The impact of histology and ground-glass opacity component on volume doubling time in primary lung cancer

Histology-driven hypofractionated radiation therapy schemes for early-stage lung adenocarcinoma and squamous cell carcinoma

BACKGROUND AND PURPOSE

Histology was found to be an important prognostic factor for local tumor control probability (TCP) after stereotactic body radiotherapy (SBRT) of early-stage non-small-cell lung cancer (NSCLC). A histology-driven SBRT approach has not been explored in routine clinical practice and histology-dependent fractionation schemes remain unknown. Here, we analyzed pooled histologic TCP data as a function of biologically effective dose (BED) to determine histology-driven fractionation schemes for SBRT and hypofractionated radiotherapy of two predominant early-stage NSCLC histologic subtypes adenocarcinoma (ADC) and squamous cell carcinoma (SCC).

MATERIAL AND METHODS

The least-χ2 method was used to fit the collected histologic TCP data of 8510 early-stage NSCLC patients to determine parameters for a well-developed radiobiological model per the Hypofractionated Treatment Effects in the Clinic (HyTEC) initiative.

RESULTS

A fit to the histologic TCP data yielded independent radiobiological parameter sets for radiotherapy of early-stage lung ADC and SCC. TCP increases steeply with BED and reaches an asymptotic maximal plateau, allowing us to determine model-independent optimal fractionation schemes of least doses in 1-30 fractions to achieve maximal tumor control for early-stage lung ADC and SCC, e.g., 30, 44, 48, and 51 Gy for ADC, and 32, 48, 54, and 58 Gy for SCC in 1, 3, 4, and 5 fractions, respectively.

CONCLUSION

We presented the first determination of histology-dependent radiobiological parameters and model-independent histology-driven optimal SBRT and hypofractionated radiation therapy schemes for early-stage lung ADC and SCC. SCC requires substantially higher radiation doses to maximize tumor control than ADC, plausibly attributed to tumor genetic diversity and microenvironment. The determined optimal SBRT schemes agree well with clinical practice for early-stage lung ADC. These proposed optimal fractionation schemes provide first insights for histology-based personalized radiotherapy of two predominant early-stage NSCLC subtypes ADC and SCC, which require further validation with large-scale histologic TCP data.

Interpreting Lung Cancer Screening CTs: Practical Approach to Lung Cancer Screening and Application of Lung-RADS

Lung cancer screening via low-dose computed tomography (CT) reduces mortality from lung cancer, and eligibility criteria have recently been expanded to include patients aged 50 to 80 with at least 20 pack-years of smoking history. Lung cancer screening CTs should be interepreted with use of Lung Imaging Reporting and Data System (Lung-RADS), a reporting guideline system that accounts for nodule size, density, and growth. The revised version of Lung-RADS includes several important changes, such as expansion of the definition of juxtapleural nodules, discussion of atypical pulmonary cysts, and stepped management for suspicious nodules. By using Lung-RADS, radiologists and clinicians can adopt a uniform approach to nodules detected during CT lung cancer screening and reduce false positives.

Shape-Sensing Robotic-Assisted Bronchoscopy versus Computed Tomography-Guided Transthoracic Biopsy for the Evaluation of Subsolid Pulmonary Nodules

INTRODUCTION

Lung cancer remains the leading cause of cancer death worldwide. Subsolid nodules (SSN), including ground-glass nodules (GGNs) and part-solid nodules (PSNs), are slow-growing but have a higher risk for malignancy. Therefore, timely diagnosis is imperative. Shape-sensing robotic-assisted bronchoscopy (ssRAB) has emerged as reliable diagnostic procedure, but data on SSN and how ssRAB compares to other diagnostic interventions such as CT-guided transthoracic biopsy (CTTB) are scarce. In this study, we compared diagnostic yield of ssRAB versus CTTB for evaluating SSN.

METHODS

A retrospective study of consecutive patients who underwent either ssRAB or CTTB for evaluating GGN and PSN with a solid component less than 6 mm from February 2020 to April 2023 at Mayo Clinic Florida and Rochester. Clinicodemographic information, nodule characteristics, diagnostic yield, and complications were compared between ssRAB and CTTB.

RESULTS

A total of 66 nodules from 65 patients were evaluated: 37 PSN and 29 GGN. Median size of PSN solid component was 5 mm (IQR: 4.5, 6). Patients were divided into two groups: 27 in the ssRAB group and 38 in the CTTB group. Diagnostic yield was 85.7% for ssRAB and 89.5% for CTTB (p = 0.646). Sensitivity for malignancy was similar between ssRAB and CTTB (86.4% vs. 88.5%; p = 0.828), with no statistical difference. Complications were more frequent in CTTB with no significant difference (8 vs. 2; p = 0.135).

CONCLUSION

Diagnostic yield for SSN was similarly high for ssRAB and CTTB, with ssRAB presenting less complications and allowing mediastinal staging within the same procedure.

Minimum latency effects for cancer associated with exposures to radiation or other carcinogens

BACKGROUND

In estimating radiation-associated cancer risks a fixed period for the minimum latency is often assumed. Two empirical latency functions have been used to model latency, continuously increasing from 0. A stochastic biologically-based approach yields a still more plausible way of describing latency and can be directly estimated from clinical data.

