Integrative nomogram of CT imaging, clinical, and hematological features for survival prediction of patients with locally advanced non-small cell lung cancer

Wang, Linlin; Dong, Taotao; Xin, Bowen; Xu, Chong-Rui; Guo, Meiying; Zhang, Huaqi; Feng, Dagan; Wang, Xiuying; J, Yu

doi:10.1007/s00330-018-5949-2

Cited by 57 publications

(51 citation statements)

References 42 publications

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“…Antoine Schernberg et al also found that combination of neutrophil counts and SUV peak value in PET images of primary tumor could effectively predict the survival of localized advanced cervical cancer patients (14). Besides, in our previous study, we found that the incorporation of hematologic and clinical variables into the radiomic prognosis model could significantly improve the C index from 0.69 to 0.79 in NSCLC patients (15). Thus, combination of different types of biomarkers could be one of the most promising method for the further improvement of model performance in future.…”

Section: Discussionmentioning

confidence: 54%

“…One thousand forty-five radiomic features were extracted for each patient based on the open source Pyradiomics platform (www.radiomics.io), in which those features could be divided into five categories including the intensity, shape, texture, wavelet, and log transformation. The detailed description of these radiomics features could also be found in our previous work (15).…”

Section: Image Segmentation and Radiomic Feature Extractionmentioning

confidence: 90%

“…Logistic regression analyses were then used for selecting the predictive radiomic features. Twenty-eight features had p < 0.05, among which five features were selected for further analyses due to their higher C index values (15). Radiomic signature was then established using the multivariate logistic regression model.…”

Section: Important Radiomic Features Selectionmentioning

confidence: 99%

“…The progress in pattern recognition and increased sample data in medical radiology has promoted the development of radiomics remarkably. Previous studies have shown that these objective and quantitative radiomic features could be used as meaningful biomarkers for the prediction of treatment response and prognosis in various types of cancer including the cervical cancer (11)(12)(13)(14)(15). However, the main limitation of popular radiomic studies is that the performance of these predictive model almost always could not reach the clinical useful level since they are often based on the conventional machine learning method and limited numbers of variables (16).…”

Section: Introductionmentioning

confidence: 99%

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Development and Validation of a Deep Learning Radiomics Model Predicting Lymph Node Status in Operable Cervical Cancer

et al. 2020

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Aim: To develop and validate a deep learning radiomics model, which could predict the lymph node metastases preoperatively in cervical cancer patients. Conclusion: This study used deep learning method to provide a comprehensive predictive model using preoperative CT images, tumor histology, and grade in cervical cancer patients. This model showed an acceptable accuracy in the prediction of lymph node status in cervical cancer. Our model may help identifying those patients who could benefit a lot from radiation therapy rather than primary hysterectomy surgery if this model could resist strict testing of future randomized controlled trials (RCTs).

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Section: Discussionmentioning

confidence: 54%

Section: Image Segmentation and Radiomic Feature Extractionmentioning

confidence: 90%

Section: Important Radiomic Features Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development and Validation of a Deep Learning Radiomics Model Predicting Lymph Node Status in Operable Cervical Cancer

et al. 2020

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“…Consequently, numerous additional prognostic or predictive factors (5)(6)(7), among them image-derived parameters (8)(9)(10)(11)(12), have been investigated aiming at more differentiated outcome prediction and more differentiated management decisions. Among parameters from positron emission tomography/computed tomography with [ 18 F] uorodeoxyglucose (FDG-PET/CT), asphericity (ASP) is a parameter that re ects shape irregularity of the primary tumor's metabolic tumor volume (MTV), combining metric and metabolic features of the primary tumor.…”

mentioning

confidence: 99%

Asphericity of Tumor FDG Uptake in Non-small Cell Lung Cancer: Reproducibility and Implications for Harmonization in Multicenter Studies

