The Growth Rate of Subsolid Lung Adenocarcinoma Nodules at Chest CT

Growth dynamics of lung nodules: implications for classification in lung cancer screening

BACKGROUND

Lung nodules observed in cancer screening are believed to grow exponentially, and their associated volume doubling time (VDT) has been proposed for nodule classification. This retrospective study aimed to elucidate the growth dynamics of lung nodules and determine the best classification as either benign or malignant.

METHODS

Data were analyzed from 180 participants (73.7% male) enrolled in the I-ELCAP screening program (140 primary lung cancer and 40 benign) with three or more annual CT examinations before resection. Attenuation, volume, mass and growth patterns (decelerated, linear, subexponential, exponential and accelerated) were assessed and compared as classification methods.

RESULTS

Most lung cancers (83/140) and few benign nodules (11/40) exhibited an accelerated, faster than exponential, growth pattern. Half (50%) of the benign nodules versus 26.4% of the malignant ones displayed decelerated growth. Differences in growth patterns allowed nodule malignancy to be classified, the most effective individual variable being the increase in volume between two-year-interval scans (ROC-AUC = 0.871). The same metric on the first two follow-ups yielded an AUC value of 0.769. Further classification into solid, part-solid or non-solid, improved results (ROC-AUC of 0.813 in the first year and 0.897 in the second year).

CONCLUSIONS

In our dataset, most lung cancers exhibited accelerated growth in contrast to their benign counterparts. A measure of volumetric growth allowed discrimination between benign and malignant nodules. Its classification power increased when adding information on nodule compactness. The combination of these two meaningful and easily obtained variables could be used to assess malignancy of lung cancer nodules.

The evolution of the treatment of non-small cell lung cancer: A shift in surgical paradigm to a more individualized approach

Surgical treatment is an integral part of the comprehensive therapeutic methods for lung cancer, especially for early-stage non-small cell lung cancer (NSCLC). With a deeper understanding of the disease, we found that lung cancer is more commonly detected in young females. For regions of Asia, more lung cancer has been detected in early-stage GGO-dominant non-smokers. Therefore, surgical strategies have also been reformed commensurate with the shift of the disease spectrum. However, the pursuit of lung-sparing individualized approaches has raised worldwide attention. Suitable surgical treatment within the curative time window is recommended to maximize the long-term benefit. This article summarizes the shift in surgical treatment for small NSCLCs and hopes to enlighten further innovations to fill in the gaps between the unmet needs and a more individualized approach.

Case report: Pulmonary sarcomatoid carcinoma demonstrating rapid growth on follow-up CT

Background

The tumor growth rate and tumor volume doubling time are crucial parameters in diagnosing and managing lung lesions. Pulmonary sarcomatoid carcinoma (PSC) is a unique and highly malignant subtype of lung cancer, with limited documentation on its growth feature. This article aims to address the gap in knowledge regarding a PSC's growth patterns by describing the characteristics of a confirmed case using computed tomography, thereby enhancing the understanding of this rare disease.

Case presentation

A 79-year-old man was transferred to our center presenting with a mild cough, blood-tinged sputum, and a malignant nodule in the left upper lobe. Chest CT revealed a solid nodule in the left upper lobe. A follow-up CT ten days later showed a significant increase in the size of the nodule, accompanied by ground-glass opacity in the surrounding lung. The rapid preoperative growth of the nodule suggested a non-neoplastic lesion, and intraoperative frozen pathology also considered the possibility of tuberculosis. Subsequently, a left upper apical-posterior segment (S1 + 2) resection was performed. Postoperative tumor pathology confirmed the diagnosis of pulmonary sarcomatoid carcinoma with extensive giant cell carcinoma and necrosis. Immunohistochemistry indicated approximately 60% PD-L1 positive and genetic testing revealed a MET mutation. The patient was discharged with oral crizotinib targeted therapy, and his condition remained stable postoperatively. The patient is currently undergoing regular follow-up at our hospital, with no evidence of distant metastasis or recurrence.

