The Prognostic Impact of Tumor Volume in Patients with Clinical Stage IA Non-Small Cell Lung Cancer

Prognostic and Predictive Biomarkers of Oligometastatic NSCLC: New Insights and Clinical Applications

This review discusses the current data on predictive and prognostic biomarkers in oligometastatic NSCLC and discusses whether biomarkers identified in other stages and widespread metastatic disease can be extrapolated to the oligometastatic disease (OMD) setting. Research is underway to explore the prognostic and predictive value of biological attributes of tumor tissue, circulating cells, the tumor microenvironment, and imaging findings as biomarkers of oligometastatic NSCLC. Biomarkers that help define true OMD and predict outcomes are needed for patient selection for oligometastatic treatment, and to avoid futile treatments in patients that will not benefit from locoregional treatment. Nevertheless, these biomarkers are still in the early stages of development and lack prospective validation in clinical trials. Furthermore, the absence of a clear definition of OMD contributes to a heterogeneous study population in which different types of OMD are mixed and treatment strategies are different. Multiple tissue-based, circulating, and imaging features are promising regarding their prognostic and predictive role in NSCLC, but data is still limited and might be biased owing to the inclusion of heterogeneous patient populations. Larger homogeneous and prospective series are needed to assess the prognostic and predictive role of these biomarkers. As obtaining tissue can be difficult and is invasive, the most promising tools for further evaluation are liquid biopsies and imaging-based biomarkers as these can also be used for longitudinal follow-up.

Leveraging the pleural space for anticancer therapies in pleural mesothelioma

Most patients with pleural mesothelioma (PM) present with symptomatic pleural effusion. In some patients, PM is only detectable on the pleural surfaces, providing a strong rationale for intrapleural anticancer therapy. In modern prospective studies involving expert radiological staging and specialist multidisciplinary teams, the population incidence of stage I PM (an approximate surrogate of pleura-only PM) is higher than in historical retrospective series. In this Viewpoint, we advocate for the expansion of intrapleural trials to serve these patients, given the paucity of data supporting licensed systemic therapies in this setting and the uncertainties involved in surgical therapy. We begin by reviewing the unique anatomical and physiological features of the PM-bearing pleural space, before critically appraising the evidence for systemic therapies in stage I PM and previous intrapleural PM trials. We conclude with a summary of key challenges and potential solutions, including optimal trial designs, repurposing of indwelling pleural catheters, and new technologies.

Prognostic impact of deep learning-based quantification in clinical stage 0-I lung adenocarcinoma

OBJECTIVES

To evaluate the performance of automatic deep learning (DL) algorithm for size, mass, and volume measurements in predicting prognosis of lung adenocarcinoma (LUAD) and compared with manual measurements.

METHODS

A total of 542 patients with clinical stage 0-I peripheral LUAD and with preoperative CT data of 1-mm slice thickness were included. Maximal solid size on axial image (MSSA) was evaluated by two chest radiologists. MSSA, volume of solid component (SV), and mass of solid component (SM) were evaluated by DL. Consolidation-to-tumor ratios (CTRs) were calculated. For ground glass nodules (GGNs), solid parts were extracted with different density level thresholds. The prognosis prediction efficacy of DL was compared with that of manual measurements. Multivariate Cox proportional hazards model was used to find independent risk factors.

RESULTS

The prognosis prediction efficacy of T-staging (TS) measured by radiologists was inferior to that of DL. For GGNs, MSSA-based CTR measured by radiologists (RMSSA%) could not stratify RFS and OS risk, whereas measured by DL using 0HU (2D-AIMSSA0HU%) could by using different cutoffs. SM and SV measured by DL using 0 HU (AISM0HU% and AISV0HU%) could effectively stratify the survival risk regardless of different cutoffs and were superior to 2D-AIMSSA0HU%. AISM0HU% and AISV0HU% were independent risk factors.

CONCLUSION

DL algorithm can replace human for more accurate T-staging of LUAD. For GGNs, 2D-AIMSSA0HU% could predict prognosis rather than RMSSA%. The prediction efficacy of AISM0HU% and AISV0HU% was more accurate than of 2D-AIMSSA0HU% and both were independent risk factors.

CLINICAL RELEVANCE STATEMENT

Deep learning algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma.

KEY POINTS

• Deep learning (DL) algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma (LUAD). • For GGNs, maximal solid size on axial image (MSSA)-based consolidation-to-tumor ratio (CTR) measured by DL using 0 HU could stratify survival risk than that measured by radiologists. • The prediction efficacy of mass- and volume-based CTRs measured by DL using 0 HU was more accurate than of MSSA-based CTR and both were independent risk factors.

Lack of incremental value of three-dimensional measurement in assessing invasiveness for lung cancer

OBJECTIVES

The aim of this study was to evaluate the performance of consolidation-to-tumour ratio (CTR) and the radiomic models in two- and three-dimensional modalities for assessing radiological invasiveness in early-stage lung adenocarcinoma.

METHODS

A retrospective analysis was conducted on patients with early-stage lung adenocarcinoma from Guangdong Provincial People's Hospital and Shenzhen People's Hospital. Manual delineation of pulmonary nodules along the boundary was performed on cross-sectional images to extract radiomic features. Clinicopathological characteristics and radiomic signatures were identified in both cohorts. CTR and radiomic score for every patient were calculated. The performance of CTR and radiomic models were tested and validated in the respective cohorts.

RESULTS

A total of 818 patients from Guangdong Provincial People's Hospital were included in the primary cohort, while 474 patients from Shenzhen People's Hospital constituted an independent validation cohort. Both CTR and radiomic score were identified as independent factors for predicting pathological invasiveness. CTR in two- and three-dimensional modalities exhibited comparable results with areas under the receiver operating characteristic curves and were demonstrated in the validation cohort (area under the curve: 0.807 vs 0.826, P = 0.059) Furthermore, both CTR in two- and three-dimensional modalities was able to stratify patients with significant relapse-free survival (P < 0.000 vs P < 0.000) and overall survival (P = 0.003 vs P = 0.001). The radiomic models in two- and three-dimensional modalities demonstrated favourable discrimination and calibration in independent cohorts (P = 0.189).

CONCLUSIONS

Three-dimensional measurement provides no additional clinical benefit compared to two-dimensional.

Ultra-high dose-rate proton FLASH improves tumor control

BACKGROUND AND PURPOSE

Proton radiotherapy (PRT) offers potential benefits over other radiation modalities, including photon and electron radiotherapy. Increasing the rate at which proton radiation is delivered may provide a therapeutic advantage. Here, we compared the efficacy of conventional proton therapy (CONVpr) to ultrahigh dose-rate proton therapy, FLASHpr, in a mouse model of non-small cell lung cancers (NSCLC).

MATERIALS AND METHODS

Mice bearing orthotopic lung tumors received thoracic radiation therapy using CONVpr (<0.05 Gy/s) and FLASHpr (>60 Gy/s) dose rates.

RESULTS

Compared to CONVpr, FLASHpr was more effective in reducing tumor burden and decreasing tumor cell proliferation. Furthermore, FLASHpr was more efficient in increasing the infiltration of cytotoxic CD8+ T-lymphocytes inside the tumor while simultaneously reducing the percentage of immunosuppressive regulatory T-cells (Tregs) among T-lymphocytes. Also, compared to CONVpr, FLASHpr was more effective in decreasing pro-tumorigenic M2-like macrophages in lung tumors, while increasing infiltration of anti-tumor M1-like macrophages. Finally, FLASHpr treatment reduced expression of checkpoint inhibitors in lung tumors, indicating reduced immune tolerance.

CONCLUSIONS

Our results suggest that FLASH dose-rate proton delivery modulates the immune system to improve tumor control and might thus be a promising new alternative to conventional dose rates for NSCLC treatment.

Tumor Volume is a Better Prognostic Factor than Greatest Tumor Diameter in operated Stage I-III Non-small Cell Lung Cancer

INTRODUCTION

The aim of this study was to investigate the prognostic impact of tumor volume (TV, recorded from surgical specimens) on patients with stage I-III non-small-cell lung cancer (NSCLC) after complete resection.

