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

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.

Prognostic value of tumor volume doubling time in lung-metastatic adenoid cystic carcinoma

OBJECTIVES

Lung metastases in adenoid cystic carcinoma (ACC) usually have indolent growth and the optimal timing to start systemic therapy is not established. We assessed ACC lung metastasis tumor growth dynamics and compared the prognostic value of time to progression (TTP) and tumor volume doubling time (TVDT).

METHODS

The study included ACC patients with ≥1 pulmonary metastasis (≥5 mm) and at least 2 chest computed tomography scans. Radiology assessment was performed from the first scan showing metastasis until treatment initiation or death. Up to 5 lung nodules per patient were segmented for TVDT calculation. To assess tumor growth rate (TGR), the correlation coefficient (r) and coefficient of determination (R2) were calculated for measured lung nodules. TTP was assessed per RECIST 1.1; TVDT was calculated using the Schwartz formula. Overall survival was analyzed using the Kaplan-Meier method.

RESULTS

The study included 75 patients. Sixty-seven patients (89%) had lung-only metastasis on first CT scan. The TGR was overall constant (median R2 = 0.974). Median TTP and TVDT were 11.2 months and 7.5 months. Shorter TVDT (<6 months) was associated with poor overall survival (HR = 0.48; p = 0.037), but TTP was not associated with survival (HR = 1.02; p = 0.96). Cox regression showed that TVDT but not TTP significantly correlated with OS. TVDT calculated using estimated tumor volume correlated with TVDT obtained by segmentation.

CONCLUSION

Most ACC lung metastases have a constant TGR. TVDT may be a better prognostic indicator than TTP in lung-metastatic ACC. TVDT can be estimated by single longitudinal measurement in clinical practice.

Is 18F-fluorodeoxyglucose PET recommended for small lung nodules? CT findings of 18F-fluorodeoxyglucose non-avid lung cancer

OBJECTIVES

To determine the image characteristics associated with low 18F-FDG (18F-fluorodeoxyglucose) avidity among 8-15 mm solid lung cancer.

METHODS

Patients satisfying the following criteria were included: underwent surgery between January 2014 and December 2019 for lung cancer, presented 8-15 mm nodule without measurable ground glass component on preoperative CT, and underwent 18F-FDG PET before resection. Image characteristics, including air bronchogram, concave shape, pleural attachment, and background emphysema, were evaluated by two board-certified radiologists. The Mann-Whitney U test was used to compare maximum standardized uptake (SUVmax) values from 18F-FDG PET images.

RESULTS

The analysis included 235 patients. The SUVmax values of lesions with air bronchogram and concave shape were significantly lower than the SUVmax values of lesions without these features (median: 1.55 vs 2.56 and 1.66 vs 2.45, both P < .001), whereas lesions arising from emphysematous lungs had significantly higher SUVmax values than lesions arising from non-emphysematous lungs (2.90 vs 1.69, P < .001). No significant differences were detected between lesions attached and not attached to pleura. The interobserver agreement was almost perfect for air bronchograms and background emphysema (κ = 0.882 and 0.927, respectively), and 89.7% of lesions with air bronchograms and arising from non-emphysematous lungs showed SUVmax values below 2.5.

CONCLUSIONS

Among 8-15 mm solid lung cancer, the presence of air bronchograms and concave shape and the absence of background emphysema were associated with low 18F-FDG accumulation.

ADVANCES IN KNOWLEDGE

18F-FDG PET can be misleading in differentiating certain type of small solid lung cancer.

A simple nomogram for predicting occult lymph node metastasis of non-small cell lung cancer from preoperative computed tomography findings, including the volume-doubling time

PURPOSE

Latent lymph node metastasis is a clinical concern in the surgical treatment of non-small cell lung cancer (NSCLC). The present study identified a simple tool, including the volume-doubling time (VDT), for evaluating the risk of nodal metastasis.

METHODS

We reviewed, retrospectively, 560 patients who underwent radical resection for cN0M0 NSCLC. The whole tumor VDT and solid component VDT (SVDT) for differentiating the histological type and adenocarcinoma subtype were analyzed and a nomogram was constructed using variables selected through a stepwise selection method. The model was assessed through a calibration curve and decision curve analysis (DCA).

RESULTS

Lymph node metastases were detected in 89 patients (15.9%). The SVDT tended to be longer in patients with adenocarcinoma (294.5 days, p < 0.0001) than in those with other histological types of NSCLC, but was shorter when the solid/micropapillary component was predominant (127.0 days, p < 0.0001). The selected variables (tumor location, solid component diameter, consolidation tumor ratio, SVDT, and carcinoembryonic antigen) demonstrated significant differences and were used for the nomogram. The calibration curve indicated consistency, and the DCA showed validity across most threshold ranges from 0 to 68%.

CONCLUSIONS

The established nomogram is a useful tool for the preoperative prediction of lymph node metastasis, and the SVDT was the most influential factor in the nomogram.

Solitary lung adenocarcinoma: follow-up CT, pathological-molecular characteristics, and surgical prognosis for different morphological classifications

OBJECTIVE

To investigate the dynamic changes during follow-up computed tomography (CT), histological subtypes, gene mutation status, and surgical prognosis for different morphological presentations of solitary lung adenocarcinomas (SLADC).

MATERIALS AND METHODS

This retrospective study compared dynamic tumor changes and volume doubling time (VDT) in 228 patients with SLADC (morphological types I-IV) who had intermittent growth during follow-ups. The correlation between the morphological classification and histological subtypes, gene mutation status, and surgical prognosis was evaluated.