METHODS

We derived the parameters for a stochastic biologically-based model from tumour growth data for various cancers, and least-squares fitted the two types of empirical latency function to the stochastic model-predicted cumulative probability.

RESULTS

There is wide variation in growth rates among tumours, particularly slow for prostate and thyroid cancer and particularly fast for leukaemia. The slow growth rate for prostate and thyroid tumours implies that the number of tumour cells required for clinical detection cannot greatly exceed 106. For all tumours, both empirical latency functions closely approximated the predicted biological model cumulative probability.

CONCLUSIONS

Our results, illustrating use of a stochastic biologically-based model using clinical data not tied to any particular carcinogen, have implications for estimating latency associated with any mutagen. They apply to tumour growth in general, and may be useful for example, in planning screenings for cancer using imaging techniques.

Surgical Resection of Benign Nodules in Lung Cancer Screening: Incidence and Features

Introduction

Interventions and surgical procedures are common for nonmalignant lung lesions detected on lung cancer screening (LCS). Inadvertent surgical resection of benign nodules with a clinical suspicion of lung cancer can occur, can be associated with complications, and adds to the cost of screening. The objective of this study is to assess the characteristics of surgically resected benign nodules detected on LCS computed tomography which were presumed to be lung cancers.

Methods

This retrospective study included 4798 patients who underwent LCS between June 2014 and January 2021. The benign lung nodules, surgically resected with a presumed cancer diagnosis, were identified from the LCS registry. Patient demographics, imaging characteristics, and pathologic diagnoses of benign nodules were analyzed.

Results

Of the 4798 patients who underwent LCS, 148 (3.1%) underwent surgical resection of a lung nodule, and of those who had a resection, 19 of 148 (12.8%) had a benign diagnosis (median age = 64 y, range: 56-77 y; F = 12 of 19, 63.2%; M = seven of 19, 36.8%). The median nodule size was 10 mm (range: 6-31 mm). Most nodules were solid (15 of 19, 78.9%), located in the upper lobes (11 of 19; 57.9%), and were peripheral (17 of 19, 89.5%). Most nodules (13 of 17; 76.5%) had interval growth, and four of 17 (23.5%) had increased fluorodeoxyglucose uptake. Of the 19 patients, 17 (89.5%) underwent sublobar resection (16 wedge resection and one segmentectomy), whereas two central nodules (10.5%) had lobectomies. Pathologies identified included focal areas of fibrosis or scarring (n = 8), necrotizing granulomatous inflammation (n = 3), other nonspecific inflammatory focus (n = 3), benign tumors (n = 3), reactive lymphoid hyperplasia (n = 1), and organizing pneumonia (n = 1).

Conclusions

Surgical resections of benign nodules that were presumed malignant are infrequent and may be unavoidable given overlapping imaging features of benign and malignant nodules. Knowledge of benign pathologies that can mimic malignancy may help reduce the incidence of unnecessary surgeries.

Growth characteristics of early-stage (IA) lung adenocarcinoma and its value in predicting lymph node metastasis

BACKGROUND

We aim to compare the differences in growth characteristics between part-solid and solid lung adenocarcinoma, and to investigate the value of volume doubling time (VDT) or mass doubling time (MDT) in predicting lymph node (LN) metastasis and preoperative evaluation in patients of early-stage (IA) non-small cell lung cancer (NSCLC).

METHOD

We reviewed 8,653 cases of surgically resected stage IA lung adenocarcinoma between 2018 and 2022, with two follow-up visits at least 3 months apart, comparing diameter, volume, and mass growth of pSN and SN. VDT and MDT calculations for nodules with a volume change of at least 25%. Univariable or multivariable analysis was used to identify the risk factors. The area under the curve (AUC) for the receiver operating characteristic (ROC) curves was used to evaluate the diagnostic value.

RESULTS

A total of 144 patients were included 114 with solid nodules (SN) and 25 with part-solid nodules (pSN). During the follow-up period, the mean VDTt and MDTt of SN were shorter than those of pSN, 337 vs. 541 days (p = 0.005), 298 vs. 458 days (p = 0.018), respectively. Without considering the ground-glass component, the mean VDTc and MDTc of SN were shorter than the solid component of pSN, 337 vs. 498 days (p = 0.004) and 298 vs. 453 days (p = 0.003), respectively. 27 nodules were clinically and pathologically diagnosed as N1/N2. Logistic regression identified initial diameter (p < 0.001), consolidation increase (p = 0.019), volume increase (p = 0.020), mass increase (p = 0.021), VDTt (p = 0.002), and MDTt (p = 0.004) were independent factors for LN metastasis. The ROC curves showed that the AUC for VDTt was 0.860 (95% CI, 0.778-0.943; p < 0.001) and for MDTt was 0.848 (95% CI, 0.759-0.936; p < 0.001).

CONCLUSIONS

Our study showed significant differences in the growth characteristics of pSN and SN, and the application of VDT and MDT could be a valid predictor LN metastasis in patients with early-stage NSCLC.