MATERIALS AND METHODS

A total of 129 patients with stage I-III NSCLC diagnosed and underwent curative resection from 2007 to 2014 in our center were included in the study. Their clinico-pathological factors were retrospectively reviewed. Overall survival (OS) and disease-free survival (DFS) analyses were performed with the Kaplan-Meier method and Cox's hazard model. According to the ROC analysis, patients were divided into 2 groups (Group 1: 58 patients <30.3 cm³ and Group 2: 71 patients ≥30.3 cm³) and the OS and DFS values were compared.

RESULTS

Median TVs and greatest tumor diameter were 12 cm³ (0.1-30) / 3 cm (0.4-6.5) in Group 1 and 98 cm³ (30.6-1521) / 6 cm (3.5-21) in Group 2. Median OS was 53 (5-177) months in Group 1 and 38 (2-200) months in Group 2 (P < .001). DFS was similar in both group (28 [1-140] vs. 24 [1-155] months, Introduction P = .489). Kaplan-Meier curves showed significantly higher OS rates in Group 1 than Group 2 (P = .04). In multivariable analysis (TV, tumor T stage, tumor N stage, receiving adjuvant radiotherapy) showed that TV (HR: 0.293, 95% CI: 0.121-0.707, P = .006) and tumor N stage (HR: 0.013, 95% CI: 0.001-0.191, P = .02) were independent factors associated with OS.

CONCLUSION

Tumor volume, not considered in the routine TNM classification, may improve prediction accuracy of overall OS in operated Stage I-III NSCLC.

Prostate Cancer Tumor Volume and Genomic Risk

Background

Despite the historic association of higher prostate cancer volume with worse oncologic outcomes, little is known about the impact of tumor volume on cancer biology.

Objective

To characterize the relationship between tumor volume (measured by percent positive cores [PPC]) and cancer biology (measured by Decipher genomic risk classifier [GC]) in men who underwent prostate biopsy.

Design setting and participants

Prostate biopsies from 52 272 men profiled with Decipher captured in a population-based prospective tumor registry were collected from 2016 to 2021.

Outcome measurements and statistical analysis

The degree of distribution and correlation of PPC with a GC score across grade group (GG) strata were examined using the Mann-Whitney U test, Pearson correlation coefficient, and multivariable linear regression controlled for clinicopathologic characteristics.

Results and limitations

A total of 38 921 (74%) biopsies passed quality control (14 331 GG1, 16 159 GG2, 5661 GG3, 1775 GG4, and 995 GG5). Median PPC and GC increased with sequentially higher GG. There was an increasingly positive correlation (r) between PPC and GC in GG2-5 prostate cancer (r [95% confidence interval {CI}]: 0.07 [0.5, 0.8] in GG2, 0.15 [0.12, 0.17] in GG3, 0.20 [0.15, 0.24] in GG4, and 0.25 [0.19, 0.31] in GG5), with no correlation in GG1 disease (r = 0.01, 95% CI [-0.001, 0.03]). In multivariable linear regression, GC was significantly associated with higher PPC for GG2-5 (all p < 0.05) but was not significantly associated with PPC for GG1. Limitations include retrospective design and a lack of final pathology from radical prostatectomy specimens.

Conclusions

Higher tumor volume was associated with worse GC for GG2-5 prostate cancer, whereas tumor volume was not associated with worse GC for GG1 disease. The finding that tumor volume is not associated with worse cancer biology in GG1 disease encourages active surveillance for most patients irrespective of tumor volume.

Patient summary

We studied the relationship between prostate cancer tumor volume and cancer biology, as measured by the Decipher genomic risk score, in men who underwent prostate biopsy. We found that tumor volume was not associated with worse cancer biology for low-grade prostate cancer. Our findings reassuringly support recent guidelines to recommend active surveillance for grade group 1 prostate cancer in most patients, irrespective of tumor volume.

Predictive validity in drug discovery: what it is, why it matters and how to improve it

Successful drug discovery is like finding oases of safety and efficacy in chemical and biological deserts. Screens in disease models, and other decision tools used in drug research and development (R&D), point towards oases when they score therapeutic candidates in a way that correlates with clinical utility in humans. Otherwise, they probably lead in the wrong direction. This line of thought can be quantified by using decision theory, in which 'predictive validity' is the correlation coefficient between the output of a decision tool and clinical utility across therapeutic candidates. Analyses based on this approach reveal that the detectability of good candidates is extremely sensitive to predictive validity, because the deserts are big and oases small. Both history and decision theory suggest that predictive validity is under-managed in drug R&D, not least because it is so hard to measure before projects succeed or fail later in the process. This article explains the influence of predictive validity on R&D productivity and discusses methods to evaluate and improve it, with the aim of supporting the application of more effective decision tools and catalysing investment in their creation.

Efficacy of a combined tool for stage I non-small cell lung cancer against lymph node metastasis

In patients with clinical stage I non-small cell lung cancer (NSCLC), the prediction of occult lymph node metastasis (LNM) based on a combination of morphology using high-resolution computed tomography (HRCT) and metabolism using positron emission tomography (PET)-CT is unknown. The present study evaluated the use of predictive radiological tools, chest CT and PET-CT, for occult LNM in patients with clinical stage I NSCLC. The records of patients who underwent lobectomy between July 2014 and November 2021 were retrospectively reviewed. The differences in clinicopathological parameters, including CT and PET, between the LNM and non-LNM groups were assessed. Pure solid tumor was defined as a consolidation-to-tumor ratio of 1. The optimal cut-off value for predictive radiological tools for LNM was assessed according to the area under the receiver operating characteristic (ROC) curve. The present study included 288 patients, of whom 39 (13.5%) had LNM; of these 38 (97.4%) were pure solid type. Larger consolidation size (CS), higher maximal standardized uptake (SUVmax) value and histological type were statistically associated with LNM (all P<0.05). The optimal cutoff values of CS and SUVmax for predicting LNM were 19 mm and 5.5 respectively, as assessed using the area under the ROC curve. The combination of CS ≥19 mm and SUVmax ≥5.5 demonstrated a markedly higher odds ratio (9.184; 95% CI, 4.345-19.407) than each parameter individually. The minimum values of CS and SUVmax associated with LNM were 10 mm and 0.8 respectively. Pure solid formation and CS as morphology and SUVmax as metabolism were useful tools that complemented each other in predicting LNM. The combined method of evaluating SUVmax and CS may identify eligibility for LN dissection. However, considering the minimum values of CS and SUVmax in LNM, it cannot affirm the omission of LN dissection for cases that do not meet the combined criteria using HRCT and PET-CT.

A comparison of two disease burden assessment methods (3D volume versus the number of lesions) for prognostication of survival in metastatic melanoma: implications for the characterization of oligometastatic disease

BACKGROUND

Oligometastatic disease (OMD), generally defined by the presence of ≤5 metastatic lesions, represents an intermediate state between localized and widespread metastatic disease. This study aimed to question the conventional definition of OMD and assessed the significance of the total volume and loci of metastases in characterizing OMD using an unselected metastatic melanoma cohort.

METHODS

We identified 86 consecutive patients with metastatic melanoma who received pembrolizumab monotherapy during 2015‒2020. We retrospectively contoured the gross tumor volumes of all metastatic lesions on baseline and follow-up imaging. The number, total volume, and loci information of metastases was collected. The primary endpoint was overall survival (OS). Density histogram plot was used for tumor characteristics description, and classification analysis using the decision tree and random forest methods was performed to determine the optimal combination of prognostic factors in the clinical setting.

RESULTS

Total 2,728 gross tumor volumes were delineated. On baseline imaging, the median number and total volume of metastases was 7 (interquartile range [IQR], 3‒17) and 28.4 cc (IQR, 8.4‒88.78), respectively. The lymph node was the most common metastatic site (n=46, 54%), followed by the lungs (n=32, 37%), liver (n=23, 27%), and bones (n=21, 24%). Two-year OS rates of patients with 1‒5, 6‒10, 11‒20, and >20 metastases were 58%, 47%, 31%, and 14%, respectively, and ≤10, 11‒30, 31‒130, and >130 cc of metastatic volume were 64%, 43%, 33%, and 25%, respectively. K-adaptive partitioning revealed that the optimal cutoff was 20 and 37.9 cc. Decision tree and random forest analyses revealed that volume and loci (brain and liver metastases) were the most important factors (Harrell's C-index, 0.78).