RESULTS

Among the 228 patients, 66 (28.9%) were classified as type I, 123 (53.9%) as type II, 16 (7%) as type III, and 23 (10.1%) as type IV. Type I had the shortest VDT (254 days), followed by types IV (381 days) and III (501 days), and then type II (993 days) (p < 0.05 each). Type I had a greater proportion of solid/micropapillary-predominant pattern than type II, and the lepidic-predominant pattern was more common in type II and III than in type I (p < 0.05 each). Furthermore, type II and IV SLADCs were correlated with positive epidermal growth factor receptor mutation (p < 0.05 each). Lastly, the Kaplan-Meier curves showed that the disease-free survival was longest for patients with type II tumors, followed by those with type III and IV tumors, and then those with type I tumors (p < 0.001 each).

CONCLUSION

A good understanding of the natural progression and pathological-molecular characteristics of different morphological SLADC types can help make accurate diagnoses, develop individual treatment strategies, and predict patient outcomes.

CRITICAL RELEVANCE STATEMENT

A good understanding of the natural progression and pathological-molecular characteristics of different morphological solitary lung adenocarcinoma types can help make accurate diagnoses, develop individual treatment strategies, and predict patient outcomes.

KEY POINTS

• Type I-IV solitary lung adenocarcinomas exhibit varying natural progression on serial CT scans. • Morphological classification of solitary lung adenocarcinomas predicts histological subtype, gene status, and surgical prognosis. • This classification of solitary lung adenocarcinomas may help improve diagnostic, therapeutic, and prognosticating abilities.

A clinical spectrum of resectable lung adenocarcinoma with micropapillary component (MPC) concurrently presenting as mixed ground-glass opacity nodules

BACKGROUND

In clinical practice, preoperative identification of mixed ground-glass opacity (mGGO) nodules with micropapillary component (MPC) to facilitate the implementation of individualized therapeutic strategies and avoid unnecessary surgery is increasingly importantOBJECTIVE: This study aimed to build a predictive model based on clinical and radiological variables for the early identification of MPC in lung adenocarcinoma presenting as mGGO nodules.

METHODS

The enrolled 741 lung adenocarcinoma patients were randomly divided into a training cohort and a validation cohort (3:1 ratio). The pathological specimens and preoperative images of malignant mGGO nodules from the study subjects were retrospectively reviewed. Furthermore, in the training cohort, selected clinical and radiological variables were utilized to construct a predictive model for MPC prediction.

RESULTS

The MPC was found in 228 (43.3%) patients in the training cohort and 72 (41.1%) patients in the validation cohort. Based on the predictive nomogram, the air bronchogram was defined as the most dominant independent risk factor for MPC of mGGO nodules, followed by the maximum computed tomography (CT) value (> 200), adjacent to pleura, gender (male), and vacuolar sign. The nomogram demonstrated good discriminative ability with a C-index of 0.783 (95%[CI] 0.744-0.822) in the training cohort and a C-index of 0.799 (95%[CI] 0.732-0.866) in the validation cohort Additionally, by using the bootstrapping method, this predictive model calculated a corrected AUC of 0.774 (95% CI: 0.770-0.779) in the training cohort.

CONCLUSIONS

This study proposed a predictive model for preoperative identification of MPC in known lung adenocarcinomas presenting as mGGO nodules to facilitate individualized therapy. This nomogram model needs to be further externally validated by subsequent multicenter studies.

Prediction of subsolid pulmonary nodule growth rate using radiomics

BACKGROUND

Pulmonary nodule growth rate assessment is critical in the management of subsolid pulmonary nodules (SSNs) during clinical follow-up. The present study aimed to develop a model to predict the growth rate of SSNs.

METHODS

A total of 273 growing SSNs with clinical information and 857 computed tomography (CT) scans were retrospectively analyzed. The images were randomly divided into training and validation sets. All images were categorized into fast-growth (volume doubling time (VDT) ≤ 400 days) and slow-growth (VDT > 400 days) groups. Models for predicting the growth rate of SSNs were developed using radiomics and clinical features. The models' performance was evaluated using the area under the curve (AUC) values for the receiver operating characteristic curve.

RESULTS

The fast- and slow-growth groups included 108 and 749 scans, respectively, and 10 radiomics features and three radiographic features (nodule density, presence of spiculation, and presence of vascular changes) were selected to predict the growth rate of SSNs. The nomogram integrating radiomics and radiographic features (AUC = 0.928 and AUC = 0.905, respectively) performed better than the radiographic (AUC = 0.668 and AUC = 0.689, respectively) and radiomics (AUC = 0.888 and AUC = 0.816, respectively) models alone in both the training and validation sets.

CONCLUSION

The nomogram model developed by combining radiomics with radiographic features can predict the growth rate of SSNs more accurately than traditional radiographic models. It can also optimize clinical treatment decisions for patients with SSNs and improve their long-term management.

Sublobar resection in non-small cell lung cancer: patient selection criteria and risk factors for recurrence

OBJECTIVE

To validate selection criteria for sublobar resection in patients with lung cancer with respect to recurrence, and to investigate predictors for recurrence in patients for whom the criteria are not suitable.

METHODS

Patients who underwent sublobar resection for lung cancer between July 2010 and December 2018 were retrospectively included. The criteria for curative sublobar resection were consolidation-to-tumor ratio ≤0.50 and size ≤3.0 cm in tumors with a ground-glass opacity (GGO) component (GGO group), and size of ≤2.0 cm and volume doubling time ≥400 days in solid tumors (solid group). Cox regression was used to identify predictors for time-to-recurrence (TTR) in tumors outside of these criteria (non-curative group).