Rogasch

Furth

Bluemel

et al. 2020

Preprint

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BackgroundAsphericity (ASP) of the primary tumor’s metabolic tumor volume (MTV) in FDG-PET/CT is independently predictive for survival in patients with non-small cell lung cancer (NSCLC). However, comparability between PET systems may be limited. Therefore, reproducibility of ASP was evaluated at varying image reconstruction and acquisition times to assess feasibility of ASP assessment in multicenter studies.MethodsRetrospective study of 50 patients with NSCLC (female, 20; median age, 69 years) undergoing pretherapeutic FDG-PET/CT (median, 3.7 MBq/kg; 180 s/bed position). Reconstruction used OSEM with TOF4/16 (iterations, 4; subsets, 16; in-plane filter, 2.0, 6.4 or 9.5 mm), TOF4/8 (4 it; 8 ss; filter, 2.0/6.0/9.5 mm), PSF+TOF2/17 (2 it; 17 ss; filter, 2.0/7.0/10.0 mm) or Bayesian penalized likelihood (Q.Clear; beta, 600/1750/4000). Resulting reconstructed spatial resolution (FWHM) was determined from hot sphere inserts of a NEMA IEC phantom. Data with approx. 5 mm FWHM were retrospectively smoothed to achieve 7 mm FWHM. List mode data were rebinned for acquisition times of 120/90/60s. Threshold-based delineation of primary tumor MTV was followed by evaluation of relative ASP/SUVmax/MTV differences between datasets and resulting proportions of discordantly classified cases.ResultsReconstructed resolution for narrow/medium/wide in-plane filter (or low/medium/high beta) was approx. 5/7/9 mm FWHM. Comparing different pairs of reconstructed resolution between TOF4/8, PSF+TOF2/17, Q.Clear and the reference algorithm TOF4/16, ASP differences were lowest at FWHM of 7 vs. 7 mm. Proportions of discordant cases (ASP >19.5% vs. ≤19.5%) were also lowest at 7 mm (TOF4/8, 2%; PSF+TOF2/17, 4%; Q.Clear, 10%). Smoothing of 5 mm data to 7 mm FWHM significantly reduced discordant cases (TOF4/8, 38% reduced to 2%; PSF+TOF2/17, 12% to 4%; Q.Clear, 10% to 6%) resulting in proportions comparable to original 7 mm data. Shorter acquisition time only increased proportions of discordant cases at <90s. ConclusionsASP differences were mainly determined by reconstructed spatial resolution, and multicenter studies should aim at comparable FWHM (e.g., 7 mm; determined by in-plane filter width). This reduces discordant cases (high vs. low ASP) to an acceptable proportion for TOF and PSF+TOF of <5% (Q.Clear: 10%). Data with better resolution (i.e. lower FWHM) could be retrospectively smoothed to the desired FWHM resulting in a comparable number of discordant cases.

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Comparison of nomogram and machine‐learning methods for predicting the survival of non‐small cell lung cancer patients

Lei

et al. 2022

Cancer Innovation

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Background Most patients with advanced non‐small cell lung cancer (NSCLC) have a poor prognosis. Predicting overall survival using clinical data would benefit cancer patients by allowing providers to design an optimum treatment plan. We compared the performance of nomograms with machine‐learning models at predicting the overall survival of NSCLC patients. This comparison benefits the development and selection of models during the clinical decision‐making process for NSCLC patients. Methods Multiple machine‐learning models were used in a retrospective cohort of 6586 patients. First, we modeled and validated a nomogram to predict the overall survival of NSCLC patients. Subsequently, five machine‐learning models (logistic regression, random forest, XGBoost, decision tree, and light gradient boosting machine) were used to predict survival status. Next, we evaluated the performance of the models. Finally, the machine‐learning model with the highest accuracy was chosen for comparison with the nomogram at predicting survival status by observing a novel performance measure: time‐dependent prediction accuracy. Results Among the five machine‐learning models, the accuracy of random forest model outperformed the others. Compared with the nomogram for time‐dependent prediction accuracy with a follow‐up time ranging from 12 to 60 months, the prediction accuracies of both the nomogram and machine‐learning models changed as time varied. The nomogram reached a maximum prediction accuracy of 0.85 in the 60th month, and the random forest algorithm reached a maximum prediction accuracy of 0.74 in the 13th month. Conclusions Overall, the nomogram provided more reliable prognostic assessments of NSCLC patients than machine‐learning models over our observation period. Although machine‐learning methods have been widely adopted for predicting clinical prognoses in recent studies, the conventional nomogram was competitive. In real clinical applications, a comprehensive model that combines these two methods may demonstrate superior capabilities.

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Integrative nomogram of CT imaging, clinical, and hematological features for survival prediction of patients with locally advanced non-small cell lung cancer

Cited by 57 publications

References 42 publications

Development and Validation of a Deep Learning Radiomics Model Predicting Lymph Node Status in Operable Cervical Cancer

Development and Validation of a Deep Learning Radiomics Model Predicting Lymph Node Status in Operable Cervical Cancer

Asphericity of Tumor FDG Uptake in Non-small Cell Lung Cancer: Reproducibility and Implications for Harmonization in Multicenter Studies

Comparison of nomogram and machine‐learning methods for predicting the survival of non‐small cell lung cancer patients

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