Conclusion

Pulmonary sarcomatoid carcinoma can exhibit rapid tumor growth on imaging, and PSC should be considered in the differential diagnosis for lesions that present with a fast growth rate. Timely and appropriate treatment for PSC may lead to a good prognosis.

Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis

BACKGROUND

The exceptional capabilities of artificial intelligence (AI) in extracting image information and processing complex models have led to its recognition across various medical fields. With the continuous evolution of AI technologies based on deep learning, particularly the advent of convolutional neural networks (CNNs), AI presents an expanded horizon of applications in lung cancer screening, including lung segmentation, nodule detection, false-positive reduction, nodule classification, and prognosis.

METHODOLOGY

This review initially analyzes the current status of AI technologies. It then explores the applications of AI in lung cancer screening, including lung segmentation, nodule detection, and classification, and assesses the potential of AI in enhancing the sensitivity of nodule detection and reducing false-positive rates. Finally, it addresses the challenges and future directions of AI in lung cancer screening.

RESULTS

AI holds substantial prospects in lung cancer screening. It demonstrates significant potential in improving nodule detection sensitivity, reducing false-positive rates, and classifying nodules, while also showing value in predicting nodule growth and pathological/genetic typing.

CONCLUSIONS

AI offers a promising supportive approach to lung cancer screening, presenting considerable potential in enhancing nodule detection sensitivity, reducing false-positive rates, and classifying nodules. However, the universality and interpretability of AI results need further enhancement. Future research should focus on the large-scale validation of new deep learning-based algorithms and multi-center studies to improve the efficacy of AI in lung cancer screening.

Prediction of Interval Growth of Lung Adenocarcinomas Manifesting as Persistent Subsolid Nodules ≤3 cm Based on Radiomic Features

RATIONALES AND OBJECTIVES

To investigate the prognostic value of the radiomic-based prediction model in predicting the interval growth rate of persistent subsolid nodules (SSNs) with an initial size of ≤ 3 cm manifesting as lung adenocarcinomas.

MATERIALS AND METHODS

A total of 133 patients (mean age, 59.02 years; male, 37.6%) with 133 SSNs who underwent a series of CT examinations at our hospital between 2012 and 2022 were included in this study. Forty-one radiomic features were extracted from each volumetric region of interest. Radiomic features combined with conventional clinical and semantic parameters were then selected for radiomic-based model building. To investigate the model performance in terms of substantial SSN growth and stage shift growth, the model performance was compared by the area under the curve (AUC) obtained by receiver operating characteristic analysis.

RESULTS

The mean follow-up period was 3.62 years. For substantial SSN growth, a radiomic-based model (Model 2) based on clinical characteristics, CT semantic features, and radiomic features yielded an AUCs of 0.869 (95% CI: 0.799-0.922). In comparison with Model 1 (clinical characteristics and CT semantic features), Model 2 performed better than Model 1 for substantial SSN growth (AUC model 1:0.793 versus AUC model 2:0.869, p = 0.028). A radiomic-based nomogram combining sex, follow-up period, and three radiomic features was built for substantial SSN growth prediction. For the stage shift growth, a radiomic-based model (Model 4) based on clinical characteristics, CT semantic features, and radiomic features yielded an AUCs of 0.883 (95% CI: 0.815-0.933). Compared with Model 3 (clinical characteristics and CT semantic features), Model 4 performed better than the model 3 for stage shift growth (AUC model 1: 0.769 versus AUC model 2: 0.883, p = 0.006). A radiomic-based nomogram combining the initial nodule size, SSN classification, follow-up period, and three radiomic features was built to predict the stage shift growth.