RESULTS

Out of 530 patients, 353 were classified into the GGO group and 177 into the solid group. In the GGO group, the 2-year recurrence rates in curative and non-curative groups were 2.1 and 7.7%, respectively (p = 0.054). In the solid group, the 2-year recurrence rates in curative and non-curative groups were 0.0 and 28.6%, respectively (p = 0.03). Predictors of 2-year TTR after non-curative sublobar resection were pathological nodal metastasis (hazard ratio [HR], 6.63; p = 0.02) and lymphovascular invasion (LVI; HR, 3.28; p = 0.03) in the GGO group, and LVI (HR, 4.37; p < 0.001) and fibrosis (HR, 3.18; p = 0.006) in the solid group.

CONCLUSION

The current patient selection criteria for sublobar resection are satisfactory. LVI was a predictor for recurrence after non-curative resection.

ADVANCES IN KNOWLEDGE

This result supports selection criteria of patients for sublobar resection. LVI may help predict recurrence after non-curative sublobar resection.

Prevalence of subclinical lung cancer detected at autopsy: a systematic review

BACKGROUND

Lung cancer screening in high-risk populations with low-dose computed tomography is supported by international associations and recommendations. Overdiagnosis is considered a risk of screening with associated harms. The aim of this paper is to determine the prevalence of subclinical lung cancer diagnosed post-mortem to better understand the reservoir of subclinical lung cancer.

METHODS

We searched EMBASE, PubMed, and MEDLINE databases from inception until March 2022 with no language restrictions. We considered all studies with ≥100 autopsies in adults. Two reviewers independently assessed eligibility of studies, extracted data, and assessed risk of bias of included studies. We performed a meta-analysis using a random-effects model for prevalence of subclinical lung cancer diagnosed post-mortem with sensitivity and subgroup analyses.

RESULTS

A total of 13 studies with 16 730 autopsies were included. Pooled prevalence was 0.4% (95% CI 0.20 to 0.82%, I2 = 84%, tau2 = 1.19, low certainty evidence,16 730 autopsies). We performed a sensitivity analysis excluding studies which did not specify exclusion of children in their cohort, with a pooled prevalence of subclinical lung cancer of 0.87% (95% CI 0.48 to 1.57%, I2 = 71%, tau2 = 0.38, 6998 autopsies, 8 studies).

CONCLUSIONS

This is the first published systematic review to evaluate the prevalence of post-mortem subclinical lung cancer. Compared to autopsy systematic reviews in breast, prostate and thyroid cancers, the pooled prevalence is lower in lung cancer for subclinical cancer. This result should be interpreted with caution due to the included studies risk of bias and heterogeneity, with further high-quality studies required in target screening populations.

Prediction of Surgical Outcome by Tumor Volume Doubling Time via Stereo Imaging Software in Early Non-Small Cell Lung Cancer

BACKGROUND

Volume doubling time (VDT) has been proven to be a powerful predictor of lung cancer progression. In non-small cell lung cancer patients receiving sublobar resection, the discussion of correlation between VDT and surgery was absent. We proposed to investigate the surgical outcomes according to VDT.

METHODS

We retrospectively studied 96 cases post sublobar resection from 2012 to 2018, collecting two chest CT scans preoperatively of each case and calculating the VDT. The receiver operating characteristic curve was constructed to identify the optimal cut-off point of VDTs as 133 days. We divided patients into two groups: VDT < 133 days and VDT ≥ 133 days. Univariable and multivariable analyses were performed for comparative purposes.

RESULTS

Univariable and multivariable analyses revealed that the consolidation and tumor diameter ratio was the factor of overall survival (OS), and VDT was the only factor of disease-free survival (DFS). The five year OS rates of patients with VDTs ≥ 133 days and VDTs < 133 days, respectively, were 89.9% and 71.9%, and the five year DFS rates were 95.9% and 61.5%.

CONCLUSION

As VDT serves as a powerful prognostic predictor and provides an essential role in planning surgical procedures, the evaluation of VDT preoperatively is highly suggested.

Imaging of lung cancer

Lung cancer is the leading cause of cancer-related mortality globally. Imaging is essential in the screening, diagnosis, staging, response assessment, and surveillance of patients with lung cancer. Subtypes of lung cancer can have distinguishing imaging appearances. The most frequently used imaging modalities include chest radiography, computed tomography, magnetic resonance imaging, and positron emission tomography. Artificial intelligence algorithms and radiomics are emerging technologies with potential applications in lung cancer imaging.

Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case-control study

Objective

The purpose of this study is to establish model for assessing inert nodules predicting nodule volume-doubling.

Methods

A total of 201 patients with T1 lung adenocarcinoma were analysed retrospectively pulmonary nodule information was predicted by an AI pulmonary nodule auxiliary diagnosis system. The nodules were classified into two groups: inert nodules (volume-doubling time (VDT)>600 days n=152) noninert nodules (VDT<600 days n=49). Then taking the clinical imaging features obtained at the first examination as predictive variables the inert nodule judgement model <sn</sn>>(INM) volume-doubling time estimation model (VDTM) were constructed based on a deep learning-based neural network. The performance of the INM was evaluated by the area under the curve (AUC) obtained from receiver operating characteristic (ROC) analysis the performance of the VDTM was evaluated by R²(determination coefficient).