Risk and Time to Diagnosis of Lung Cancer in Incidental Pulmonary Nodules

PURPOSE

To determine the risk of lung cancer in incidental pulmonary nodules, as well as the time until cancer growth is detected.

PATIENTS AND METHODS

This retrospective study examined patients with incidental nodules detected on chest computed tomography (CT) in 2017. Characteristics of the dominant nodule were automatically extracted from CT reports, and cancer diagnoses were manually verified by a thoracic radiologist. Nodules were categorized per Fleischner Society guideline categories: solid <6 mm, solid 6 to 8 mm, solid >8 mm, subsolid <6 mm, ground glass nodules ≥6 mm, and part-solid nodules ≥6 mm. The time to nodule growth was determined by CT reports.

RESULTS

A total of 3180 patients (nodules) were included, of which 155 (5%) were diagnosed with lung cancer. By category, 7/1601 (0.4%) solid nodules <6 mm, 11/713 (1.5%) solid nodules 6 to 8 mm, 71/446 (15.9%) solid nodules >8 mm, 1/124 (0.8%) subsolid nodules <6 mm, 29/202 (14.4%) ground glass nodules ≥6 mm, and 36/94 (37.9%) part-solid nodules ≥6 mm were malignant. Of solid lung cancers <6 mm, growth was observed in 1/4 imaged by 1 year and 2/5 by 2 years; of solid lung cancers 6 to 8 mm, growth was observed in 3/10 imaged by 1 year and 6/10 by 2 years.

CONCLUSION

Solid nodules <6 mm have a very low risk of malignancy and may not require routine follow-up. However, when malignant, growth is often not observed until 2 or more years later; therefore, stability at 1 to 2 years does not imply benignity.

Volume Doubling Times of Benign and Malignant Nodules in Lung Cancer Screening

The purpose of this study was to measure the fractions of benign and malignant nodules in lung cancer screening that grow on follow-up, and to measure the volume doubling time (VDT) of those that grow. In this retrospective study, we included nodules from CT lung cancer screening in our healthcare network, for which a follow-up CT performed at least 2 months later showed the nodule to be persistent. The nodules were measured using semiautomated volumetric segmentation software at both timepoints. Growth was defined as an increase in volume by 25%. VDTs were calculated, and the fraction <400 days was recorded. Categorical variables were compared with Fisher's exact test, and continuous variables by the Wilcoxon test. The study included 153 nodules, of which 44 were malignant and 109 benign. Thirty (68%) of malignant nodules and 36 (33%) of benign nodules grew (P < 0.001). For growing nodules, VDT was 318 days for malignant nodules and 389 for benign nodules (P = 0.21). For growing solid nodules, VDT was 204 days for malignant nodules and 386 days for benign nodules (P = 0.01); of these, VDT was <400 days for 12/13 (92%) of malignant nodules and 15/26 (58%) of benign nodules. In conclusion, malignant nodules were more likely to grow, and solid malignant nodules grew faster, than benign nodules. However, there was substantial overlap between benign and malignant nodules. This limits the utility of volume doubling time in determining malignant nodules.

Growth assessment of pure ground-glass nodules on CT: comparison of density and size measurement methods

PURPOSE

To investigate the differences of size and density measurements in assessing pure ground-glass nodules (pGGNs) growth, and compare the growth rates and growth proportions of the two methods during follow-up period.

METHODS

Ninety patients with at least 3 consecutive thin-section chest CTs and confirmed 103 pGGNs on baseline CT were enrolled retrospectively. Using the two definitions of size and density to evaluate pGGNs growth with semi-automated segmentation. Then, the two methods were compared to assess differences in pGGNs growth.

RESULTS

For the size and density methods to assess nodule growth, 50.5% and 26.2% showed interval growth at the last CT (p < 0.001). Among the 19 nodules that grew in both size and density, the volume doubling time (VDT) of solid component (mean, 317.1; standard deviation, 224.8 days) was shorter than total VDT (median, 942.8; range, 400.1-2315.9 days) (p < 0.001). Of the 27 growth pGGNs assessed by the density method, the growth rates at years 1 and 2 were 25.9% and 63.0%, while the growth rates of 52 growing nodules assessed by size method were 11.5% and 48.1%, respectively. Twenty of 103 (19.4%) nodules were classified into category 4A lesions, and 7 (6.8%) were 4B lesions.

CONCLUSION

Compared to size measurements, observed density increases have a higher proportion of early growth and faster growth rates in growing nodules. Clinicians need to pay close attention to the nodules of new solid components and make timely decision management.

Invasiveness assessment by artificial intelligence against intraoperative frozen section for pulmonary nodules ≤ 3 cm

PURPOSE

To investigate the performance of an artificial intelligence (AI) algorithm for assessing the malignancy and invasiveness of pulmonary nodules in a multicenter cohort.

METHODS

A previously developed deep learning system based on a 3D convolutional neural network was used to predict tumor malignancy and invasiveness. Dataset of pulmonary nodules no more than 3 cm was integrated with CT images and pathologic information. Receiver operating characteristic curve analysis was used to evaluate the performance of the system.