CONCLUSIONS

The OMD state could represent a continuous spectrum of disease burden instead of a binary phenomenon. We propose integrating the volumetric and spatial information of metastases into the characterization of OMD and the stratification tool of clinical trials in the metastatic setting, although external validation studies are needed.

Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT

BACKGROUND

Pre-treatment FDG-PET/CT scans were analyzed with machine learning to predict progression of lung malignancies and overall survival (OS).

METHODS

A retrospective review across three institutions identified patients with a pre-procedure FDG-PET/CT and an associated malignancy diagnosis. Lesions were manually and automatically segmented, and convolutional neural networks (CNNs) were trained using FDG-PET/CT inputs to predict malignancy progression. Performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Image features were extracted from CNNs and by radiomics feature extraction, and random survival forests (RSF) were constructed to predict OS. Concordance index (C-index) and integrated brier score (IBS) were used to evaluate OS prediction.

FINDINGS

1168 nodules (n=965 patients) were identified. 792 nodules had progression and 376 were progression-free. The most common malignancies were adenocarcinoma (n=740) and squamous cell carcinoma (n=179). For progression risk, the PET+CT ensemble model with manual segmentation (accuracy=0.790, AUC=0.876) performed similarly to the CT only (accuracy=0.723, AUC=0.888) and better compared to the PET only (accuracy=0.664, AUC=0.669) models. For OS prediction with deep learning features, the PET+CT+clinical RSF ensemble model (C-index=0.737) performed similarly to the CT only (C-index=0.730) and better than the PET only (C-index=0.595), and clinical only (C-index=0.595) models. RSF models constructed with radiomics features had comparable performance to those with CNN features.

INTERPRETATION

CNNs trained using pre-treatment FDG-PET/CT and extracted performed well in predicting lung malignancy progression and OS. OS prediction performance with CNN features was comparable to a radiomics approach. The prognostic models could inform treatment options and improve patient care.

FUNDING

NIH NHLBI training grant (5T35HL094308-12, John Sollee).

Prognostic impact of artificial intelligence-based volumetric quantification of the solid part of the tumor in clinical stage 0-I adenocarcinoma

INTRODUCTION

The size of the solid part of a tumor, as measured using thin-section computed tomography, can help predict disease prognosis in patients with early-stage lung cancer. Although three-dimensional volumetric analysis may be more useful than two-dimensional evaluation, measuring the solid part of some lesions is difficult using this methods. We developed an artificial intelligence-based analysis software that can distinguish the solid and non-solid parts (ground-grass opacity). This software calculates the solid part volume in a totally automated and reproducible manner. The predictive performance of the artificial intelligence software was evaluated in terms of survival or recurrence-free survival.

METHODS

We analyzed the high-resolution computed tomography images of the primary lesion in 772 consecutive patients with clinical stage 0-I adenocarcinoma. We performed automated measurement of the solid part volume using an artificial intelligence-based algorithm in collaboration with FUJIFILM Corporation. The solid part size, the solid part volume based on traditional three-dimensional volumetric analysis, and the solid part volume based on artificial intelligence were compared.

RESULTS

Higher areas under the curve related to the solid part volume were provided by the artificial intelligence-based method (0.752) than by the solid part size (0.722) and traditional three-dimensional volumetric analysis-based method (0.723). Multivariate analysis demonstrated that the solid part volume based on artificial intelligence was independently correlated with overall survival (P = 0.019) and recurrence-free survival (P < 0.001).

CONCLUSION

The solid part volume measured by artificial intelligence was superior to conventional methods in predicting the prognosis of clinical stage 0-I adenocarcinoma.

Predicting prognosis using a pathological tumor cell proportion in stage I lung adenocarcinoma

Background

Tumor size is a valuable prognostic factor because it is considered a measure of tumor burden. However, it is not always correlated with the tumor burden. This study aimed to identify the prognostic role of pathological tumor proportional size using the proportion of tumor cells on the pathologic report after curative resection in pathologic stage I lung adenocarcinoma.

Methods

We retrospectively reviewed the medical records of 630 patients with pathologic stage I lung adenocarcinoma after lung resection for curative aims. According to the pathologic data, the proportion of tumor cells was reviewed and pathological tumor proportional size was estimated by multiplying the maximal diameter of the tumor by the proportion of tumor cells. We investigated the prognostic role of pathological tumor proportional size.

Results

The median tumor size was 2 cm (range: 0.3–4), and the median pathological tumor proportional size was 1.5 (range: 0.12–3.8). This value was recategorized according to the current tumor‐node‐metastasis (TNM) classification, and 184 patients showed down staging compared with the current stage. The survival curve for disease‐free survival using pathological tumor proportional size showed more distinction than the current stage classification. Multivariate analysis revealed that a down stage indicated a favorable prognostic factor.

Conclusion

Pathological tumor cell proportional size may be associated with prognosis in stage I lung adenocarcinoma. If the pathological tumor proportional size shows a downward stage, it may indicate a smaller tumor burden and better prognosis

Prognostic impact of tumor volume in patients with complete resection of thymoma

Background

The impact of tumor volume on prognosis is unclear. We therefore investigated the correlation between tumor volume and prognosis in patients with complete resection of thymoma.

Methods

A total of 177 patients who underwent curative surgical resection for thymoma were retrospectively collected and reviewed. We performed a volumetric analysis of each case using the modified version of “Watchin GGO” and evaluated the relationship between tumor volume and recurrence.

Results

The median tumor size was 5.0 (range 0.5–15) cm, and the median tumor volume was 35.1 (range 0.265–881.0) cm³. The Pearson product–moment correlation coefficient was 0.658, suggesting a moderately strong connection between tumor volume and tumor size. To determine the impact of tumor volume on tumor recurrence, receiver operating characteristic curves of the recurrence and tumor volume were calculated. The area under the curve was 0.65 (95% confidence interval [CI]: 0.51–0.80), and the optimal cutoff level of the tumor volume for recurrence was 82.6 cm³, with a sensitivity and specificity of 0.64 (11/17) and 0.74 (119/160), respectively. Patients with tumors ≥82.6 cm³ had a significantly worse recurrence‐free survival than those with smaller tumors (p = 0.0122, hazard ratio: 2.99), with 5‐year recurrence rates of 74.9% (95% CI: 58.6%–86.3%) versus 88.9% (95% CI: 79.0%–94.4%).

Conclusion

The volume of completely resectable thymoma may be a useful prognostic indicator.

Tumour volume is a predictor of lymphovascular invasion in differentiated small thyroid cancer

Objectives

For small thyroid cancers (≤2 cm), tumour volume may better predict aggressive disease, defined by lymphovascular invasion (LVI) than a traditional single measurement of diameter. We aimed to investigate the relationship between tumour diameter, volume and associated LVI.

Methods

Differentiated thyroid cancers (DTC) ≤ 2 cm surgically resected between 2007 and 2016 were analysed. Volume was calculated using the formula for an ellipsoid shape from pathological dimensions. A 'larger volume' cut-off was established by receiver operating characteristic (ROC) analysis using the presence of lateral cervical lymph node metastasis (N1b). Logistic regression was performed to compare the 'larger volume' cut-off to traditional measurements of diameter in the prediction.

Results

During the study period, 2405 DTCs were surgically treated and 523 met the inclusion criteria. The variance of tumour volume relative to diameter increased exponentially with increasing tumour size; the interquartile ranges for the volumes of 10, 15 and 20 mm diameter tumours were 126, 491 and 1225 mm^3, respectively. ROC analysis using volume to predict N1b disease established an optimal volume cut-off of 350 mm³ (area under curve = 0.59, P = 0.02) as 'larger volume'. 'Larger volume' DTC was an independent predictor for LVI in multivariate analysis (odds ratio (OR) = 1.7, P = 0.02), whereas tumour diameter > 1 cm was not (OR = 1.5, P = 0.13). Both the volume > 350 mm³ and dimension > 1 cm were associated with greater than five lymph node metastasis and extrathyroidal extension.

