In the present study, we demonstrated the construction of and validation of a radiomics-clinical nomogram based on preoperative MRI and clinical characteristics for predicting low- and high-grade NSCLC. Using the training and validation cohorts, our nomogram showed excellent discriminative power and clinical utility. Hence, our proposed nomogram may be an effective and non-invasive tool for preoperative histological grade assessment in patients with lung cancer.
Currently, there is no histological grading system with clearly defined criteria and clinical significance widely accepted for patients with lung cancer. The World Health Organization (WHO) (4th edition) classification method for lung cancer grades adenocarcinoma as 1 (good differentiation, predominantly with wall growth), 2 (moderate differentiation, with acinar or nipple), or 3 (poor differentiation, mainly solid or micropapillary) 28. These structural patterns are based on the most important tissue classifications for adenocarcinoma. Previously, Weichert et al. developed a grading system for lung squamous cell carcinoma, which summarized the scores of two independent prognostic markers, including tumor sprouting and tumor nest size 29. However, both definitions depend on the subjective judgment of the pathologist, and the hierarchy is easily confused. To study the unity of the standard, traditional histological grading methods were also used in this study, including the similarity of structural patterns to the normal lung tissue of its origin, as well as the tumor cell atypia and degree of differentiation. The traditional histology grading system has been established for many years and is clinically relevant and reproducible in breast cancer 30, prostate cancer 31, endometrial cancer 32, soft tissue sarcoma 33, and renal cancer 34. Therefore, we assume that the traditional histological grading system is applicable in NSCLC.
From the hypothesis described above, the feasibility and performance of histological grading identification in NSCLC patients remain unclear for multi-sequence MRI ensembles. Therefore, we aimed to (1) explore and study imaging grouping strategies based on multi-sequence MRI, such as T2WI, DWI, and ADC, to preoperatively identify high- and low-grade NSCLC, and (2) verify whether the combination of radiomics features and clinical features may improve the discriminating ability of this study.
Our findings demonstrate that the radiomics features extracted from the T2WI, DWI, and ADC were strongly correlated with the histological grade of NSCLC. The double MRI sequences used in the current study are standard and used in hospitals worldwide. The radiomics-based classification of NSCLC allowed for the non-invasive prediction and stratification of high- and low-grade NSCLC. In our experiments, Radscore was the most effective method of distinguishing the histological grade of NSCLC. The Radscore combined five optimal characteristics as one biological marker. Recent studies have combined multiple marker analyses into a single individual marker 35, 36. For example, a recent study screened 21 independent genes from patients with breast cancer. The characteristics of these 21 genes together were identified and validated as the optimal features that can prevent certain breast cancer patient groups from requiring chemotherapy 36.
The highest sum of the absolute value coefficients was obtained from the DWI features; thus, higher DWI signals showed higher tumor levels, which exhibited faster cell proliferation, higher cell densities, larger cell nuclei, higher intracellular macromolecular protein content, higher cytoplasmic ratios, smaller extracellular space 13, 37, and more limited intracellular and extracellular diffusion. As of now, DWI is the only imaging method that can detect limited cell diffusion. DWI was first used in the central nervous system, but applying this technology to the lungs is challenging due to its drawbacks, including its low signal-to-noise ratio due to the inherent low density of protons located in the lungs, image artifacts caused by the movement of the heart and the breath, and high-gradient fields that influence the magnetic susceptibility of the inflated lung tissue, which increases the likelihood of artifacts 38, 39. Our experimental team used the ISHIM-EPI sequence to reduce magnetic-sensitive and respiratory motion artifacts. Under free-breathing conditions, the signal-to-noise ratio and image quality were improved for the DWI.
Only a few studies have explored the preoperative differentiation of NSCLC histological grades based on dual-energy CT and ADC in recent years. These tests have limitations, including CT ionizing radiation problems and overlap of ADC values 13; however, traditional medical imaging methods have always been qualitative. The rise of imaging radiomics, the rapid development of image acquisition methods, standardization strategies, and image analysis tools have enabled researchers to objectively, accurately, and quantitatively describing tumor imaging as a non-invasive biological marker for predicting the prognosis of patients. No previous studies achieved a quantitative risk stratification of the histological level of NSCLC patients based on a nomogram model. Previously, Chen et al. performed image analytics based on enhanced CT to extract 591 radiomics features 40. The minimum redundancy, maximum correlation algorithm, and logistic regression model were used to reduce dimensionality and select the best features. Finally, a model was established from nine features. A set of radiomics features was validated in an independent validation group. The feature set was used to distinguish between high and low-grade lung cancer in the training group, of which the AUC was 0.763, and the accuracy rate was 68.7%. For comparison, the AUC was 0.782, and the accuracy rate was 71.2% in the verification group. This result was similar to our Radscore results from multisequence MRI radiomics. We also applied the nomogram model proposed by the clinical factor. The results were slightly better than those of the enhanced CT-based imaging radiomics method in terms of identification tasks.
Clinical features, such as age, sex, smoking, tumor location, tumor typing, longest tumor diameter, vertical tumor diameter, CEA, Ki67, and histological subtype, are commonly used to diagnose patients with lung cancer. If the combination of the factors and the feature-generated Radscore can improve recognition performance requires further study. Surprisingly, the univariate analysis revealed that none of these factors correlated with the grading of lung cancer. Univariate correlation is not reported to show sufficient predictive strength 41, which is a common strategy for excluding the variable from model development. However, among these predictors, nuances in the dataset may result in the exclusion of important predictors. These results may also have been due to confusion with other predictors. We also believed that all factors should be related; thus, the multifactor analysis showed that sex, smoking, and Radscore together were significant predictors of recognition tasks. Then, we generated the nomogram model from these predictive factors and obtained a satisfactory recognition rate with better recognition performance than that of the imaging ensemble model. Therefore, the combination of radiomics features and clinical factors can enhance its recognition ability. In addition, we also verified that the nomogram model showed good prediction accuracy and clinical application value.
This study had some limitations. First, because the study was retrospective, and the patient population was relatively limited, inherent bias may exist. The addition of more patients in a multi-center trial would help validate our findings. In addition, owing to incomplete archival database data, this study excluded other potential clinical features, including genetic mutations and possible molecular markers, which require further analysis. Third, although we used multivariate logistic regression models, ROC curves combined with nomograms as well as calibration curves, which are commonly accepted in the field of medical imaging analysis, along with other comparative studies are needed.