RESULTS

A total of 466 resected pulmonary nodules were included in this study. The areas under the curves (AUCs) of the deep learning system in the prediction of malignancy as compared with pathological reports were 0.80, 0.80, and 0.75 for all, subcentimeter, and solid nodules, respectively. Additionally, the AUC in the AI-assisted prediction of invasive adenocarcinoma (IA) among subsolid lesions (n = 184) was 0.88. Most malignancies that were misdiagnosed by the AI system as benign diseases with a diameter measuring greater than 1 cm (26/250, 10.4%) presented as solid nodules (19/26, 73.1%) on CT. In an exploratory analysis involving nodules underwent intraoperative pathologic examination, the concordance rate in identifying IA between the AI model and frozen section examination was 0.69, with a sensitivity of 0.50 and specificity of 0.97.

CONCLUSION

The deep learning system can discriminate malignant diseases for pulmonary nodules measuring no more than 3 cm. The AI model has a high positive predictive value for invasive adenocarcinoma with respect to intraoperative frozen section examination, which might help determine the individualized surgical strategy.

Tumor volume doubling time as a potential predictor of prognosis in clinical stage I lung squamous cell carcinoma

BACKGROUND

Tumor volume doubling time (VDT) has been shown to predict prognosis in various non-small cell lung carcinoma with scant evidence for lung squamous cell carcinoma (SCC). The purpose of this study was to investigate the prognostic value of tumor VDT in resected lung SCCs.

METHODS

In this study, subjects were 51 patients who underwent lobectomy for clinical stage I SCC of the peripheral lung at our institution between January 2006 and April 2020. Univariable and multivariable analyses of overall survival (OS) and recurrence-free survival (RFS) were performed using the Cox proportional hazards model. The Kaplan-Meier method was used to create OS and RFS curves and to determine statistical significance. The cut-off value of VDT was defined by receiver operating characteristic (ROC) curve analysis on survival.

RESULTS

Multivariable analysis found only VDT (HR, 0.990; 95% CI: 0.979-0.997) to be an independent predictor of OS. Also, only VDT (HR, 0.989; 95% CI: 0.978-0.995) was an independent predictor of RFS. The 5-year OS rates were 88.4% and 30.4% in the long (≥150 days) and short (<150 days) VDT groups, respectively (P=0.002). The 5-year RFS rates were 88.8% and 26.5% in the long (≥150 days) and short (<150 days) VDT groups, respectively (P<0.001).

CONCLUSIONS

Tumor VDT was found to be a useful prognostic predictor in clinical stage I lung SCC in this study.

Management of ground-glass opacities and sub-solid pulmonary nodules: a surgeon's perspective

The regular use of high-resolution computed tomogram scans has led to an increase in the detection of asymptomatic ground-glass opacities and sub-solid nodules at an early stage. Different growth patterns of these lesions are making decision-making a real challenge. With growing experience and improving radiology interventions, management of these lesions is changing constantly. However, with variations in growth patterns and outcomes, immediate treatment options as well as follow-up surveillance and subsequent interventions can be confounding for the clinicians. This mini review describes algorithms for managing these ground-glass opacities (GGOs) and sub-solid nodules (SSNs) with a focus on the surgical options.

Is using a consolidation tumor ratio 0.5 as criterion feasible in daily practice? Evaluation of interobserver measurement variability of consolidation tumor ratio of lung cancer less than 3 cm in size

BACKGROUND

Consolidation tumor ratio (CTR) calculated as the ratio of the tumor consolidation diameter to the tumor maximum diameter on thin-section computed tomography (CT) of lung cancer has been reported as an important prognostic factor. It has also been used for treatment decision-making. This study aimed to investigate the interobserver variability of CTR measurements on preoperative CT and propose a clinically useful CTR-based classification criterion.

METHODS

We enrolled 119 patients who underwent surgery for suspected or diagnosed small-sized lung cancer (≤3.0 cm in diameter). Nine doctors reviewed preoperative CT scans to measure CTR. Interobserver variability of CTR measurements was evaluated using the coefficient of variation (CV) and Fleiss' κ. The prognostic effect of the CTR-based classification was assessed using the Kaplan-Meier method.

RESULTS

Interobserver variability of CTR measurement was the highest for tumors with the lowest CTR (CTR = 0); it decreased as CTR increased and reached a plateaued level of low variability (CV <0.5) at CTR of 0.5. We proposed a three-group classification based on the findings of CTR interobserver variability (CTR < 0.5, 0.5 ≤ CTR < 1, and CTR = 1). Interobserver agreement of the judgment of the CTR-based classification was excellent (Fleiss' κ = 0.81). The classification significantly stratified patient prognosis (p < 0.001, 5-year overall survival rates with CTR < 0.5, 0.5 ≤ CTR < 1, and CTR = 1 were 100, 88, and 73.8%, respectively).

CONCLUSIONS

CTR 0.5 is a clinically relevant and helpful cutoff for treatment decision-making in patients with early-stage lung cancer based on high interobserver agreement and good prognostic stratification.

A CT-based nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodule according to the 2021 WHO classification

Purpose

To establish a nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodules (SSNs) according to the 2021 WHO classification.