Conclusion

In this study for small DTCs ≤ 2 cm, the volume of >350 mm³ was a better predictor of LVI than greatest dimension > 1 cm.

Mmp12 Is Upregulated by in utero Second-Hand Smoke Exposures and Is a Key Factor Contributing to Aggravated Lung Responses in Adult Emphysema, Asthma, and Lung Cancer Mouse Models

Matrix metalloproteinase-12 (Mmp12) is upregulated by cigarette smoke (CS) and plays a critical role in extracellular matrix remodeling, a key mechanism involved in physiological repair processes, and in the pathogenesis of emphysema, asthma, and lung cancer. While cigarette smoking is associated with the development of chronic obstructive pulmonary diseases (COPD) and lung cancer, in utero exposures to CS and second-hand smoke (SHS) are associated with asthma development in the offspring. SHS is an indoor air pollutant that causes known adverse health effects; however, the mechanisms by which in utero SHS exposures predispose to adult lung diseases, including COPD, asthma, and lung cancer, are poorly understood. In this study, we tested the hypothesis that in utero SHS exposure aggravates adult-induced emphysema, asthma, and lung cancer.

Methods: Pregnant BALB/c mice were exposed from gestational days 6–19 to either 3 or 10mg/m³ of SHS or filtered air. At 10, 11, 16, or 17weeks of age, female offspring were treated with either saline for controls, elastase to induce emphysema, house-dust mite (HDM) to initiate asthma, or urethane to promote lung cancer. At sacrifice, specific disease-related lung responses including lung function, inflammation, gene, and protein expression were assessed.

Results: In the elastase-induced emphysema model, in utero SHS-exposed mice had significantly enlarged airspaces and up-regulated expression of Mmp12 (10.3-fold compared to air-elastase controls). In the HDM-induced asthma model, in utero exposures to SHS produced eosinophilic lung inflammation and potentiated Mmp12 gene expression (5.7-fold compared to air-HDM controls). In the lung cancer model, in utero exposures to SHS significantly increased the number of intrapulmonary metastases at 58weeks of age and up-regulated Mmp12 (9.3-fold compared to air-urethane controls). In all lung disease models, Mmp12 upregulation was supported at the protein level.

Conclusion: Our findings revealed that in utero SHS exposures exacerbate lung responses to adult-induced emphysema, asthma, and lung cancer. Our data show that MMP12 is up-regulated at the gene and protein levels in three distinct adult lung disease models following in utero SHS exposures, suggesting that MMP12 is central to in utero SHS-aggravated lung responses.

Prognostic performance in lung cancer according to tumor size: Comparison of axial, multiplanar, and 3-dimensional CT measurement to pathological size

PURPOSE

This study aimed to compare the prognostic performance of clinical T staging based on axial, multiplanar, and 3-dimensional measurement on CT with that of pathological T staging in patients with non-small cell lung cancer.

METHOD

Patients with surgically resected lung cancer without pathological node metastasis between June 2010 and December 2017 were retrospectively included. Clinical T stages were determined based on the maximal tumor size on axial, multiplanar (axial, coronal, and sagittal) images and 3-dimensional tumor mask. The prognostic performances of clinical and pathological T staging for disease-free survival (DFS) were compared using the concordance indices (C-indices).

RESULTS

A total of 544 patients (64.7 ± 9.7 years, 352 men) were included; 160 patients (29.4%) experienced events including 29 (5.3%) who expired. The median DFS was 44.1 months. The mean tumor size on axial, multiplanar images, 3-dimensional tumor mask, and pathology was 30.8 ± 17.3, 33.9 ± 19.4, 39.2 ± 21.4, and 33.4 ± 18.0 mm, respectively. Clinical staging based on multiplanar measurement showed a higher agreement (67.5% [367/544]) with pathological staging than axial (60.5% [329/544]) and 3-dimensional measurement (50.9% [277/544]) based staging did (p = .0005 and <.0001, respectively). The adjusted C-indices of axial, multiplanar, 3-dimensional, and pathological tumor stages were 0.66 (95% confidence interval [CI]: 0.66-0.67), 0.66 (95% CI: 0.66-0.66), 0.67 (95% CI: 0.67-0.67), and 0.67 (95% CI: 0.66-0.67), respectively (p > .05).

CONCLUSIONS

The prognostic performances of tumor staging according to size measurement methods were not significantly different. Multiplanar measurement may be preferable for clinical staging considering its highest agreement with pathological staging.

Emphysematous changes and lower levels of plasma irisin are associated with bronchiolitis obliterans syndrome after bilateral living-donor lobar lung transplantation

PURPOSE

Decreased irisin levels may be associated with the development of emphysema. Similarly, emphysematous changes may develop in patients with chronic lung allograft dysfunction (CLAD) after living-donor lobar lung transplantation (LDLLT). We investigated the severity of emphysematous changes and the relationship between irisin levels and CLAD after bilateral LDLLT and cadaveric lung transplantation (CLT).

METHODS

The subjects of this retrospective study were 59 recipients of bilateral LDLLT (n = 31) or CLT (n = 28), divided into a non-CLAD group (n = 41), a LDLLT-CLAD group (n = 11), and a CLT-CLAD group (n = 7). We compared the severity of emphysematous changes, the skeletal muscle mass, and the plasma irisin levels among the groups.

RESULTS

The emphysematous changes were significantly more severe in the LDLLT-CLAD and CLT-CLAD groups (p = 0.046 and 0.036), especially in patients with bronchiolitis obliterans syndrome (BOS), than in the non-CLAD group. Although the skeletal muscle mass was similar in all the groups, the plasma irisin levels were significantly lower in the LDLLT-CLAD group (p = 0.022), especially in the patients with BOS after LDLLT, than in the non-CLAD group.

CONCLUSION

Emphysematous changes and lower levels of plasma irisin were associated with CLAD, especially in patients with BOS, after bilateral LDLLT.

[Value of PET/CT Combined with CT Three-dimensional Reconstruction in Distinguishing Different Pathological Subtypes of Early Lung Adenocarcinoma]

背景与目的

早期肺腺癌中病理亚型以贴壁为主型的浸润性腺癌（lepidic predominant invasive adenocarcinoma, LPA）与原位腺癌（adenocarcinoma in situ, AIS）、微浸润性腺癌（microinvasive adenocarcinoma, MIA）的良好预后相似，临床上也迫切需要能够区分LPA与非LPA型浸润性腺癌（non-lepidic predominant invasive adenocarcinoma, non-LPA）的手段，本研究拟通过正电子发射型计算机断层显像（positron emission computed tomography, PET）/计算机断层扫描（computed tomography, CT）的最大标准化摄取值（maximal standard uptake value, SUVmax）和CT三维重建后参数探讨术前影像学表现为部分实性结节（part-solid nodules, PSNs）的早期肺腺癌不同病理亚型间的关系。

方法

回顾性分析2016年1月-2019年1月于江苏省苏北人民医院胸外科行解剖性肺切除术且影像学表现为PSNs的早期肺腺癌患者资料，所有患者胸部增强CT和PET/CT资料完整可获取，利用Mimics软件行三维重建，获取肿瘤体积、肿瘤三维平均CT值（3-dimensional mean-CT value, 3Dm-CT）、SUVmax等数据，采用SPSS 25.0进行统计分析，GraphPad Prism 8.3.0绘制受试者工作曲线（receiver operating curve, ROC），P < 0.05为差异有统计学意义。

结果

最终共计67例患者纳入本研究，按病理亚型不同将所有患者分为两组，AIS、MIA及浸润性腺癌（invasive adenocarcinoma, IAC）中的LPA归为低危组28例（41.8%），其余non-LPA如腺泡型（acinar pattern-predominant adeno-carcinoma, APA）、乳头型（papillary pattern-predominant adenocarcinoma, PPA）、微乳头型（micropapillary pattern-predominant adeno-carcinoma, MPA）归为高危组39例（58.2%），两组间SUVmax（t=3.153, P=0.002）、肿瘤体积（t=3.331, P=0.001)、实性/磨玻璃成分体积（t=2.74, P=0.006) /（t=3.127, P=0.002）、实性/磨玻璃成分3Dm-CT（t=3.655, P < 0.001) /（t=7.082, P < 0.001) 均具有显著统计学意义。ROC曲线提示：SUVmax[曲线下面积（area under curve, AUC）=0.727]、肿瘤体积（AUC=0.740）、磨玻璃成分体积（AUC=0.725）、实性成分3Dm-CT（AUC=0.763）、磨玻璃成分3Dm-CT（AUC=0.756）预测效能最佳。将上述AUC > 0.7的协变量纳入多因素ROC曲线分析，获得联合预测因子（AUC=0.835）具有中等以上预测价值。