CONCLUSION

Radiomic-based models have superior utility in estimating the prognostic interval growth of patients with early lung adenocarcinomas (≤ 3 cm) than conventional clinical-semantic models in terms of substantial interval growth and stage shift growth, potentially guiding clinical decision-making with follow-up strategies of SSNs in personalized precision medicine.

Breast Cancer Growth on Serial MRI: Volume Doubling Time Based on 3-Dimensional Tumor Volume Assessment

BACKGROUND

The volume doubling time (VDT) of breast cancer was most frequently calculated using the two-dimensional (2D) diameter, which is not reliable for irregular tumors. It was rarely investigated using three-dimensional (3D) imaging with tumor volume on serial magnetic resonance imaging (MRI).

PURPOSE

To investigate the VDT of breast cancer using 3D tumor volume assessment on serial breast MRIs.

STUDY TYPE

Retrospective.

SUBJECTS

Sixty women (age at diagnosis: 57 ± 10 years) with breast cancer, assessed by two or more breast MRI examinations. The median interval time was 791 days (range: 70-3654 days).

FIELD STRENGTH/SEQUENCE

3-T, fast spin-echo T2-weighted imaging (T2WI), single-shot echo-planar diffusion-weighted imaging (DWI), and gradient echo dynamic contrast-enhanced imaging.

ASSESSMENT

Three radiologists independently reviewed the morphological, DWI, and T2WI features of lesions. The whole tumor was segmented to measure the volume on contrast-enhanced images. The exponential growth model was fitted in the 11 patients with at least three MRI examinations. The VDT of breast cancer was calculated using the modified Schwartz equation.

STATISTICAL TESTS

Mann-Whitney U test, Kruskal-Wallis test, Chi-squared test, intraclass correlation coefficients, and Fleiss kappa coefficients. A P-value <0.05 was considered statistically significant. The exponential growth model was evaluated using the adjusted R2 and root mean square error (RMSE).

RESULTS

The median tumor diameter was 9.7 mm and 15.2 mm on the initial and final MRI, respectively. The median adjusted R2 and RMSE of the 11 exponential models were 0.97 and 15.8, respectively. The median VDT was 540 days (range: 68-2424 days). For invasive ductal carcinoma (N = 33), the median VDT of the non-luminal type was shorter than that of the luminal type (178 days vs. 478 days). On initial MRI, breast cancer manifesting as a focus or mass lesion showed a shorter VDT than that of a non-mass enhancement (NME) lesion (median VDT: 426 days vs. 665 days).

DATA CONCLUSION

A shorter VDT was observed in breast cancer manifesting as focus or mass as compared to an NME lesion.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Low-Dose CT Screening of Persistent Subsolid Lung Nodules: First-Order Features in Radiomics

BACKGROUND

 Nondisappearing subsolid nodules requiring follow-up are often detected during lung cancer screening, but changes in their invasiveness can be overlooked owing to slow growth. We aimed to develop a method for automatic identification of invasive tumors among subsolid nodules during multiple health checkups using radiomics technology based on low-dose computed tomography (LD-CT) and examine its effectiveness.

METHODS

 We examined patients who underwent LD-CT screening from 2014 to 2019 and had lung adenocarcinomas resected after 5-year follow-ups. They were categorized into the invasive or less-invasive group; the annual growth/change rate (Δ) of the nodule voxel histogram using three-dimensional CT (e.g., tumor volume, solid volume percentage, mean CT value, variance, kurtosis, skewness, and entropy) was assessed. A discriminant model was designed through multivariate regression analysis with internal validation to compare its efficacy with that of a volume doubling time of < 400 days.

RESULTS

 The study included 47 tumors (23 invasive, 24 less invasive), with no significant difference in the initial tumor volumes. Δskewness was identified as an independent predictor of invasiveness (adjusted odds ratio, 0.021; p = 0.043), and when combined with Δvariance, it yielded high accuracy in detecting invasive lesions (88% true-positive, 80% false-positive). The detection model indicated surgery 2 years earlier than the volume doubling time, maintaining accuracy (median 3 years vs.1 year before actual surgery, p = 0.011).

