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

LncRNA CARD8-AS1 suppresses lung adenocarcinoma progression by enhancing TRIM25-mediated ubiquitination of TXNRD1

Long non-coding RNAs (lncRNAs) play crucial roles in the tumorigenesis and progression of lung adenocarcinoma (LUAD). However, little was known about the role of lncRNAs in high-risk LUAD subtypes: micropapillary-predominant adenocarcinoma (MPA) and solid-predominant adenocarcinoma (SPA). In this study, we conducted a systematic screening of differentially expressed lncRNAs using RNA sequencing in 10 paired MPA/SPA tumor tissues and adjacent normal tissues. Consequently, 110 significantly up-regulated lncRNAs and 288 aberrantly down-regulated lncRNAs were identified (|Log2 Foldchange| ≥ 1 and corrected P < 0.05). The top 10 lncRNAs were further analyzed in 89 MPA/SPA tumor tissues and 59 normal tissues from The Cancer Genome Atlas database. Among them, CARD8-AS1 showed the most significant differential expression, and decreased expression of CARD8-AS1 was significantly associated with a poorer prognosis. Functionally, CARD8-AS1 overexpression remarkably suppressed the proliferation, migration and invasion of LUAD cells both in vitro and in vivo. Conversely, inhibition of CARD8-AS1 yielded opposite effects. Mechanistically, CARD8-AS1 acted as a scaffold to facilitate the interaction between TXNRD1 and E3 ubiquitin ligase TRIM25, thereby promoting the degradation of TXNRD1 through the ubiquitin-proteasome pathway. Additionally, TXNRD1 was found to promote LUAD cell proliferation, migration and invasion in vitro. Furthermore, the suppressed progression of LUAD cells resulting from CARD8-AS1 overexpression could be significantly reversed by simultaneous overexpression of TXNRD1. In conclusion, this study revealed that the lncRNA CARD8-AS1 played a suppressive role in the progression of LUAD by enhancing TRIM25-mediated ubiquitination of TXNRD1. The CARD8-AS1-TRIM25-TXNRD1 axis may represent a promising therapeutic target for LUAD.

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.

Intratumoural and peritumoural CT-based radiomics for diagnosing lepidic-predominant adenocarcinoma in patients with pure ground-glass nodules: a machine learning approach

AIM

To develop and validate a diagnostic model utilising machine-learning algorithms that differentiates lepidic predominant adenocarcinoma (LPA) from other pathological subtypes in patients with pure ground-glass nodules (pGGNs).

MATERIALS AND METHODS

This bicentric study was conducted across two medical centres and included 151 patients diagnosed with lung adenocarcinoma based on histopathological confirmation of pGGNs. The training cohort consisted of 99 patients from Institution 1, while the test cohort included 52 patients from Institution 2. Radiomics features were extracted from both tumours and the 2 mm peritumoural parenchyma. The tumoural and peritumoural radiomics were designated as Modeltumoural and Modelperitumoural, respectively. The diagnostic efficacy of various models was evaluated through the receiver operating characteristic (ROC) curve analysis. Subsequently, a machine-learning-based prediction model that combined Modeltumoural, Modelperitumoural, and Modelclinical-radiological was developed to differentiate LPA from other pathological subtypes in patients with pGGNs.

RESULTS

Modeltumoural achieved area under the curve (AUC) values of 0.762 and 0.783 in the training and validation sets, respectively. Modelperitumoural attained AUCs of 0.742 and 0.667, and Modelclinical-radiological generated an AUC of 0.727 and 0.739 in the training and validation sets, respectively. Among the machine-learning models evaluated, gradient boosting machines demonstrated the best diagnostic efficacy, with accuracy, AUC, F1 score, and log loss values of 0.885, 0.956, 0.943, and 0.260, respectively.

CONCLUSION

The combined model based on machine learning that incorporated tumour and peritumoural parenchyma, as well as clinical and imaging characteristics, may offer benefits in assessing the pathological subtype of pGGNs.

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.

A nomogram for predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules: incorporating subjective CT signs and histogram parameters based on artificial intelligence

PURPOSE

To construct a nomogram based on subjective CT signs and artificial intelligence (AI) histogram parameters to identify invasiveness of lung adenocarcinoma presenting as pure ground-glass nodules (pGGNs) and to evaluate its diagnostic performance.

METHODS

187 patients with 228 pGGNs confirmed by postoperative pathology were collected retrospectively and divided into pre-invasive group [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS)] and invasive group [minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC)]. All pGGNs were randomly assigned to training cohort (n = 160) and validation cohort (n = 68). Nomogram was developed using subjective CT signs and AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) curve.