结论

PET/CT中SUVmax和CT三维重建参数与影像学表现为PSNs的早期肺腺癌的不同病理亚型具有显著相关性，联合SUVmax、肿瘤体积、磨玻璃成分体积和实性/磨玻璃成分3Dm-CT对鉴别表现为PSNs的早期肺腺癌的病理亚型具有一定价值。

Narrative review of optimal prognostic radiological tools using computed tomography for T1N0-staged non-small cell lung cancer

Various radiological tools can predict the prognosis of non-small cell lung cancer (NSCLC). In this study, we evaluated the prognostic effect of different radiological tools such as whole tumor size (WTS), consolidation size (CS), consolidation tumor ratio (CTR), tumor disappearance ratio (TDR), mediastinal diameter (MD), and ground glass opacity (GGO) using high-resolution computed tomography (HRCT). We reviewed recent retrospective studies on the predictive effect of these radiological tools on disease-free survival (DFS) and overall survival (OS) in patients with T1N0-staged NSCLC. We searched PubMed and the British Library databases for the English literature published from January 2010 to December 2020 and generated a total of 32 publications (NSCLC, n=16; adenocarcinoma, n=16). The TNM classification version 7 was used in 18 studies, and version 8 in 14 studies. The evaluated radiological parameters were WTS, CS including T category, CTR, TDR, MD, presence of GGO, GGO ratio, and pure GGO. This review suggested that CS, MD, and the presence of GGO are optimal prognostic radiological tools for cT1N0-Staged NSCLC. CTR or TDR for part solid nodules (PSNs) is not a well-accepted prognostic factor. Further investigations are required to differentiate between benign scars and malignant components on HRCT and evaluate the prognosis of PSNs (1< CS ≤2 cm) with large WTS in the future.

Evolutionary dynamics at the tumor edge reveal metabolic imaging biomarkers

Human cancers are biologically and morphologically heterogeneous. A variety of clonal populations emerge within these neoplasms and their interaction leads to complex spatiotemporal dynamics during tumor growth. We studied the reshaping of metabolic activity in human cancers by means of continuous and discrete mathematical models and matched the results to positron emission tomography (PET) imaging data. Our models revealed that the location of increasingly active proliferative cellular spots progressively drifted from the center of the tumor to the periphery, as a result of the competition between gradually more aggressive phenotypes. This computational finding led to the development of a metric, normalized distance from 18F-fluorodeoxyglucose (18F-FDG) hotspot to centroid (NHOC), based on the separation from the location of the activity (proliferation) hotspot to the tumor centroid. The NHOC metric can be computed for patients using 18F-FDG PET-computed tomography (PET/CT) images where the voxel of maximum uptake (standardized uptake value [SUV]max) is taken as the activity hotspot. Two datasets of 18F-FDG PET/CT images were collected, one from 61 breast cancer patients and another from 161 non-small-cell lung cancer patients. In both cohorts, survival analyses were carried out for the NHOC and for other classical PET/CT-based biomarkers, finding that the former had a high prognostic value, outperforming the latter. In summary, our work offers additional insights into the evolutionary mechanisms behind tumor progression, provides a different PET/CT-based biomarker, and reveals that an activity hotspot closer to the tumor periphery is associated to a worst patient outcome.

Consolidation volume and integration of computed tomography values on three-dimensional computed tomography may predict pathological invasiveness in early lung adenocarcinoma

PURPOSE

To investigate the relationship between three-dimensional computed tomography (3D-CT) findings and pathological invasiveness in lung adenocarcinoma.

METHODS

We retrospectively evaluated 95 patients who underwent surgical resection of lung adenocarcinoma of ≤ 20 mm. The diameters, volumes, and CT values of tumor consolidation were analyzed. We defined the modified CT value by setting air as 0 and water as 1000 and assumed a correlation with pathological invasiveness. Pre-invasive lesions and minimally invasive adenocarcinomas were classified as non-invasive adenocarcinoma. We compared the clinico-radiological features with pathological invasiveness. Receiver operator characteristic (ROC) curves and recurrence-free survival curves were constructed.

RESULTS

Twenty-six non-invasive adenocarcinomas and 69 invasive adenocarcinomas were evaluated. The multivariate analysis revealed that the consolidation volume and the integration of modified CT values were the most important predictors of pathological invasion. The area under the ROC curve and the cut-off values of the consolidation volume were 0.868 and 75 mm³, respectively. The area under the ROC curve and the cut-off values of the integration of modified CT values were 0.871 and 80,000, respectively. There was no recurrence in cases with values below the cut-off across all parameters.

CONCLUSION

The consolidation volume and integration of modified CT values were shown to be highly predictive of pathological invasiveness.

The utility of three-dimensional computed tomography for prediction of tumor invasiveness in clinical stage IA lung adenocarcinoma

Background

It is critical to have an accurate measurement of solid tumor size in order to predict the invasiveness of small lung adenocarcinomas. Some lesions cannot be measured accurately via High-resolution computed tomography (HRCT) due to their irregular shape and unclear borders. For this reason, we evaluated the relative efficacy of three-dimensional (3D) CT for predicting invasive adenocarcinoma.

Methods

We evaluated 195 patients with clinical stage IA adenocarcinomas, including 109 with lesions documented as invasive that were surgically resected at our institute during 2017. All lesions were categorized as either (I) lesions that were difficult to evaluate (i.e., hazy lesions; HL) or (II) more typical lesions (TL). The relationships between solid tumor size as determined by HRCT, solid tumor volume as determined by 3D CT and pathologic diagnosis were evaluated.

Results

Fifty-seven patients (29%) were diagnosed with HL. We set the cut-off value for the solid volume at 225 mm³ as predictive for invasive adenocarcinoma. When evaluating all 195 patients as a group, the accuracy, sensitivity, and specificity based on the solid tumor volume were similar to those based on the solid tumor size. When we limit our analysis to the HL group, the specificity based on solid tumor volume (65.5%) was higher than that based on solid tumor size (44.8%) with a difference that approached statistical significance (P=0.070).

Conclusions

3D CT was equivalent to HRCT for predicting invasive adenocarcinoma and may be particularly useful for diagnosing lesions that are difficult to evaluate on HRCT.

The Study of Tumor Volume as a Prognostic Factor in T Staging System for Non-Small Cell Lung Cancer: An Exploratory Study

Background:

This study aimed to evaluate T staging system for non-small cell lung cancer (NSCLC) using tumor volume (TV) and other prognostic factors.

Methods:

This study included 1309 cases. The TV and greatest tumor diameter (GTD) were semi-automatically measured. The receiver operating characteristic (ROC) curves of TV and GTD were used to predict survival. The regression analysis was used to describe the correlation between GTD and TV. Overall survival (OS) was analyzed using the Kaplan-Meier method. Cox’s proportional hazards regression model was applied for multivariate analysis.

Results:

Using the OS in pN0M0 patients (997 cases), we obtained 4 optimal cutoff values and divided all cases into 5 TV groups (V1: TV ≤ 2.80 cm³; V2: TV > 2.80–6.40 cm³; V3: TV > 6.40–12.9 cm³; V4: TV > 12.9–55.01 cm³; V5: TV > 55.01 cm³) with significant OS (P < 0.001). Multivariate analysis showed that age, visceral pleural invasion (VPI), and all TV cutoff points were independent factors of OS (P < 0.05). For V3 and V4 groups, the OS in patients without VPI was better than that in patients with VPI. Using the values of TV, VPI, and N stages, we classified all cases into 5 stages from I to V depending on the OS. The OS in I, II, III, IV, and V stages were 71.3%, 65.5%, 59.8%, 47.7%, and 35.1% respectively (P < 0.001).