Methods

A total of 656 patients who underwent SSNs resection were retrospectively enrolled. Among them, 407 patients were assigned to the derivation cohort and 249 patients were assigned to the validation cohort. Univariate and multi-variate logistic regression algorithms were utilized to identity independent risk factors of adenocarcinomas. A nomogram based on the risk factors was generated to predict the risk of adenocarcinomas. The discrimination ability of the nomogram was evaluated using the concordance index (C-index), its performance was calibrated using a calibration curve, and its clinical significance was evaluated using decision curves and clinical impact curves.

Results

Lesion size, mean CT value, vascular change and lobulation were identified as independent risk factors for adenocarcinomas. The C-index of the nomogram was 0.867 (95% CI, 0.833-0.901) in derivation cohort and 0.877 (95% CI, 0.836-0.917) in validation cohort. The calibration curve showed good agreement between the predicted and actual risks. Analysis of the decision curves and clinical impact curves revealed that the nomogram had a high standardized net benefit.

Conclusions

A nomogram for predicting the risk of adenocarcinomas in patients with SSNs was established in light of the 2021 WHO classification. The developed model can be adopted as a pre-operation tool to improve the surgical management of patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40644-022-00483-1.

Lung-RADS Category 3 and 4 Nodules on Lung Cancer Screening in Clinical Practice

Background: Lung-RADS category 3 and 4 nodules account for most cancers among screening-detected lung cancers and are considered actionable nodules with management implications. The cancer frequency among such nodules is estimated in the Lung-RADS recommendations and has been investigated primarily using retrospectively assigned Lung-RADS classifications. Objective: To assess the frequency of cancer among lung nodules assigned Lung-RADS category 3 or 4 at lung cancer screening (LCS) in clinical practice and factors that impact the cancer frequency within each category. Methods: This retrospective study was based on review of clinical radiology reports of 9148 consecutive low-dose CT LCS examinations performed in 4798 patients between June 2014 and January 2021 as part of an established LCS program. Unique nodules assigned Lung-RADS category 3 or 4 (4A, 4B, or 4X) that were clinically categorized as benign or malignant by a multidisciplinary conference, considering histologic analysis and follow-up imaging, were selected for further analysis; benign diagnoses based on stability required at least 12 months of follow-up imaging. Indeterminate nodules were excluded. Cancer frequencies were evaluated. Results: Of the 9148 LCS examinations, 857 (9.4%) were assigned Lung-RADS category 3, and 721 (7.9%) were assigned category 4. The final analysis included 1297 unique nodules in 1139 patients (598 men, 541 women; mean age, 66.0±6.3 years). A total of 1108/1297 (85.4%) nodules were deemed benign, and 189/1297 (14.6%) were deemed malignant. Frequencies of malignancy for category 3, 4A, 4B, and 4X nodules were 3.9%, 15.5%, 36.3%, and 76.8%, respectively. A total of 45/46 (97.8%) endobronchial nodules (all category 4A) were deemed benign based on resolution. Cancer frequency was 13.1%, 24.4%, and 13.5% for solid, part-solid, and ground-glass nodules, respectively. Conclusions: When applying Lung-RADS for LCS clinical practice, the frequency of Lung-RADS category 3 and 4 nodules, as well as cancer frequency in these categories, were higher than prevalence and cancer risk estimated for category 3 and 4 nodules in the Lung-RADS recommendations and reported in earlier studies using retrospective category assignments. Nearly all endobronchial category 4A nodules were benign. Clinical Impact: Future Lung-RADS iterations should consider these findings from real-world practice to improve the system's clinical utility.

Pulmonary nodular lymphoid hyperplasia in a 53-year-old man with malignant sign: a case report

Background

Pulmonary nodular lymphoid hyperplasia (PNLH) is a rare benign illness. Due to atypical clinical and radiographic presentations, diagnosis largely depends on postoperative pathological examination. Thus, preoperative misdiagnosis is often occurred.

Case presentation

We present a case of asymptomatic PNLH that was seen as ground-glass opacity (GGO) on computed tomography (CT). After 3-year observation, the diagnosis tends to adenocarcinoma owing to increasing density of the node and vessel convergence sign, which were signs of malignancy. Video-assisted segmentectomy (S10) was carried out. Histopathologic examination of postoperative specimen showed follicular lymphoid hyperplasia with interfollicular lymphoplasmacytosis, consistent with PNLH. The follow-up chest CT images showed no recurrence or metastasis.

Conclusion

Although it is a benign disease, PNLH can exhibit malignant signs in the imaging examinations, which could lead to misdiagnosis. This reminds us of the uncertainty between imaging findings and diagnosis. The diagnosis depends on postoperative pathological examination. Volume doubling time is a potential parameter to differentiate PNLH from lung cancer.