Results

The accuracy of the INM in the training and testing cohorts was 81.13% and 77.50%, respectively. The AUC of the INM in the training and testing cohorts was 0.7707 (95% CI 0.6779-0.8636) and 0.7700 (95% CI 0.5988-0.9412), respectively. The INM was effective in identifying inert pulmonary nodules; additionally, the R2 of the VDTM in the training cohort was 0.8008, and that in the testing cohort was 0.6268. The VDTM showed moderate performance in estimating the VDT, which can provide some reference during a patients' first examination and consultation.

Conclusion

The INM and the VDTM based on deep learning can help radiologists and clinicians distinguish among inert nodules and predict the nodule volume-doubling time to accurately treat patients with pulmonary nodules.

Growth Rates of Pulmonary Carcinoid Tumors and Hamartomas

BACKGROUND

Pulmonary nodule growth is often measured by volume doubling time (VDT), which may guide management. Most malignant nodules have a VDT of 20 to 400 days, with longer VDTs typically observed in indolent nodules. We assessed the utility of VDT in differentiating pulmonary carcinoids and hamartomas.

METHODS

A review was performed from January 2012 to October 2021 to identify patients with pathologic diagnoses and at least 2 chest computed tomography scans obtained 6 or more months apart. Visualization software was used to segment nodules and calculate diameter and volume. Volume doubling time was calculated for scans with 1-mm slices. For the remainder, estimated nodule volume doubling time (eVDT) was calculated using nodule diameter. Volume doubling times/eVDTs were placed into growth categories: less than 400 days; 400-600 days; and more than 600 days.

RESULTS

Sixty nodules were identified, 35 carcinoids and 25 hamartomas. Carcinoids were larger than hamartomas (median diameter, 13.5 vs 11.5 mm; P = 0.05). For carcinoid tumors, median VDT (n = 15) was 1485 days, and median eVDT (n = 32) was 1309 days; for hamartomas, median VDT (n = 8) was 2040 days and median eVDT (n = 25) was 2253 days. Carcinoid tumor eVDT was significantly shorter than hamartomas ( P = 0.03). By growth category, 1 of 25 hamartomas and 5 of 35 carcinoids had eVDT less than 400 days and 24 of 25 hamartomas and 27 of 35 carcinoids had eVDT more than 600 days. Of 4 carcinoid tumors with metastases, 2 had eVDT less than 400 days and 2 had eVDT more than 600 days.

CONCLUSIONS

Growth rate was not a reliable differentiator of pulmonary hamartomas and carcinoids. Slow growing carcinoids can metastasize. Radiologists should be cautious when discontinuing computed tomography follow-up based on growth rates alone.

Radiological precursor lesions of lung squamous cell carcinoma: Early progression patterns and divergent volume doubling time between hilar and peripheral zones

OBJECTIVES

This study investigated the early progression patterns of lung squamous cell carcinoma (SqCC) on computed tomography (CT) images.

MATERIALS AND METHODS

In total, 65 patients with SqCC who underwent surgical resection and two CT scans separated by an interval of at least 6 months were enrolled. We categorized the findings of the initial and at-diagnosis CT images into five patterns as previously reported. The volume doubling time (VDT) was calculated for measurable lesions.

RESULTS

A single nodule pattern on CT images at-diagnosis was most common in 56 (86.2 %) patients, in line with practical clinical findings. However, the patterns were diverse in the initial images, with 28 (43.1 %) patients displaying atypical findings, including multiple nodules (3.1 %), endobronchial lesions (20.0 %), subsolid nodules (10.8 %), and cyst wall thickening (9.2 %). All endobronchial lesions were located in the central/middle zone of the lung field, whereas lesions presented as multiple nodules, subsolid nodules, and cyst wall thickening were predominantly observed in the peripheral zone. The differences in the developed zones were reflected in the median VDT, and the tumors with an initial endobronchial pattern had a significantly shorter VDT than those with a subsolid nodule pattern (median: 140 days vs 276 days, p < 0.001).

CONCLUSIONS

Lung SqCC initiated with various CT image patterns, although most tumors ultimately developed a single nodule pattern by diagnosis. The initial CT image patterns differed between the hilar and peripheral zones, suggesting a difference in the progression scheme, which was also supported by differences in VDT.

Impact of Duration of Diagnostic Workup on Prognosis for Early Lung Cancer

INTRODUCTION

Growth assessment for pulmonary nodules is an important diagnostic tool; however, the impact on prognosis due to time delay for follow-up diagnostic scans needs to be considered.

METHODS

Using the data between 2003 and 2019 from the International Early Lung Cancer Action Program, a prospective cohort study, we determined the size-specific, 10-year Kaplan-Meier lung cancer (LC) survival rates as surrogates for cure rates. We estimated the change in LC diameter after delays of 90, 180, and 365 days using three representative LC volume doubling times (VDTs) of 60 (fast), 120 (moderate), and 240 (slow). We then estimated the decrease in the LC cure rate resulting from time between computed tomography scans to assess for growth during the diagnostic workup.

RESULTS

Using a regression model of the 10-year LC survival rates on LC diameter, the estimated LC cure rate of a 4.0 mm LC with fast (60-d) VDT is 96.0% (95% confidence interval [CI]: 95.2%-96.7%) initially, but it would decrease to 94.3% (95% CI: 93.2%-95.0%), 92.0% (95% CI: 90.5%-93.4%), and 83.6%(95% CI: 80.6%-86.6%) after delays of 90, 180, and 365 days, respectively. A 20.0-mm LC with the same VDTs has a lower LC cure rate of 79.9% (95% CI: 76.2%-83.5%) initially and decreases more rapidly to 71.5% (95% CI: 66.4%-76.7%), 59.8% (95% CI: 52.4%-67.1%), and 17.9% (95% CI: 3.0%-32.8%) after the same delays of 90, 180, and 365 days, respectively.