CONCLUSION

 LD-CT radiomics showed promising potential in ensuring timely detection and monitoring of subsolid nodules that warrant follow-up over time.

Observational Study of the Natural Growth History of Peripheral Small-Cell Lung Cancer on CT Imaging

BACKGROUND

This study aimed to investigate the natural growth history of peripheral small-cell lung cancer (SCLC) using CT imaging.

METHODS

A retrospective study was conducted on 27 patients with peripheral SCLC who underwent at least two CT scans. Two methods were used: Method 1 involved direct measurement of nodule dimensions using a calliper, while Method 2 involved tumour lesion segmentation and voxel volume calculation using the "py-radiomics" package in Python. Agreement between the two methods was assessed using the intraclass correlation coefficient (ICC). Volume doubling time (VDT) and growth rate (GR) were used as evaluation indices for SCLC growth, and growth distribution based on GR and volume measurements were depicted. We collected potential factors related to imaging VDT and performed a differential analysis. Patients were classified into slow-growing and fast-growing groups based on a VDT cut-off point of 60 days, and univariate analysis was used to identify factors influencing VDT.

RESULTS

Median VDT calculated by the two methods were 61 days and 71 days, respectively, with strong agreement. All patients had continuously growing tumours, and none had tumours that decreased in size or remained unchanged. Eight patients showed possible growth patterns, with six possibly exhibiting exponential growth and two possibly showing Gompertzian growth. Tumours deeper in the lung grew faster than those adjacent to the pleura.

CONCLUSIONS

Peripheral SCLC tumours grow rapidly and continuously without periods of nongrowth or regression. Tumours located deeper in the lung tend to grow faster, but further research is needed to confirm this finding.

Computed tomography radiomics in growth prediction of pulmonary ground-glass nodules

PURPOSE

Individualized follow-up of pulmonary ground-glass nodules (GGNs) remains challenging in clinical practice. Accurate prediction of the growth or long-term stability of persistent GGNs is essential to optimize the follow-up intervals.

METHODS

In this retrospective study, 253 patients with 1115 computed tomography (CT) images were recruited. In total, 1115 CT images were randomized into training (70%) and validation sets (30%). We developed models for the growth or long-term stable prediction of GGNs using radiomics and clinical features. We evaluated the prediction accuracy of the models using receiver operating characteristic (ROC) curve analysis, and the areas under the curve (AUCs) were established. The ROC curves of the models were compared using the DeLong method.

RESULTS

The growth and stable groups contained 535 and 580 GGNs, respectively. Traditional radiographic features have limited value in the prediction of growth or long-term stability of GGNs. The prediction nomogram model combining radiomics and clinical features (size, location, and age) yielded the best AUC in both the training and validation sets (AUC = 0.843 and 0.824, respectively). The radiomics model outperformed the clinical model in both sets (AUC: 0.836 vs 0.772 and 0.818 vs 0.735, respectively). The radiomics signature and nomogram model achieved similar AUCs (Delong test, training set: P = 0.09; validation set: P = 0.37).

CONCLUSIONS

We developed and validated a nomogram model combining radiomics signature, size, age, and location to predict the growth or long-term stability of GGNs. The model achieved good performance and may provide a basis for the improvement of follow-up management of GGNs.

The natural growth history of persistent pulmonary subsolid nodules: Radiology, genetics, and clinical management

The high detection rate of pulmonary subsolid nodules (SSN) is an increasingly crucial clinical issue due to the increased number of screening tests and the growing popularity of low-dose computed tomography (LDCT). The persistence of SSN strongly suggests the possibility of malignancy. Guidelines have been published over the past few years and guide the optimal management of SSNs, but many remain controversial and confusing for clinicians. Therefore, in-depth research on the natural growth history of persistent pulmonary SSN can help provide evidence-based medical recommendations for nodule management. In this review, we briefly describe the differential diagnosis, growth patterns and rates, genetic characteristics, and factors that influence the growth of persistent SSN. With the advancement of radiomics and artificial intelligence (AI) technology, individualized evaluation of SSN becomes possible. These technologies together with liquid biopsy, will promote the transformation of current diagnosis and follow-up strategies and provide significant progress in the precise management of subsolid nodules in the early stage of lung cancer.