Cost-Effectiveness of Treatment Thresholds for Subsolid Pulmonary Nodules in CT Lung Cancer Screening

Background Guidelines such as the Lung CT Screening Reporting and Data System (Lung-RADS) are available for determining when subsolid nodules should be treated within lung cancer screening programs, but they are based on expert opinion. Purpose To evaluate the cost-effectiveness of varying treatment thresholds for subsolid nodules within a lung cancer screening setting by using a simulation model. Materials and Methods A previously developed model simulated 10 million current and former smokers undergoing CT lung cancer screening who were assumed to have a ground-glass nodule (GGN) at baseline. Nodules were allowed to grow and to develop solid components over time according to a monthly cycle and lifetime horizon. Management strategies generated by varying treatment thresholds, including the solid component size and use of the Brock risk calculator, were tested. For each strategy, average U.S. costs and quality-adjusted life years (QALYs) gained per patient were computed, and the incremental cost-effectiveness ratios (ICERs) of those on the efficient frontier were calculated. One-way and probabilistic sensitivity analyses of results were performed by varying several relevant parameters, such as treatment costs or malignancy growth rates. Results Variants of the Lung-RADS guidelines that did not treat pure GGNs were cost-effective. Strategies based on the Brock risk calculator did not reach the efficient frontier. The strategy with the highest QALYs under a willingness-to-pay threshold of $100 000 per QALY included no treatment of GGNs and a threshold of 4-mm solid component size for treatment of subsolid nodules. This strategy yielded an ICER of $52 993 per QALY (95% CI: 44 407, 64 372). Probabilistic sensitivity analysis showed this was the optimal strategy under a range of parameter variations. Conclusion Treatment of pure ground-glass nodules was not cost-effective. Strategies that use modifications of the Lung CT Screening Reporting and Data System guidelines were cost-effective for treating part-solid nodules; an optimal threshold of 4 mm for the solid component yielded the most quality-adjusted life years. © RSNA, 2021 Online supplemental material is available for this article.

Considering lead-time bias in evaluating the effectiveness of lung cancer screening with real-world data

Low-dose computed tomography screening can be used to diagnose lung cancer at a younger age compared to no screening. Real-world studies observing mortality after lung cancer diagnosis are subject to lead-time bias. This study developed a method using a nationwide cancer registry and stage shift from trial for the adjustment of lead-time bias. 78,897 Taiwanese nationwide lung cancer patients aged 55–82 were matched with 788,820 referents randomly selected from the general population at a ratio of 1:10 by age, sex, calendar year, and comorbidities, to estimate the pathology- and stage-specific life expectancy (LE). Loss-of-LE is the difference between the LE of cancer patients and that of referents. By multiplying LE and loss-of-LE by the pathology and stage shift in the National Lung Screening Trial (NLST), we compared the effectiveness of cancer screening measured by LE gained and loss-of-LE saved. The mean LEs of stage IA and IV adenocarcinoma were 14.5 and 1.9 years, respectively, indicating a LE gain of 12.6 years. However, the mean loss-of-LEs of stage IA and IV adenocarcinoma were 3.7 and 15.1 years, respectively, with a saving of only 11.4 years, implying an adjustment of different distributions of age, sex, and calendar year of diagnosis from stage shift and a reduction in lead-time bias. Applying such estimations on the results of 10,000 participants with the same pathology and stage shift in the NLST, the benefit of screening using LE gained would be 410.3 (95% prediction interval: 328.4 to 503.3) years. It became 297.1 (95% prediction interval: 187.8 to 396.4) years when using loss-of-LE saved, indicating the former approach would overestimate the effectiveness by 38%. Our approach of multiplying loss-of-LE by pathology and stage shift to estimate loss-of-LE saved could adjust for different distributions of age, sex, and calendar year at early diagnosis and reduce lead-time bias.

Artificial intelligence for detection and characterization of pulmonary nodules in lung cancer CT screening: ready for practice?

Lung cancer computed tomography (CT) screening trials using low-dose CT have repeatedly demonstrated a reduction in the number of lung cancer deaths in the screening group compared to a control group. With various countries currently considering the implementation of lung cancer screening, recurring discussion points are, among others, the potentially high false positive rates, cost-effectiveness, and the availability of radiologists for scan interpretation. Artificial intelligence (AI) has the potential to increase the efficiency of lung cancer screening. We discuss the performance levels of AI algorithms for various tasks related to the interpretation of lung screening CT scans, how they compare to human experts, and how AI and humans may complement each other. We discuss how AI may be used in the lung cancer CT screening workflow according to the current evidence and describe the additional research that will be required before AI can take a more prominent role in the analysis of lung screening CT scans.

Lung Cancer Radiotherapy: Simulation and Analysis Based on a Multicomponent Mathematical Model

Background

Lung cancer has been one of the most deadly illnesses all over the world, and radiotherapy can be an effective approach for treating lung cancer. Now, mathematical model has been extended to many biomedical fields to give a hand for analysis, evaluation, prediction, and optimization.

Methods

In this paper, we propose a multicomponent mathematical model for simulating the lung cancer growth as well as radiotherapy treatment for lung cancer. The model is digitalized and coded for computer simulation, and the model parameters are fitted with many research and clinical data to provide accordant results along with the growth of lung cancer cells in vitro.

Results

Some typical radiotherapy plans such as stereotactic body radiotherapy, conventional fractional radiotherapy, and accelerated hypofractionated radiotherapy are simulated, analyzed, and discussed. The results show that our mathematical model can perform the basic work for analysis and evaluation of the radiotherapy plan.