Conclusions:

We proposed a new T staging system using TV as the main prognostic descriptor in NSCLC patients, which may provide a better comprehensive clinical value than GTD in clinical applications.

Tumor volume is more reliable to predict nodal metastasis in non-small cell lung cancer of 3.0 cm or less in the greatest tumor diameter

Background

In this study, we sought to evaluate the correlation between TV, GTD, and lymph node metastases in NSCLC patients with tumors of GTD ≤ 3.0 cm.

Methods

We retrospectively analyzed the characteristics of clinicopathologic variables for lymph node involvement in 285 NSCLC patients with tumors of GTD ≤ 3.0 cm who accepted curative surgical resection. The TVs were semi-automatically measured by a software, and optimal cutoff points were obtained using the X-tile software. The relationship between GTD and TV were described using non-linear regression. The correlation between GTD, TV, and N stages was analyzed using the Pearson correlation coefficient. The one-way ANOVA was used to compare the GTD and TV of different lymph node stage groups.

Results

The relationship between GTD and TV accorded with the exponential growth model: y = 0.113e^1.455x (y = TV, x = GTD). TV for patients with node metastases (4.78 cm³) was significantly greater than those without metastases (3.57 cm³) (P < 0.001). However, there were no obvious GTD differences in cases with or without lymph node metastases (P = 0.054). We divided all cases into three TV groups using the two cutoff values (0.9 cm³ and 3.9 cm³), and there was an obvious difference in the lymphatic involvement rate between the groups (P < 0.001). The tendency to metastasize was greater with higher TV especially when the TV was > 0.9–14.2 cm³ (P = 0.010).

Conclusions

For NSCLC tumors with GTD ≤ 3.0 cm, TV is a more sensitive marker than GTD in predicting the positive lymph node metastases. The likelihood for metastasis increases with an increasing TV especially when GTD is > 2.0–3.0 cm.

Efficacy of measuring the invasive diameter of lung adenocarcinoma using mediastinal window settings: A retrospective study

The recently published 8th edition of the tumor node and metastasis Classification of Lung Cancer proposes using the maximum dimension of the solid component of a ground glass nodule (GGN) for the T categorization. However, few studies have investigated the collection of this information when using mediastinal window settings. In this study, we evaluated tumor measurement data obtained from computed tomography (CT) scans when using mediastinal window settings.This study included 202 selected patients with persistent, partly solid GGNs detected on thin-slice CT after surgical treatment between 2004 and 2013. We compared the differences in tumor diameters measured by 2 different radiologists using a repeated-measures analysis of variance. We divided the patients into 2 groups based on the clinical T stage (T1a+T1b vs T1c) and estimated the probability of overall survival (OS) and disease-free survival (DFS) using Kaplan-Meier curves.The study included 94 male and 108 female patients. The inter-reviewer differences between tumor diameters were significantly smaller when the consolidation to maximum tumor diameter ratio was ≤0.5. The 2 clinical groups classified by clinical T stage differed significantly with respect to DFS when using the mediastinal window settings. However, no significant differences in OS or DFS were observed when using the lung window setting.Our study yielded 2 major findings. First, the diameters of GGNs could be measured more accurately using the mediastinal window setting. Second, measurements obtained using the mediastinal window setting more clearly depicted the effect of clinical T stage on DFS.

Machine learning helps identifying volume-confounding effects in radiomics

PURPOSE

Highlighting the risk of biases in radiomics-based models will help improve their quality and increase usage as decision support systems in the clinic. In this study we use machine learning-based methods to identify the presence of volume-confounding effects in radiomics features. Methods 841 radiomics features were extracted from two retrospective publicly available datasets of lung and head neck cancers using open source software. Unsupervised hierarchical clustering and principal component analysis (PCA) identified relations between radiomics and clinical outcomes (overall survival). Bootstrapping techniques with logistic regression verified features' prognostic power and robustness. Results Over 80% of the features had large pairwise correlations. Nearly 30% of the features presented strong correlations with tumor volume. Using volume-independent features for clustering and PCA did not allow risk stratification of patients. Clinical predictors outperformed radiomics features in bootstrapping and logistic regression. Conclusions The adoption of safeguards in radiomics is imperative to improve the quality of radiomics studies. We proposed machine learning (ML) - based methods for robust radiomics signatures development.

Postoperative changes of the free pericardial fat pad for bronchial stump coverage

Background

Bronchopleural fistula (BPF) remains a serious complication after surgery for lung cancer with bronchial resection. A free pericardial fat pad (FPFP) is applied in high-risk cases to reduce BPF frequency. BPF may occur 6 months after surgery. Thus, we evaluated the residual FPFP volume at 6 months after surgery to estimate the residual FPFP ratio and determine the amount of FPFP to be harvested during surgery.

Methods

We retrospectively investigated 40 patients who underwent lobectomy with bronchial stump coverage using FPFP. During surgery, the volume of the harvested FPFP was measured and the FPFP was affixed to the bronchial stump. Further, 6 months after surgery, the residual volume of the installed FPFP was analyzed using a three-dimensional volume analyzer and the residual ratio was calculated. We also evaluated clinicopathological factors influencing the resected FPFP and residual ratio.

Results

The median resected FPFP volume was 11 [3-40] mL. During multivariate analysis, body mass index and surgical approach were found to be significant factors associated with the resected FPFP volume. The median residual FPFP volume was 4.3 (0.4-15.5) mL. The median residual ratio was 0.39 (0.13-0.66). The resected FPFP volume was significantly associated with the residual volume (P<0.001) but not with the residual ratio (P=0.811). No factor was associated with the residual ratio.

Conclusions

In all cases, residual FPFP was confirmed at 6 months after surgery and the residual ratio was 40%. It is necessary to determine the volume of FPFP to be harvested while carefully considering the shrinkage ratio.

CT radiomics and PET radiomics: ready for clinical implementation?

INTRODUCTION

Today, rapid technical and clinical developments result in an increasing number of treatment options for oncological diseases. Thus, decision support systems are needed to offer the right treatment to the right patient. Imaging biomarkers hold great promise in patient-individual treatment guidance. Routinely performed for diagnosis and staging, imaging datasets are expected to hold more information than used in the clinical practice. Radiomics describes the extraction of a large number of meaningful quantitative features from medical images, such as computed tomography (CT) and positron emission tomography (PET). Due to the non-invasive nature and ability to capture 3D image-based heterogeneity, radiomic features are potential surrogate markers of the cancer phenotype. Several radiomic studies are published per day, owing to encouraging results of many radiomics-based patient outcome models. Despite this comparably large number of studies, radiomics is mainly studied in proof of principle concept. Hence, a translation of radiomics from a hot topic research field into an essential clinical decision-making tool is lacking, but of high clinical interest.

EVIDENCE ACQUISITION

Herein, we present a literature review addressing the clinical evidence of CT and PET radiomics. An extensive literature review was conducted in PubMed, including papers on robustness and clinical applications.

EVIDENCE SYNTHESIS

We summarize image-modality related influences on the robustness of radiomic features and provide an overview of clinical evidence reported in the literature. Today, more evidence has been provided for CT imaging, however, PET imaging offers the promise of direct imaging of biological processes and functions. We provide a summary of future research directions, which needs to be addressed in order to successfully introduce radiomics into clinical medicine. In comparison to CT, more focus should be directed towards harmonization of PET acquisition and reconstruction protocols, which is important for transferable modelling.

CONCLUSIONS

Both CT and PET radiomics are promising pre-treatment and intra-treatment biomarkers for outcome prediction. Most studies are performed in retrospective setting, however their validation in prospective data collections is ongoing.

Post-therapy pathologic tumor volume predicts survival in gastric cancer patients who underwent neoadjuvant chemotherapy and gastrectomy

BACKGROUND

To demonstrate that post-therapy pathological tumor volume (ypTV) should be considered as an independent prognostic factor in advanced gastric cancer (GC) patients who underwent neoadjuvant chemotherapy (NAC) and gastrectomy.