Clinical and CT Features of Subsolid Pulmonary Nodules With Interval Growth: A Systematic Review and Meta-Analysis

Background

Establishing risk-based follow-up management strategies is crucial to the surveillance of subsolid pulmonary nodules (SSNs). However, the risk factors for SSN growth are not currently clear. This study aimed to perform a systematic review and meta-analysis to identify clinical and CT features correlated with SSN growth.

Methods

Relevant studies were retrieved from Web of Science, PubMed, Cochrane Library, and EMBASE. The correlations of clinical and CT features with SSN growth were pooled using a random-effects model or fixed-effects model depending on heterogeneity, which was examined by the Q test and I2 test. Pooled odds ratio (OR) or pooled standardized mean differences (SMD) based on univariate analyses were calculated to assess the correlation of clinical and CT features with SSN growth. Pooled ORs based on multivariate analyses were calculated to find out independent risk factors to SSN growth. Subgroup meta-analysis was performed based on nodule consistency (pure ground-glass nodule (pGGN) and part-solid nodule (PSN). Publication bias was examined using funnel plots.

Results

Nineteen original studies were included, consisting of 2444 patients and 3012 SSNs. The median/mean follow-up duration of these studies ranged from 24.2 months to 112 months. Significant correlations were observed between SSN growth and eighteen features. Male sex, history of lung cancer, nodule size &gt; 10 mm, nodule consistency, and age &gt; 65 years were identified as independent risk factors for SSN growth based on multivariate analyses results. Eight features, including male sex, smoking history, nodule size &gt; 10 mm, larger nodule size, air bronchogram, higher mean CT attenuation, well-defined border, and lobulated margin were detected to be significantly correlated with pGGNs growth. Smoking history showed no significant correlation with pGGN growth based on the multivariate analysis results.

Conclusions

Eighteen clinical and CT features were identified to be correlated with SSN growth, among which male sex, history of lung cancer, nodule size &gt; 10 mm, nodule consistency and age &gt; 65 years were independent risk factors while history of lung cancer was not correlated with pGGN growth. These factors should be considered when making risk-based follow-up plans for SSN patients.

Deep-learning reconstruction for ultra-low-dose lung CT: Volumetric measurement accuracy and reproducibility of artificial ground-glass nodules in a phantom study

OBJECTIVES

The lung nodule volume determined by CT is used for nodule diagnoses and monitoring tumor responses to therapy. Increased image noise on low-dose CT degrades the measurement accuracy of the lung nodule volume. We compared the volumetric accuracy among deep-learning reconstruction (DLR), model-based iterative reconstruction (MBIR), and hybrid iterative reconstruction (HIR) at an ultra-low-dose setting.

METHODS

Artificial ground-glass nodules (6 mm and 10 mm diameters, -660 HU) placed at the lung-apex and the middle-lung field in chest phantom were scanned by 320-row CT with the ultra-low-dose setting of 6.3 mAs. Each scan data set was reconstructed by DLR, MBIR, and HIR. The volumes of nodules were measured semi-automatically, and the absolute percent volumetric error (APEvol) was calculated. The APEvol provided by each reconstruction were compared by the Tukey-Kramer method. Inter- and intraobserver variabilities were evaluated by a Bland-Altman analysis with limits of agreements.