Conclusion

It will be expected that in the near future, mathematical model will be a valuable tool for optimization in personalized medical treatment.

Lung-RADS Version 1.1: Challenges and a Look Ahead, From the AJR Special Series on Radiology Reporting and Data Systems

In 2014, the American College of Radiology (ACR) created Lung-RADS 1.0. The system was updated to Lung-RADS 1.1 in 2019, and further updates are anticipated as additional data become available. Lung-RADS provides a common lexicon and standardized nodule follow-up management paradigm for use when reporting lung cancer screening (LCS) low-dose CT (LDCT) chest examinations and serves as a quality assurance and outcome monitoring tool. The use of Lung-RADS is intended to improve LCS performance and lead to better patient outcomes. To date, the ACR's Lung Cancer Screening Registry is the only LCS registry approved by the Centers for Medicare & Medicaid Services and requires the use of Lung-RADS categories for reimbursement. Numerous challenges have emerged regarding the use of Lung-RADS in clinical practice, including the timing of return to LCS after planned follow-up diagnostic evaluation; potential substitution of interval diagnostic CT for future LDCT; role of volumetric analysis in assessing nodule size; assessment of nodule growth; assessment of cavitary, subpleural, and category 4X nodules; and variability in reporting of the S modifier. This article highlights the major updates between versions 1.0 and 1.1 of Lung-RADS, describes the system's ongoing challenges, and summarizes current evidence and recommendations.

Volume and Mass Doubling Time of Lung Adenocarcinoma according to WHO Histologic Classification

Objective

This study aimed to evaluate the tumor doubling time of invasive lung adenocarcinoma according to the International Association of the Study for Lung Cancer (IASLC)/American Thoracic Society (ATS)/European Respiratory Society (ERS) histologic classification.

Materials and Methods

Among the 2905 patients with surgically resected lung adenocarcinoma, we retrospectively included 172 patients (mean age, 65.6 ± 9.0 years) who had paired thin-section non-contrast chest computed tomography (CT) scans at least 84 days apart with the same CT parameters, along with 10 patients with squamous cell carcinoma (mean age, 70.9 ± 7.4 years) for comparison. Three-dimensional semiautomatic segmentation of nodules was performed to calculate the volume doubling time (VDT), mass doubling time (MDT), and specific growth rate (SGR) of volume and mass. Multivariate linear regression, one-way analysis of variance, and receiver operating characteristic curve analyses were performed.

Results

The median VDT and MDT of lung cancers were as follows: acinar, 603.2 and 639.5 days; lepidic, 1140.6 and 970.1 days; solid/micropapillary, 232.7 and 221.8 days; papillary, 599.0 and 624.3 days; invasive mucinous, 440.7 and 438.2 days; and squamous cell carcinoma, 149.1 and 146.1 days, respectively. The adjusted SGR of volume and mass of the solid-/micropapillary-predominant subtypes were significantly shorter than those of the acinar-, lepidic-, and papillary-predominant subtypes. The histologic subtype was independently associated with tumor doubling time. A VDT of 465.2 days and an MDT of 437.5 days yielded areas under the curve of 0.791 and 0.795, respectively, for distinguishing solid-/micropapillary-predominant subtypes from other subtypes of lung adenocarcinoma.

Conclusion

The tumor doubling time of invasive lung adenocarcinoma differed according to the IASCL/ATS/ERS histologic classification.

Subsolid pulmonary nodules: Controversy and perspective

Ground glass and part-solid nodules, collectively referred to as subsolid nodules, present a challenge in management, with a high risk of malignancy but, when malignant, demonstrating indolent behavior. Emerging data suggest longer follow-up intervals and shorter duration of follow-up is likely appropriate in these nodules. Additionally, definitive therapy is shifting to less aggressive approaches such as sub-lobar resection. Patients may benefit from individualized approaches, incorporating both patient and imaging features to determine whether treatment is necessary.

The growth feature and its diagnostic value for benign and malignant pulmonary nodules met in routine clinical practice

Background

Growth rate is an independent risk factor for lung cancer in screened pulmonary nodules. This study aimed to clarify growth characteristics of pulmonary nodules in routine clinical practice and examine whether volume doubling time (VDT) can predict the malignancy of these nodules.

Methods

We retrospectively enrolled patients with 5-30-mm-sized pulmonary nodules that had been surgically resected after a follow-up of at least 3 months. Two follow-up computed tomography (CT) images with similar thickness and long interval were obtained. Then, three-dimensional (3D) manual segmentation for all nodules was performed on two follow-up CT scans. Subsequently, VDT was calculated for nodules with a change in volume of at least 25%.