CONCLUSIONS

Time between scans required to measure growth of lung nodules affects prognosis with the effect being greater for fast growing and larger cancers. Quantifying the extent of change in prognosis is required to understand efficiencies of different management protocols.

Determination of the optimum definition of growth evaluation for indeterminate pulmonary nodules detected in lung cancer screening

Objective

To determine the optimum definition of growth for indeterminate pulmonary nodules detected in lung cancer screening.

Materials and methods

Individuals with indeterminate nodules as defined by volume of 50–500 mm3 (solid nodules) and solid component volume of 50–500 mm3 or average diameter of non-solid component ≥8 mm (part-solid nodules) on baseline lung cancer screening low-dose chest CT (LDCT) were included. The average diameters and volumes of the nodules were measured on baseline and follow-up LDCTs with semi-automated segmentation. Sensitivities and specificities for lung cancer diagnosis of nodule growth defined by a) percentage volume growth ≥25% (defined in the NELSON study); b) absolute diameter growth >1.5 mm (defined in the Lung-RADS version 1.1); and c) subjective decision by a radiologist were evaluated. Sensitivities and specificities of diagnostic referral based on various thresholds of volume doubling time (VDT) were also evaluated.

Results

Altogether, 115 nodules (one nodule per individual; 93 solid and 22 part-solid nodules; 105 men; median age, 68 years) were evaluated (median follow-up interval: 201 days; interquartile range: 127–371 days). Percentage volume growth ≥25% exhibited higher sensitivity but lower specificity than those of diametrical measurement compared to absolute diameter growth >1.5 mm (sensitivity, 69.2% vs. 42.3%, p = 0.023; specificity, 82.0% vs. 96.6%, p = 0.002). The radiologist had an equivalent sensitivity (53.9%; p = 0.289) but higher specificity (98.9%; p = 0.002) compared to those of volume growth, but did not differ from those of diameter growth (p>0.05 both in sensitivity and specificity). Compared to the VDT threshold of 600 days (sensitivity, 61.5%; specificity, 87.6%), VDT thresholds ≤200 and ≤300 days exhibited significantly lower sensitivity (30.8%, p = 0.013) and higher specificity (94.4%, p = 0.041), respectively.

Conclusion

Growth evaluation of screening-detected indeterminate nodules with volumetric measurement exhibited higher sensitivity but lower specificity compared to diametric measurements.

Progressive Massive Fibrosis Mimicking Lung Cancer: Two Case Reports with Potentially Useful CT Features for Differential Diagnosis

Progressive massive fibrosis (PMF) with atypical findings is often misdiagnosed as lung cancer. Atypical features of PMF have been described in some reports; however, these reports only introduced their cases with a short literature review. We report two cases of solitary PMFs with no underlying simple pneumoconiosis or rapid growth at atypical location that were mistaken for lung cancer. We also suggest the useful CT findings to aid in the differential diagnosis.

Aggressive histological component in subsolid lung adenocarcinoma: priority for resection without delay

INTRODUCTION

This study explored the predictors of a histological aggressive component in ground glass opacity-containing lung adenocarcinoma.

METHODS

Of the 2388 patients who underwent resection for lung cancer at our institute between 2017 and 2020, we collected data on the 501 patients with ground glass opacity-containing adenocarcinoma with a total diameter of ≤2 cm. Using a historical cohort, we identified histological aggressive components that were related to a poor prognosis in early-stage adenocarcinoma. A multivariable analysis was conducted to identify predictors for the presence of a histological aggressive component.

RESULTS

Lymphovascular invasion and predominant micropapillary or solid patterns were identified as histological aggressive components by a prognostic analysis using a historical cohort. Of the 501 patients included, 36 (7.2%) had at least one histological aggressive component. A multivariate analysis showed that a consolidation/tumour ratio > 0.5 (P < 0.01), maximum standardized uptake value on positron emission tomography ≥1.5 (P = 0.01) and smoking index >20 pack-years (P = 0.01) were predictors of the presence of a histological aggressive component. A total of 98% of cases without any of the above factors did not have a histological aggressive component.

CONCLUSIONS

Approximately 7% of ground glass opacity-containing small adenocarcinomas contained histological aggressive component. A consolidation/tumour ratio > 0.5, maximum standardized uptake value ≥ 1.5 and smoking index >20 pack-years were predictors for such cases. These predictors may be useful for screening patients with a potentially high risk of a poor prognosis and for prioritizing resection without delay.

ViSTA: A Novel Network Improving Lung Adenocarcinoma Invasiveness Prediction from Follow-Up CT Series

Simple Summary

Assessing follow-up computed tomography(CT) series is of great importance in clinical practice for lung nodule diagnosis. Deep learning is a thriving data mining method in medical imaging and has obtained surprising results. However, previous studies mostly focused on the analysis of single static time points instead of the entire follow-up series and required regular intervals between CT examinations. In the current study, we propose a new deep learning framework, named ViSTA, that can better evaluate tumor invasiveness using irregularly serial follow-up CT images to avoid aggressive procedures or delay diagnosis in clinical practice. ViSTA provides a new solution for irregularly sampled data. ViSTA delivers superior performance compared with other static or serial deep learning models. The proposed ViSTA framework is capable of improving performance close to the human level in the prediction of invasiveness of lung adenocarcinoma while being transferrable to other tasks analyzing serial medical data.