RESULTS

DLR provided a lower APEvol compared to MBIR and HIR. The APEvol of DLR (1.36%) was significantly lower than those of the HIR (8.01%, p = 0.0022) and MBIR (7.30%, p = 0.0053) on a 10-mm-diameter middle-lung nodule. DLR showed narrower limits of agreement compared to MBIR and HIR in the inter- and intraobserver agreement of the volumetric measurement.

CONCLUSIONS

DLR showed higher accuracy compared to MBIR and HIR for the volumetric measurement of artificial ground-glass nodules by ultra-low-dose CT.

ADVANCES IN KNOWLEDGE

DLR with ultra-low-dose setting allows a reduction of dose exposure, maintaining accuracy for the volumetry of lung nodule, especially in patients which deserve a long-term follow-up.

A predictive nomogram for two-year growth of CT-indeterminate small pulmonary nodules

OBJECTIVES

Lung cancer is the most common cancer and the leading cause of cancer-related death worldwide. The optimal management of computed tomography (CT)-indeterminate pulmonary nodules is important. To optimize individualized follow-up strategies, we developed a radiomics nomogram for predicting 2-year growth in case of indeterminate small pulmonary nodules.

METHODS

A total of 215 histopathology-confirmed small pulmonary nodules (21 benign and 194 malignant) in 205 patients with ultra-high-resolution CT (U-HRCT) were divided into growth and nongrowth nodules and were randomly allocated to the primary (n = 151) or validation (n = 64) group. The least absolute shrinkage and selection operator (LASSO) method was used for radiomics feature selection and radiomics signature determination. Multivariable logistic regression analysis was used to develop a radiomics nomogram that integrated the radiomics signature with significant clinical parameters (sex and nodule type). The area under the curve (AUC) was applied to assess the predictive performance of the radiomics nomogram. The net benefit of the radiomics nomogram was assessed using a clinical decision curve.

RESULTS

The radiomics signature and nomogram yielded AUCs of 0.892 (95% confidence interval [CI]: 0.843-0.940) and 0.911 (95% CI: 0.867-0.955), respectively, in the primary group and 0.826 (95% CI: 0.727-0.926) and 0.843 (95% CI: 0.749-0.937), respectively, in the validation group. The clinical usefulness of the nomogram was demonstrated by decision curve analysis.

CONCLUSIONS

A radiomics nomogram was developed by integrating the radiomics signature with clinical parameters and was easily used for the individualized prediction of two-year growth in case of CT-indeterminate small pulmonary nodules.

KEY POINTS

• A radiomics nomogram was developed for predicting the two-year growth of CT-indeterminate small pulmonary nodules. • The nomogram integrated a CT-based radiomics signature with clinical parameters and was valuable in developing an individualized follow-up strategy for patients with indeterminate small pulmonary nodules.

Assessing invasiveness of subsolid lung adenocarcinomas with combined attenuation and geometric feature models

The aim of this study was to develop and test multiclass predictive models for assessing the invasiveness of individual lung adenocarcinomas presenting as subsolid nodules on computed tomography (CT). 227 lung adenocarcinomas were included: 31 atypical adenomatous hyperplasia and adenocarcinomas in situ (class H1), 64 minimally invasive adenocarcinomas (class H2) and 132 invasive adenocarcinomas (class H3). Nodules were segmented, and geometric and CT attenuation features including functional principal component analysis features (FPC1 and FPC2) were extracted. After a feature selection step, two predictive models were built with ordinal regression: Model 1 based on volume (log) (logarithm of the nodule volume) and FPC1, and Model 2 based on volume (log) and Q.875 (CT attenuation value at the 87.5% percentile). Using the 200-repeats Monte-Carlo cross-validation method, these models provided a multiclass classification of invasiveness with discriminative power AUCs of 0.83 to 0.87 and predicted the class probabilities with less than a 10% average error. The predictive modelling approach adopted in this paper provides a detailed insight on how the value of the main predictors contribute to the probability of nodule invasiveness and underlines the role of nodule CT attenuation features in the nodule invasiveness classification.