METHODS

A total of 253 GC patients who received gastrectomy between January 2010 and December 2016 in our hospital were enrolled in this study. Clinicopathologic factors were evaluated using univariable and multivariable analysis. ypTV was calculated using π* (tumor diameter/2)² *tumor invasion depth (cm³).

RESULTS

Cut-point survival analysis demonstrated that the appropriate cut-offs for ypTV were 3, 6, 10, and 19 (cm³). Patients with tumor volumes of 0-3.0, 3.1-6.0, 6.1-10.0, 10.1-19.0, ≥19.1 cm³ were defined as ypTV1, 2, 3, 4a and 4b. Using multivariable analysis, the tumor volume (ypTV stage, P < 0.05), ypN stage (P < 0.05), response to NAC (P < 0.05), vascular invasion (P < 0.05) and ypTvNM staging (P < 0.05) were independent prognostic factors. Kaplan-Meier analysis demonstrated that the 8th AJCC/UICC ypTNM staging was not a significant predictor for survival (P > 0.05); however, our newly defined ypTvNM staging was a significant predictor for survival (P < 0.05).

CONCLUSIONS

ypTV should be considered as an independent prognostic factor for GC patients after NAC. ypTvNM staging should be recommended to improve the accuracy of prognostic prediction for GC patients who received NAC plus gastrectomy.

Volume-Based Consolidation-to-Tumor Ratio Is a Useful Predictor for Postoperative Upstaging in Stage I and II Lung Adenocarcinomas

BACKGROUND

 We investigated the postoperative upstaging of stage I and II lung adenocarcinoma patients to identify useful predictors for accurate staging.

METHODS

 We retrospectively reviewed data from 80 consecutive patients undergoing lobectomy and mediastinal lymph node dissection for clinical stage I and II lung adenocarcinomas. We evaluated clinical variables, including the preoperative serum carcinoembryonic antigen (CEA), tumor diameter, consolidation-to-tumor ratio (CTR), maximum standardized uptake value (SUVmax) on FDG- PET, expression of epithelial growth factor receptor mutations, and pathological invasion to the pleura (pl), lymph duct (ly), and vein (v).

RESULTS

 Eleven patients (13.8%) showed postoperative upstaging. Three cases had pN1 migrating from cN0, four cases had pN2 from cN0, and four cases showed malignant pleural effusion. The CEA level and CTR were significantly higher in the upstaging group. The tumors in the upstaging group showed more frequent pathological invasion to the visceral pleura and vein. In patients with 3 cm or smaller consolidation, two-dimensional (2D)-CTR and volume-based CTR were independent predictors of upstaging.

CONCLUSIONS

 Volume-based CTR could be a useful predictor for accurate clinical staging in stage I and II adenocarcinoma patients in addition to consolidation size, serum CEA level, and 2D-CTR. Both volume-based and 2D-CTRs might be especially useful in T1 diseases.

Tumor Volume Is Better Than Diameter for Predicting the Prognosis of Patients with Early-Stage Non-small Cell Lung Cancer

BACKGROUND

This study aimed to investigate whether tumor volume (TV) is better than diameter for predicting the prognosis of patients with early-stage non-small cell lung cancer (NSCLC) after complete resection.

METHODS

This study retrospectively reviewed the clinicopathologic characteristics of 274 patients with early-stage NSCLC who had received pretreatment computed tomography (CT) scans and complete resection. TV was semi-automatically measured from CT scans using an imaging software program. The optimal cutoff of TV was determined by X-tile software. Disease-free survival (DFS) and overall survival (OS) were assessed by the Kaplan-Meier method. The prognostic significance of TV and other variables was assessed by Cox proportional hazards regression analysis.

RESULTS

Using 3.046 cm³ and 8.078 cm³ as optimal cutoff values of TV, the patients were separated into three groups. A larger TV was significantly associated with poor DFS and OS in the multivariable analysis. Kaplan-Meier curves of DFS and OS showed significant differences on the basis of TV among patients with stage 1a disease, greatest tumor diameter (GTD) of 2 cm or smaller, and GTD of 2-3 cm, respectively. Using two TV cutoff points, three categories of TV were created. In 54 cases (19.7%), patients migrated from the GTD categories of 2 cm or smaller, 2-3 cm, and larger than 3 cm into the TV categories of 3.046 cm³ or smaller, 3.046-8.078 cm³, and larger than 8.078 cm³.

CONCLUSION

TV is an independent prognostic factor of DFS and OS for early-stage NSCLC. The findings show that TV is better than GTD for predicting the prognosis of patients with early-stage NSCLC.

[Establishment and Verification of A Novel Predictive Model of Malignancy for Non-solid Pulmonary Nodules]

背景与目的

数学预测模型是判断肺小结节恶性概率的有效工具。伴随肺癌流行病学趋势的改变，以非实性肺小结节为影像学表现的早期肺癌检出率逐年升高，准确鉴别并及时治疗干预可有效改善预后。本研究旨在专门针对非实性肺小结节构建新型恶性概率预测模型，为有创诊疗提供客观依据，并尽量避免不必要的侵袭性操作及其可能造成的严重后果。

方法

回顾性分析自2013年1月-2018年4月，单中心经穿刺活检或手术切除获得明确病理诊断的362例非实性肺小结节病例资料，包括临床基本资料、血清肿瘤标记物和影像学特征等。病例分两组，应用建模组数据做单因素分析和二分类Logistic回归，判定独立危险因素，建立预测模型；应用验证组数据验证模型预测价值并与其他模型比较。

结果

362例非实性肺小结节病例中，313例（86.5%）确诊为非典型腺瘤样增生（atypical adenomatous hyperplasia, AAH）/原位腺癌（adenocarcinoma in situ, AIS）、微浸润腺癌（minimally invasive adenocarcinoma, MIA）或浸润性腺癌，49例诊断为良性病变。年龄、血清肿瘤标记物癌胚抗原（carcino-embryonic antigen, CEA）和Cyfra21-1、肿瘤实性成分比值（consolidation tumor ratio, CTR）、分叶征和钙化被确定为独立危险因素。模型受试者工作曲线下面积为0.894。预测灵敏度为87.6%，特异度为69.7%，阳性预测94.8%，阴性预测值为46.9%。经验证模型预测价值显著优于VA、Brock和GMUFH模型。

结论

本研究建立的新型非实性肺小结节恶性概率预测模型具备较高的诊断灵敏度和阳性预测值。经初步验证，其预测价值优于传统模型。未来经大样本验证后，可用作高危非实性肺小结节活检或手术切除前的初筛方法，具备临床应用价值。

Prognostic value of tumor solid-part size and solid-part volume in patients with clinical stage I non-small cell lung cancer

Background

This study aimed to predict the malignant potential of clinical stage I non-small cell lung cancer (c-I NSCLC) by semiautomatic three-dimensional (3D) volumetric measurement of a tumor (3D-data) and the axial computed tomography (CT) data derived from a 3D volumetric dataset (2D-data). The predictive performance was evaluated in terms of overall survival (OS), disease-free survival (DFS), and pathological invasive factors (positive lymphatic invasion, blood vessel invasion, pleural invasion, or lymph node metastasis).

Methods

We identified 252 patients (122 male; mean age, 68 years; range, 23-84 years) with c-I NSCLC who underwent high resolution CT and reconstruction of 3D imaging, followed by complete resection between January 2012 and December 2015. In this study, 2D-data including whole tumor size (WTS) and solid-part size (SPS) and 3D-data including whole tumor volume (WTV) and solid-part volume (SPV) acquired by a 3D volume rendering software were analyzed.

Results

The area under the receiver operating characteristic (ROC) curve for WTS, SPS, WTV, SPV relevant to recurrence was 0.667, 0.727, 0.654, and 0.751 while analyses of ROC curves revealed optimal WTS, SPS, WTV, and SPV cut-off values to predict recurrence of 2.48 cm, 2.03 cm, 3,258 mm³ and 1,889 mm³, respectively. The association between SPS and SPV was the coefficient of determination (R ²) =0.59. Multivariate analysis showed that SPV >1,889 mm³ (P=0.016) and male (P=0.041) were significant predictors of OS whereas SPV >1,889 mm³ (P=0.001), male (P=0.003), and the serum carcinoembryonic antigen value (P=0.041) were significantly correlated with DFS. SPS, SPV as well as the combination of SPS and SPV were all significantly correlated with the prediction of OS and DFS, and the incidence of pathological invasive factors.