RESULTS

The nomogram was constructed with nodule shape, 3D mean diameter, maximum CT value, and skewness. It showed better discriminative power in differentiating invasive lesions from pre-invasive lesions with area under curve (AUC) of 0.849 (95% CI 0.790-0.909) in the training cohort and 0.831 (95% CI 0.729-0.934) in the validation cohort, which performed better than nodule shape (AUC 0.675, 95% CI 0.609-0.741), 3D mean diameter (AUC 0.762, 95% CI 0.688-0.835), maximum CT value (AUC 0.794, 95% CI 0.727-0.862), or skewness (AUC 0.594, 95% CI 0.506-0.682) alone in training cohort (for all, P < 0.05).

CONCLUSION

For pulmonary pGGNs, the nomogram based on subjective CT signs and AI histogram parameters had a good predictive ability to discriminate invasive lung adenocarcinoma from pre-invasive lung adenocarcinoma, and it has the potential to improve diagnostic efficiency and to help the patient management.

Artificial intelligence-based radiomics for the prediction of nodal metastasis in early-stage lung cancer

We aimed to investigate the value of computed tomography (CT)-based radiomics with artificial intelligence (AI) in predicting pathological lymph node metastasis (pN) in patients with clinical stage 0-IA non-small cell lung cancer (c-stage 0-IA NSCLC). This study enrolled 720 patients who underwent complete surgical resection for c-stage 0-IA NSCLC, and were assigned to the derivation and validation cohorts. Using the AI software Beta Version (Fujifilm Corporation, Japan), 39 AI imaging factors, including 17 factors from the AI ground-glass nodule analysis and 22 radiomics features from nodule characterization analysis, were extracted to identify factors associated with pN. Multivariate analysis showed that clinical stage IA3 (p = 0.028), solid-part size (p < 0.001), and average solid CT value (p = 0.033) were independently associated with pN. The receiver operating characteristic analysis showed that the area under the curve and optimal cut-off values of the average solid CT value relevant to pN were 0.761 and -103 Hounsfield units, and the threshold provided sensitivity, specificity, and negative predictive values of 69%, 65%, and 94% in the entire cohort, respectively. Measuring the average solid-CT value of tumors for pN may have broad applications such as guiding individualized surgical approaches and postoperative treatment.

Artificial-intelligence-based computed tomography histogram analysis predicting tumor invasiveness of lung adenocarcinomas manifesting as radiological part-solid nodules

Background

Tumor invasiveness plays a key role in determining surgical strategy and patient prognosis in clinical practice. The study aimed to explore artificial-intelligence-based computed tomography (CT) histogram indicators significantly related to the invasion status of lung adenocarcinoma appearing as part-solid nodules (PSNs), and to construct radiomics models for prediction of tumor invasiveness.

Methods

We identified surgically resected lung adenocarcinomas manifesting as PSNs in Peking University People's Hospital from January 2014 to October 2019. Tumors were categorized as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) by comprehensive pathological assessment. The whole cohort was randomly assigned into a training (70%, n=832) and a validation cohort (30%, n=356) to establish and validate the prediction model. An artificial-intelligence-based algorithm (InferRead CT Lung) was applied to extract CT histogram parameters for each pulmonary nodule. For feature selection, multivariate regression models were built to identify factors associated with tumor invasiveness. Logistic regression classifier was used for radiomics model building. The predictive performance of the model was then evaluated by ROC and calibration curves.

Results

In total, 299 AIS/MIAs and 889 IACs were included. In the training cohort, multivariate logistic regression analysis demonstrated that age [odds ratio (OR), 1.020; 95% CI, 1.004-1.037; p=0.017], smoking history (OR, 1.846; 95% CI, 1.058-3.221; p=0.031), solid mean density (OR, 1.014; 95% CI, 1.004-1.024; p=0.008], solid volume (OR, 5.858; 95% CI, 1.259-27.247; p = 0.037), pleural retraction sign (OR, 3.179; 95% CI, 1.057-9.559; p = 0.039), variance (OR, 0.570; 95% CI, 0.399-0.813; p=0.002), and entropy (OR, 4.606; 95% CI, 2.750-7.717; p<0.001) were independent predictors for IAC. The areas under the curve (AUCs) in the training and validation cohorts indicated a better discriminative ability of the histogram model (AUC=0.892) compared with the clinical model (AUC=0.852) and integrated model (AUC=0.886).

Conclusion

We developed an AI-based histogram model, which could reliably predict tumor invasiveness in lung adenocarcinoma manifesting as PSNs. This finding would provide promising value in guiding the precision management of PSNs in the daily practice.

