cancer Control (UICC) [1][2][3][4][5] . Despite the value of tumor size as a prognostic indicator in those solid tumors, the prognostic signi cance of tumor size in gastrointestinal tumors has not been widely realized.…”

Section: Introductionmentioning

confidence: 99%

Tumor size as a prognostic factor improves the accuracy of the prognostic prediction of T4 stage colon cancer: a propensity score analysis

Liang¹,

Li²,

He³

et al. 2021

Preprint

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The aim of this study was to evaluate the potential impact of tumor size on the long-term outcome of CC patients after curative surgery. A total of 782 curatively resected T4 stage CC patients without distant metastasis were enrolled. Patients were categorized into 2 groups according to the best threshold of tumor size: larger group (LG) and smaller group (SG). Propensity score matching was used to adjust for the differences in baseline characteristics. The ideal cutoff points for tumor size was 5 cm. In the multivariate analysis for the whole study series, tumor size was an independent prognostic factors. Patients in the LG had a significant lower 5-year OS rate, but higher distant metastatic rate than those in the SG (37.1% versus 25.2%, P < 0.001). After matching, patients in the LG still demonstrated a significant lower 5-year OS rate than those in the SG (63.5% versus 74.2%, P < 0.001). Patients in the LG benefited more from postoperative adjuvant chemotherapy than patients in the SG. The modified stage including tumor size was found to be more appropriate for predicting the OS of T4 stage CC than TNM stage. In conclusion, tumor size was an independent prognostic factor and could correlate with higher distant metastasis rate and better response of adjuvant chemotherapy. We maintain that tumor size should be incorporated into the staging system to enhance the accuracy of the prognostic prediction of T4 stage CC patients.

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Prognostic effects of preoperative obstructive pneumonitis or atelectasis and comparison with tumor size in non-small cell lung cancer

Cited by 11 publications

References 26 publications

COV is a readily available quantitative indicator of metabolic heterogeneity for predicting survival of patients with early and locally advanced NSCLC manifesting as central lung cancer

COV is a readily available quantitative indicator of metabolic heterogeneity for predicting survival of patients with early and locally advanced NSCLC manifesting as central lung cancer

Prognostic Value of Deep Learning-Mediated Treatment Monitoring in Lung Cancer Patients Receiving Immunotherapy

Tumor size as a prognostic factor improves the accuracy of the prognostic prediction of T4 stage colon cancer: a propensity score analysis

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