Abstract

To investigate the value of the deep learning method in predicting the invasiveness of early lung adenocarcinoma based on irregularly sampled follow-up computed tomography (CT) scans. In total, 351 nodules were enrolled in the study. A new deep learning network based on temporal attention, named Visual Simple Temporal Attention (ViSTA), was proposed to process irregularly sampled follow-up CT scans. We conducted substantial experiments to investigate the supplemental value in predicting the invasiveness using serial CTs. A test set composed of 69 lung nodules was reviewed by three radiologists. The performance of the model and radiologists were compared and analyzed. We also performed a visual investigation to explore the inherent growth pattern of the early adenocarcinomas. Among counterpart models, ViSTA showed the best performance (AUC: 86.4% vs. 60.6%, 75.9%, 66.9%, 73.9%, 76.5%, 78.3%). ViSTA also outperformed the model based on Volume Doubling Time (AUC: 60.6%). ViSTA scored higher than two junior radiologists (accuracy of 81.2% vs. 75.4% and 71.0%) and came close to the senior radiologist (85.5%). Our proposed model using irregularly sampled follow-up CT scans achieved promising accuracy in evaluating the invasiveness of the early stage lung adenocarcinoma. Its performance is comparable with senior experts and better than junior experts and traditional deep learning models. With further validation, it can potentially be applied in clinical practice.

Outcomes of patients with different lepidic percentage and tumor size of stage I lung adenocarcinoma

Background

To evaluate the long‐term outcomes after surgical resection for stage I lung adenocarcinoma based on the percentage of lepidic component (LC) and invasive tumor size (IS).

Methods

The clinicopathological characteristics of 1049 patients with stage I lung adenocarcinoma who underwent surgery between 2006 and 2016 were retrospectively reviewed. Tumors were categorized into groups: A (LC ≥ 50%) and B (LC < 50%). Groups A0 and A1 consisted of minimally invasive adenocarcinomas (MIA) and other lepidic‐predominant invasive adenocarcinomas, respectively. Group B was categorized into B1 (IS ≤ 1 cm), B2 (1 < IS≤2 cm), and B3 (2 < IS≤3 cm) by invasive tumor size and divided into subgroups (B1[lep+]/[lep−], B2[lep+]/[lep−], and B3[lep+]/[lep−]) according to the presence[lep+] or absence[lep−] of LCs. Cumulative incidence of recurrence (CIR) and cancer‐specific survival (CSS) were examined.

Results

LC decreased with increasing IS. Only 24 (8.5%) tumors in group A had an IS >1 cm. 10‐year CIR and CSS were 15.2% and 86.0%. LC and IS were found to be independent predictors of CSS. Patients in group A had 1.4% 10‐year CIR and 100% 10‐year CSS. In group B, a significantly higher CIR and worse CSS were observed as IS increased (p < 0.001), but LC was not a predictor for CSS (p = 0.593). No significant differences in CIR or CSS were found in presence of LC or not when LC < 50% (B1[lep+]/[lep−], B2[lep+]/[lep−], and B3[lep+]/[lep−]: p = 0.36/0.48, p = 0.82/0.94, and p = 0.90/0.37, respectively).

Conclusions

LC≥50% tumors demonstrated excellent prognosis regardless of IS. The outcomes of LC < 50% tumors were well predicted by IS, corresponding to the T‐staging system. The predictive value of LC for prognosis became insignificant.

Prognostic and predictive value of the newly proposed grading system of invasive pulmonary adenocarcinoma in Chinese patients: a retrospective multicohort study

Our aim was to validate and analyze the prognostic impact of the novel International Association for the Study of Lung Cancer (IASLC) Pathology Committee grading system for invasive pulmonary adenocarcinomas (IPAs) in Chinese patients and to evaluate its utility in predicting a survival benefit from adjuvant chemotherapy (ACT). In this multicenter, retrospective, cohort study, we included 926 Chinese patients with completely resected stage I IPAs and classified them into three groups (Grade 1, n = 119; Grade 2, n = 431; Grade 3, n = 376) according to the new grading system proposed by the IASLC. Recurrence-free survival (RFS) and overall survival (OS) were estimated by the Kaplan-Meier method, and prognostic factors were assessed using univariable and multivariable Cox proportional hazards models. All included cohorts were well stratified in terms of RFS and OS by the novel grading system. Furthermore, the proposed grading system was found to be independently associated with recurrence and death in the multivariable analysis. Among patients with stage IB IPA (N = 490), the proposed grading system identified patients who could benefit from ACT but who were undergraded by the adenocarcinoma (ADC) classification. The novel grading system not only demonstrated prognostic significance in stage I IPA in a multicenter Chinese cohort but also offered clinical value for directing therapeutic decisions regarding adjuvant chemotherapy.

Volume doubling time and radiomic features predict tumor behavior of screen-detected lung cancers

BACKGROUND: Image-based biomarkers could have translational implications by characterizing tumor behavior of lung cancers diagnosed during lung cancer screening. In this study, peritumoral and intratumoral radiomics and volume doubling time (VDT) were used to identify high-risk subsets of lung patients diagnosed in lung cancer screening that are associated with poor survival outcomes. METHODS: Data and images were acquired from the National Lung Screening Trial. VDT was calculated between two consequent screening intervals approximately 1 year apart; peritumoral and intratumoral radiomics were extracted from the baseline screen. Overall survival (OS) was the main endpoint. Classification and Regression Tree analyses identified the most predictive covariates to classify patient outcomes. RESULTS: Decision tree analysis stratified patients into three risk-groups (low, intermediate, and high) based on VDT and one radiomic feature (compactness). High-risk patients had extremely poor survival outcomes (hazard ratio [HR] = 8.15; 25% 5-year OS) versus low-risk patients (HR = 1.00; 83.3% 5-year OS). Among early-stage lung cancers, high-risk patients had poor survival outcomes (HR = 9.07; 44.4% 5-year OS) versus the low-risk group (HR = 1.00; 90.9% 5-year OS). For VDT, the decision tree analysis identified a novel cut-point of 279 days and using this cut-point VDT alone discriminated between aggressive (HR = 4.18; 45% 5-year OS) versus indolent/low-risk cancers (HR = 1.00; 82.8% 5-year OS). CONCLUSION: We utilized peritumoral and intratumoral radiomic features and VDT to generate a model that identify a high-risk group of screen-detected lung cancers associated with poor survival outcomes. These vulnerable subset of screen-detected lung cancers may be candidates for more aggressive surveillance/follow-up and treatment, such as adjuvant therapy.