Results

Overall, 305 pulmonary nodules in 305 patients (men, 36.7%; median age, 57) were included. The mean increased diameter, mass, and volume of benign (n=86) and malignant (n=219) nodules were 0.09 vs. 2.37 mm, 0.10 vs. 0.66 g, and 32.74 vs. 1,871.28 mm3, respectively (P<0.05). In total, 24 of 86 benign nodules (28%, 18 grew and 6 shrank) and 121 of 219 malignant nodules (55%, 114 grew and 7 shrank) changed over time. The median VDTs of growing benign and malignant nodules were 389 and 526 days, respectively, (P=0.18), and the area under the receiver operating characteristic (ROC) curve was 0.67 (0.55-0.78), with a sensitivity and specificity of 69% and 58%, respectively. The median VDT for growing nodules was 339 days for inflammatory pseudotumors, 226 days for granulomas, 640 days for benign tumors, 1,541 days for enlarged lymph nodes, 762 days for adenocarcinoma in situ, 954 days for microinvasive adenocarcinoma, 534 days for invasive adenocarcinoma, and 118 days for squamous cell carcinoma.

Conclusions

In routine clinical practice, many malignant nodules could grow slowly or even remain stable over time. Regarding growing nodules, the diagnostic value of VDT was limited.

Volume Doubling Times of Lung Adenocarcinomas: Correlation with Predominant Histologic Subtypes and Prognosis

Background The volume doubling time (VDT) is a key parameter in the differentiation of aggressive tumors from slow-growing tumors. How different histologic subtypes of primary lung adenocarcinomas vary in their VDT and the prognostic value of this measurement is unknown. Purpose To investigate differences in VDT between the predominant histologic subtypes of primary lung adenocarcinomas and to assess the correlation between VDT and prognosis. Materials and Methods This retrospective study included patients who underwent at least two serial CT examinations before undergoing operation between July 2010 and December 2018. Three-dimensional tumor segmentation was performed on two CT images and VDTs were calculated. VDTs were compared between predominant histologic subtypes and lesion types by using Kruskal-Wallis tests. Disease-free survival (DFS) was obtained in patients undergoing surgical procedures before July 2017. Univariable and multivariable Cox proportional hazards regression analyses were performed to determine predictors of DFS. Results Among 268 patients (mean age, 64 years ± 8 [standard deviation]; 143 men), there were 30 lepidic, 87 acinar, 109 papillary, and 42 solid or micropapillary predominant subtypes. The median VDT was 529 days (interquartile range, 278-872 days) for lung adenocarcinomas. VDTs differed across subtypes (P < .001) and were shortest in solid or micropapillary subtypes (229 days; interquartile range, 77-530 days). Solid lesions (VDT, 248 days) had shorter VDTs than subsolid lesions (part-solid lesions, 665 days; nonsolid lesions, 648 days) (P < .001). In the 148 patients (mean age, 64 years ± 8; 89 men) included in the survival analysis, 35 patients had disease recurrence and 17 patients died. VDT (<400 days) was an independent risk factor for poor DFS (hazard ratio, 2.6; P = .01) and higher TNM stage. Adding VDT to TNM stage improved model performance (C-index, 0.69 for TNM stage vs 0.77 for combined VDT class and TNM stage; P = .002). Conclusion Volume doubling times varied significantly according to the predominant histologic subtypes of lung adenocarcinoma and had additional prognostic value for disease-free survival. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Ko in this issue.

Cancer Risk in Subsolid Nodules in the National Lung Screening Trial

Background Subsolid pulmonary nodules, comprising pure ground-glass nodules (GGNs) and part-solid nodules (PSNs), have a high risk of indolent malignancy. Lung Imaging Reporting and Data System (Lung-RADS) nodule management guidelines are based on expert opinion and lack independent validation. Purpose To evaluate Lung-RADS estimates of the malignancy rates of subsolid nodules, using nodules from the National Lung Screening Trial (NLST), and to compare Lung-RADS to the NELSON trial classification as well as the Brock University calculator. Materials and Methods Subsets of GGNs and PSNs were selected from the NLST for this retrospective study. A thoracic radiologist reviewed the baseline and follow-up CT images, confirmed that they were true subsolid nodules, and measured the nodules. The primary outcome for each nodule was the development of malignancy within the follow-up period (median, 6.5 years). Nodules were stratified according to Lung-RADS, NELSON trial criteria, and the Brock model. For analyses, nodule subsets were weighted on the basis of frequency in the NLST data set. Nodule stratification models were tested by using receiver operating characteristic curves. Results A total of 622 nodules were evaluated, of which 434 nodules were subsolid. At baseline, 304 nodules were classified as Lung-RADS category 2, with a malignancy rate of 3%, which is greater than the 1% in Lung-RADS (P = .004). The malignancy rate for GGNs smaller than 10 mm (two of 129, 1.3%) was smaller than that for GGNs measuring 10-19 mm (11 of 153, 6%) (P = .01). The malignancy rate for Lung-RADS category 3 was 14% (13 of 67), which is greater than the reported 2% in Lung-RADS (P < .001). The Brock model predicted malignancy better than Lung-RADS and the NELSON trial scheme (area under the receiver operating characteristic curve = 0.78, 0.70, and 0.67, respectively; P = .02 for Brock model vs NELSON trial scheme). Conclusion Subsolid nodules classified as Lung Imaging Reporting and Data System (Lung-RADS) categories 2 and 3 have a higher risk of malignancy than reported. The Brock risk calculator performed better than measurement-based classification schemes such as Lung-RADS. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Kauczor and von Stackelberg in this issue.