Current Readings: Radiologic Interpretation of the Part-Solid Nodule: Clinical Relevance and Novel Technologies

Establishment of a malignancy and benignancy prediction model of sub-centimeter pulmonary ground-glass nodules based on the inflammation-cancer transformation theory

Background

In recent years, Chinese clinicians are frequently encountered by patients with multiple lung nodules and these intensity ground-glass nodules (GGNs) are usually small in size and some of them have no spicule sign. In addition, early lung cancer is diagnosed in large numbers of non-heavy smokers and individuals with no caner history. Obviously, the Mayo model is not applicable to these patients. The aim of the present study is to develop a new and more applicable model that can predict malignancy or benignancy of pulmonary GGNs based on the inflammation-cancer transformation theory.

Materials and methods

Included in this study were patients who underwent surgical resection or lung puncture biopsy of GGNs in Shanghai 10th People’s Hospital between January 1, 2018 and May 31, 2021 with the inclusion criterion of the maximum diameter of GGN < 1.0 cm. All the included patients had their pulmonary GGNs diagnosed by postoperative pathology. The patient data were analyzed to establish a prediction model and the predictive value of the model was verified.

Results

Altogether 100 GGN patients who met the inclusion criteria were included for analysis. Based on the results of logistic stepwise regression analysis, a mathematical predication equation was established to calculate the malignancy probability as follows: Malignancy probability rate (p) = ex/(1 + ex); p > 0.5 was considered as malignant and p ≤ 0.5 as benign, where x = 0.9650 + [0.1791 × T helper (Th) cell] + [0.2921 × mixed GGN (mGGN)] + (0.4909 × vascular convergence sign) + (0.1058 × chronic inflammation). According to this prediction model, the positive prediction rate was 73.3% and the negative prediction rate was 100% versus the positive prediction rate of 0% for the Mayo model.

Conclusion

By focusing on four major factors (chronic inflammation history, human Th cell, imaging vascular convergence sign and mGGNs), the present prediction model greatly improves the accuracy of malignancy or benignancy prediction of sub-centimeter pulmonary GGNs. This is a breakthrough innovation in this field.

Distinguishing multiple lung primaries from intra-pulmonary metastases and treatment implications

INTRODUCTION

The distinction between multiple primary lung cancers and intra-pulmonary metastases has been extensively investigated because of its important clinical and therapeutic implications.

AREAS COVERED

Rapidly improving imaging technology and genomic analysis has led to a finer discrimination between multiple primary lung tumors and pulmonary metastases. However, over the past few decades, standardized criteria for the identification of multiple lung tumors have been lacking. Therefore, in 2017 a multidisciplinary international committee composed of the Union for International Cancer Control (UICC), American Joint Committee on Cancer (AJCC) and International Association for the Study of Lung Cancer (IASLC) addressed this problem when drawing up the 8th edition of TMN stage classification, that now represents a specific consensus on this topic. The most advanced diagnostic strategies associated with screening allow for the detection of early stage synchronous lung cancers.

EXPERT OPINION

Although diagnostic confirmation relies on pathologic and clinical examination, new molecular analyses help in the discrimination between primary and secondary tumors. The treatment of multiple primary lung tumors remains, whenever possible, a local treatment based on surgical resection, providing the absence of distant or local (lymph node) metastases.