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.

Application of Simultaneous 18 F-FDG PET With Monoexponential, Biexponential, and Stretched Exponential Model-Based Diffusion-Weighted MR Imaging in Assessing the Proliferation Status of Lung Adenocarcinoma

BACKGROUND

Ki-67 proliferation index (PI) is important for providing information on tumor behavior, treatment response, and prognosis. Integrated positron emission tomography/magnetic resonance (PET/MR) may have the potential to assess Ki-67 PI in patients with lung adenocarcinoma.

PURPOSE

To explore the value of simultaneous ¹⁸ F-fluorodeoxyglucose (¹⁸ F-FDG) PET/MR-derived parameters in assessing the proliferation status of lung adenocarcinoma and to determine the best combination of parameters.

STUDY TYPE

Prospective.

POPULATION

Seventy-eight patients with lung adenocarcinoma and with Ki-67 PI.

FIELD STRENGTH/SEQUENCE

3.0 T, simultaneous PET/MRI including diffusion-weighted imaging (DWI) and ¹⁸ F-FDG PET.

ASSESSMENT

DWI-derived parameters, namely, apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f), diffusion heterogeneity index (α), and distributed diffusion coefficient (DDC); and PET-derived parameters, namely, maximum standardized uptake value (SUV_max ), metabolic tumor volume (MTV), and total lesion glycolytic volume (TLG), were calculated and compared between the high (>25%) and low (≤25%) Ki-67 PI groups. The correlations between PET-derived parameters and DWI-derived parameters were analyzed.

STATISTICAL TESTS

Student's t-test, Mann-Whitney U test, chi-square test, and receiver operating characteristic (ROC) curves. A P-value <0.05 was considered statistically significant.

RESULTS

The SUV_max , MTV, TLG, ADC, D, and DDC values were significantly different between the high (N = 35) and low Ki-67 PI groups (N = 43). D, SUV_max , and MTV independently predicted the Ki-67 PI status. The combination of D, SUV_max , and MTV had the largest area under the ROC curve (AUC = 0.900), which was significantly larger than the AUC alone of DDC (AUC = 0.725), SUV_max (AUC = 0.815), MTV (AUC = 0.774), or TLG (AUC = 0.783). The perfusion fraction did not correlate with SUV_max , MTV, or TLG (r = -0.03, -0.11, and -0.04, respectively; P = 0.786, 0.348, and 0.733).

DATA CONCLUSION

The combination of D, SUV_max , and MTV may predict Ki-67 PI status. No correlation was observed between perfusion parameters and metabolic parameters.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: Stage 2.

Volume Doubling Times of Pulmonary Metastases in Patients With Bone and Soft Tissue Sarcomas: Associations With Subsequent New Metastases and Survival After Metastasectomy

Background: Pulmonary metastases of bone and soft tissue sarcoma are common and have a high recurrence rate after metastatectomy. Factors associated with post-metastatectomy recurrence are not well studied. Objective: To investigate the association of the volume doubling time (VDT) of pulmonary metastases with the subsequent development of new pulmonary nodules and survival after metastasectomy in patients with bone or soft tissue sarcoma. Methods: This retrospective study included patients with bone or soft tissue sarcoma who, between July 2020 and December 2020, underwent first complete metastasectomy of pulmonary nodules visualized on two sequential preoperative CT scans. Semi-automatic volumetric segmentation of the pulmonary metastases was performed on the two CT scans, and VDTs were calculated. VDT was compared between patients with and without subsequent new metastases after metastatectomy. Cox proportional hazards regression analyses were performed to determine risk factors for recurrence-free survival (RFS) after metastasectomy and for post-metastasectomy overall survival (OS). Results: Forty patients (21 women, 19 men; mean age, 51.1±14.3 years) were included. Of these patients, 23 (57.5%) developed new metastatic nodules after metastasectomy, and 10 (25%) died during follow-up. Median VDT was shorter in patients with, versus without, new metastases after metastasectomy (56 vs. 140 days, p=.002). Only four of 23 patients with new metastases had VDT ≥140 days. In multivariable analysis, older age [hazard ratio (HR), 1.06; p=.004), female sex (HR, 2.80; p=.03), and VDT <140 days (hazard ratio [HR], 4.22; p=.01) were independent predictors of worse RFS. Also in multivariable analysis, only older age (HR, 1.17; p=.005) and VDT <50 days (HR, 8.60; p=.02) were independent predictors of worse OS. OS was significantly worse in patients with VDT <140 days (10 deaths among 27 patients) than in patients with VDT ≥140 days (no deaths in 13 patients) (p=.01). Conclusions: In patients with bone and soft tissue sarcoma, shorter VDT of pulmonary metastases is independently associated with subsequent new metastases after metastasectomy and worse OS. Clinical Impact: VDT of pulmonary nodules may be considered in patient selection for pulmonary metastasectomy and in postoperative patient management.