Conclusions

SPV and the integrated use of SPS and SPV was highly beneficial for the prediction of postoperative prognosis in c-I NSCLC.

Therapeutic bronchoscopy in the era of genotype directed lung cancer management

Lung cancer is the leading cause of cancer related deaths. Non-small cell lung cancer (NSCLC) accounts for ~85% of lung cancers. Our understanding of driver mutations and genotype directed therapy has revolutionized the management of advanced NSCLC. Commonly described mutations include mutations in epidermal growth factor (EGFR) & BRAF and translocations in anaplastic lymphoma kinase (ALK) & rat osteosarcoma (ROS1). Drugs directed against these translocations have significantly improved progression free survival individually and have shown a survival benefit when studied in the Lung Cancer Mutation Consortium (median survival 3.5 vs. 2.4 years compared to standard therapy). In a related yet parallel universe, the number of bronchoscopic ablative modalities available for management of cancer related airway obstruction have increased exponentially over the past decade. A wealth of literature has given us a better understanding of the technical aspects, benefits and risks associated with these procedures. While they all show benefits in terms of relieving airway obstruction, symptom control, quality of life and lung function testing, their complication rates vary based on the modality. The overall complication rate was ~4% in the AQuIRE registry. Bronchoscopic therapeutic modalities include rigid bronchoscopy with mechanical debulking, laser, thermo-coagulation [electrocautery & argon plasma coagulation (APC)], cryotherapy, endobronchial brachytherapy (EBT), photodynamic therapy (PDT), intratumoral chemotherapy (ITC) and transbronchial needle injection (TBNI) of chemotherapy. Intuitively, one would assume that the science of driver mutations would crisscross with the science of bronchoscopic ablation as they overlap in the same patient population. Sadly, this is not the case and there is a paucity of literature looking at these fields together. This results in several unanswered questions about the interplay between these two therapies.

Predictive accuracy of lepidic growth subtypes in early-stage adenocarcinoma of the lung by quantitative CT histogram and FDG-PET

OBJECTIVES

The aim of this study was to analyze the accuracy of computed tomography (CT) and F-18 fluorodeoxyglucose-positron emission tomography/CT (FDG-PET/CT) to distinguish lepidic growth adenocarcinoma (LGA), including adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and lepidic-predominant adenocarcinoma, all of which have favorable survival outcomes, from the more aggressive and invasive non-LGA subtypes.

MATERIALS AND METHODS

We identified 225 patients with c-0/I adenocarcinoma of the lung who underwent PET/CT and 3DCT followed by complete resection. Maximum standardized uptake values (SUVmax) of FDG and several histogram parameters were analyzed. Histological grades were classified according to the predominant subtype (G1: lepidic; G3: micropapillary or solid; and G2: subtypes other than G1/G3).

RESULTS

The proportion of pathological invasive factors (lymphatic vessel involvement/blood vessel invasion/pleural invasion/lymph node metastasis) of patients with preinvasive adenocarcinoma, G1, G2, and G3 tumors were 0%, 3.6%, 48.0%, and 100%, respectively; p <  0.001). Multivariate analysis with CT-related parameters demonstrated that 75^th percentile CT attenuation value (75^th%, p <  0.001) and maximum CT attenuation value (maxCT, p =  0.009) were associated with incidence of non-LGA, whereas the value of SUVmax demonstrated a significant correlation (p <  0.001). When all patients were dichotomized according to ground-glass opacities (GGO)/solid-dominancy for CT maximum diameter, a significant correlation with non-LGA was shown in patients with solid-dominant tumor on SUVmax (p <  0.001) and with GGO-dominant tumor on 75^th% (p =  0.006) and maxCT (p =  0.007). The combination of one of the two significant histogram parameters and SUVmax revealed higher predictive performance for pathological high malignant features (positive pathological invasive factors, non-LGA, and the highly malignant subtype covering G2 with moderately or poorly-differentiated carcinoma and G3) than the individual use of either factor.

CONCLUSION

The 75^th%, maxCT, and SUVmax were highly useful in distinguishing LGA from non-LGA in c-0/I adenocarcinoma.

Implication of total tumor size on the prognosis of patients with clinical stage IA lung adenocarcinomas appearing as part-solid nodules: Does only the solid portion size matter?

OBJECTIVES

The aim was to investigate the effect of clinico-radiologic variables, including total tumor (T_total) size and clinical T category, on the prognosis of patients with stage IA (T1N0M0) lung adenocarcinomas appearing as part-solid nodules (PSNs).

METHODS

This institutional review board-approved retrospective study included 506 patients (male:female = 200:306; median age, 62 years) with PSNs of the adenocarcinoma spectrum in clinical stage IA who underwent standard lobectomy at a single tertiary medical center. Prognostic stratification of the patients in terms of disease-free survival was analyzed with variables including age, sex, T_total size, solid portion size, clinical T category, and tumor location using univariate and subsequent multivariate Cox regression analysis. Subgroup analysis was performed to reveal the effect of the T_total size at each clinical T category.

RESULTS

Multivariate Cox regression analysis demonstrated that T_total size*cT1b [interaction term; hazard ratio (HR) = 1.091; 95% confidence interval (CI): 1.015, 1.173; p = 0.019] and cT1c (HR = 68.436; 95% CI: 2.797, 1674.415; p = 0.010) were independent risk factors for the tumor recurrence. When patients with cT1b were dichotomized based on a T_total size cutoff of 3.0 cm, PSNs with T_total > 3.0 cm showed a significantly worse outcome (HR = 3.796; 95% CI: 1.006, 14.317; p = 0.049). No significant difference was observed in the probability of recurrence between cT1b with T_total > 3.0 cm and cT1c (p = 0.915).

CONCLUSIONS

T_total size is a significant prognostic factor in adenocarcinoma patients in cT1b without lymph node or distant metastasis. PSNs in cT1b with T_total > 3.0 cm have a comparable risk of lung cancer recurrence to those in cT1c.

KEY POINTS

• Current T descriptor was a powerful prognostic factor in stage IA adenocarcinomas appearing as part-solid nodules. • Total tumor size further stratified risk of recurrence of adenocarcinomas in cT1b. • Upstaging of tumors in cT1b with total tumor size > 3.0 cm may be more appropriate.

Prognostic Factors in Completely Resected Node-Negative Lung Adenocarcinoma of 3 cm or Smaller

INTRODUCTION

The role of adjuvant chemotherapy for patients with stage I NSCLC remains unknown. The prognostic value of histological subtypes in resected node-negative small lung adenocarcinoma has not been widely investigated. This study investigated the prognostic factors in patients with node-negative lung adenocarcinoma 3 cm or smaller to find potential candidates for adjuvant chemotherapy.

METHODS

A total of 726 patients with completely resected node-negative lung adenocarcinoma 3 cm or smaller were included in the study. Prognostic factors for overall survival or probability of freedom from recurrence (FFR) were investigated.

RESULTS

During follow-up, recurrence developed in 59 patients (8.1%). Univariate analysis showed that the micropapillary/solid predominant pattern group was associated with a significantly lower probability of FFR (p = 0.001) in node-negative lung adenocarcinoma 3 cm or smaller. Those with greater tumor size (p = 0.001) and the micropapillary/solid predominant pattern group (p = 0.035) had a significantly lower probability of FFR in multivariate analysis. For tumors 2 cm or smaller, the micropapillary/solid predominant pattern group had a trend toward a lower probability of FFR (p = 0.053) in multivariate analysis. Presence of the solid pattern was a prognostic factor for lower probability of FFR (p = 0.001) in multivariate analysis.

CONCLUSIONS

The new adenocarcinoma classification has significant impact on recurrence in node-negative lung adenocarcinoma 3 cm or smaller. Patients with the micropapillary/solid predominant pattern have a significantly higher risk for recurrence. For tumors 2 cm or smaller, presence of the solid pattern was a prognostic factor for higher probability of recurrence. This information is useful for patient stratification for adjuvant therapy.