Micropapillary Predominance Is a Risk Factor for Brain Metastasis in Resected Lung Adenocarcinoma

BACKGROUND

Histologic subtyping offers some prognostic value in lung adenocarcinoma. We thus hypothesized that histologic subtypes may be useful for risk stratification of brain metastasis (BM). In this study, we aimed to investigate the impact of histologic subtypes on the risk for BM in patients with resected lung adenocarcinoma.

PATIENTS AND METHODS

Of 1099 consecutive patients who had undergone curative-intent surgery (2000-2014), 448 patients who had undergone complete resection for lung adenocarcinoma were included in this study. Correlated clinical variables and BM-free survival were analyzed.

RESULTS

Micropapillary predominance was significantly associated with higher risk of BM after complete resection in univariate analyses (P < .001). In addition, multivariate analyses showed that micropapillary predominance was an independent risk factor for BM (hazard ratio = 2.727; 95% confidence interval, 1.260-5.900; P = .011), along with younger age and advanced pathologic stage. Unlike the other subtypes, an increase in the percentage of the micropapillary subtype was positively correlated with an increase in BM frequency. Patients with micropapillary adenocarcinoma showed significantly poorer brain metastasis-free survival compared with those with non-micropapillary adenocarcinoma (3 years, 78.2% vs. 95.6%; 5 years, 67.3% vs. 94.3%; P < .001).

CONCLUSION

The current study demonstrated a significant correlation between micropapillary subtype and higher risk of BM in patients with resected lung adenocarcinoma. This routine histologic evaluation of resected adenocarcinoma may provide useful information for the clinician when considering postoperative management in patients with lung adenocarcinoma. Histologic subtyping offer some prognostic value in lung adenocarcinoma. Because brain metastasis is critical and often refractory to systemic chemotherapy, early detection is clinically important to achieve effective local treatment. We retrospectively analyzed the association between histologic subtypes and occurrence of brain metastasis and found a significant association between micropapillary predominance and higher risk for brain metastasis. Our findings may be relevant when considering postoperative management.

Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics

Objectives

To investigate the value of imaging in predicting the growth rate of early lung adenocarcinoma.

Methods

From January 2012 to June 2018, 402 patients with pathology-confirmed lung adenocarcinoma who had two or more thin-layer CT follow-up images were retrospectively analyzed, involving 407 nodules. Two complete preoperative CT images and complete clinical data were evaluated. Training and validation sets were randomly assigned according to an 8:2 ratio. All cases were divided into fast-growing and slow-growing groups. Researchers extracted 1218 radiomics features from each volumetric region of interest (VOI). Then, radiomics features were selected by repeatability analysis and Analysis of Variance (ANOVA); Based on the Univariate and multivariate analyses, the significant radiographic features is selected in training set. A decision tree algorithm was conducted to establish the radiographic model, radiomics model and the combined radiographic-radiomics model. Model performance was assessed by the area under the curve (AUC) obtained by receiver operating characteristic (ROC) analysis.

Results

Sixty-two radiomics features and one radiographic features were selected for predicting the growth rate of pulmonary nodules. The combined radiographic-radiomics model (AUC 0.78) performed better than the radiographic model (0.727) and the radiomics model (0.710) in the validation set.

Conclusions

The model has good clinical application value and development prospects to predict the growth rate of early lung adenocarcinoma through the combined radiographic-radiomics model.

Tumor cell proliferation (Ki-67) expression and its prognostic significance in histological subtypes of lung adenocarcinoma

OBJECTIVES

Ki-67 is a key molecular marker to indicate the proliferative activity of tumor cells in lung cancer. However, Ki-67 expression and its prognostic significance in histological subtypes of lung adenocarcinoma (LUAD) remain unclear.

MATERIALS AND METHODS

We retrospectively analyzed 1028 invasive LUAD patients who underwent surgery treatment between January 2012 and April 2020 in our department. Associations between Ki-67 expression and histological subtypes of LUAD, as well as other clinicopathological characteristics, were evaluated. The prognostic role of Ki-67 in LUAD subtypes was further assessed using log-rank test and univariate/multivariate Cox proportional hazards regression analyses.

RESULTS

Ki-67 expression differed across LUAD histological subtypes. The solid-predominant adenocarcinoma (SPA, 46.31 ± 24.72) had the highest expression level of Ki-67, followed by micropapillary (MPA, 31.71 ± 18.14), papillary (PPA, 22.09 ± 19.61), acinar (APA, 19.73 ± 18.71) and lepidic-predominant adenocarcinoma (LPA, 9.86 ± 8.10, P <  0.001). Tumors with solid or micropapillary components also had a higher Ki-67 expression than those without solid or micropapillary components. Besides, males, smokers, larger tumor size, lymph node metastasis and EGFR wild type were correlated with elevated Ki-67 expression. Univariate analysis indicated that increased Ki-67 expression and MPA/SPA subtypes were significantly associated with a poorer prognosis. Notably, the survival differences between LUAD subtypes vanished after adjusting for tumor size and Ki-67 expression in multivariate analysis, while Ki-67 was an independent prognostic factor of LUAD. Patients with MPA/SPA had non-inferior overall and disease-free survival than LPA/APA/PPA patients with a Ki-67 expression comparable to MPA/SPA subjects.

CONCLUSION

Ki-67 expression varied considerably according to the predominant histological subtypes of LUAD. Ki-67 expression level and tumor size contributed to the survival differences between LUAD histological subtypes.