The prognostic relationship of 18F-FDG PET/CT metabolic and volumetric parameters in metastatic ALK + NSCLC

OBJECTIVE

The aim of this study is to determine the role of metabolic and volumetric parameters obtained from 18Fluorine-Fluorodeoxyglucose PET/computed tomography (18F-FDG PET/CT) imaging on progression-free survival (PFS) and overall survival (OS) in patients with advanced nonsquamous cell lung carcinoma (NSCLC) with anaplastic lymphoma kinase (ALK) rearrangement.

METHODS

Pre and post-treatment PET/CT images of the ALK + NSCLC patients between January 2015 and July 2020 were evaluated. The highest standardized uptake value (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) values were obtained from pre-tyrosine kinase inhibitor (TKI) basal PET/CT (PETpre) and post-TKI PET/CT (PETpost) images. Total MTV (tMTV) and total TLG (tTLG) values were calculated by summing MTV and TLG values in all tumor foci. The change (Δ) in pSUVmax, pMTV, pTLG, tMTV and tTLG before and after treatment was calculated.The relationship of these parameters with OS and PFS was analyzed.

RESULTS

tTLGpre, tMTVpre, pTLGpre, pMTVpre, ∆SUVmax, ∆tMTV and ∆tTLG values were found to be associated with OS; ∆tMTV, ∆tTLG, tTLGpre, tMTVpre, pTLGpre and pMTVpre were associated with PFS. The cutoff values in both predicting OS and PFS were calculated as -31.6 and 391.1 for ∆tMTV and tTLGpre, respectively. In Cox regression analysis, ∆tMTV and stage for OS and ∆tMTV and tTLGpre for PFS were obtained as prognostic factors.

CONCLUSIONS

Metabolic and volumetric parameters, especially TLG values in the whole body before treatment and change in whole body MTV value, obtained from PET/CT may be useful in predicting prognosis and determining treatment strategies for patients with advanced ALK + NSCLC.

Study on the Correlation Between CT Features and Vascular Tumor Thrombus Together With Nerve Invasion in Surgically Resected Lung Adenocarcinoma

Background

We aimed to analyze the relationship between pulmonary adenocarcinoma patients with vascular tumor thrombus and nerve invasion and different CT features.

Methods

The preoperative CT scanning data of 86 patients with lung adenocarcinoma who underwent surgical resection in our hospital from January 2020 to January 2022 were analyzed in the form of retrospective analysis. The CT images of all patients were observed, and the relationship between them and vascular tumor thrombus and nerve invasion of lung adenocarcinoma was analyzed. At the same time, the sensitivity, specificity, and accuracy of enhanced CT and plain CT were compared to evaluate the diagnostic efficacy of both.

Results

The results showed that the vascular tumor thrombus of lung adenocarcinoma was mainly related to the solid components and lobulated and calcified tumors in CT images, and the nerve invasion of lung adenocarcinoma was mainly related to the tumors with bronchial inflation sign in CT images (P < 0.05). The sensitivity, specificity, and accuracy of enhanced CT in the diagnosis of vascular tumor thrombus were 78.26%, 96.83%, and 91.86%, respectively, and the sensitivity, specificity, and accuracy in the diagnosis of nerve invasion were 75.00%, 98.72%, and 96.51%, respectively. The sensitivity, specificity, and accuracy of plain CT in the diagnosis of vascular tumor thrombus were 43.48%, 92.06%, and 79.07%, respectively, and the sensitivity, specificity, and accuracy in the diagnosis of nerve invasion were 25.00%, 94.87%, and 88.37%, respectively. The contrast showed that the sensitivity and accuracy of enhanced CT were higher than those of plain CT (P < 0.05), but the difference of specificity was not obvious (P > 0.05).

Conclusions

Solid components and lobulated and calcified tumors in CT signs are closely related to vascular tumor thrombus of lung adenocarcinoma, while patients with bronchial inflation sign are related to nerve invasion.

Radiomics for identifying lung adenocarcinomas with predominant lepidic growth manifesting as large pure ground-glass nodules on CT images

Purpose

To explore the value of radiomics in the identification of lung adenocarcinomas with predominant lepidic growth in pure ground-glass nodules (pGGNs) larger than 10 mm.

Methods

We retrospectively analyzed CT images of 204 patients with large pGGNs (≥ 10 mm) pathologically diagnosed as minimally invasive adenocarcinomas (MIAs), lepidic predominant adenocarcinomas (LPAs), and non-lepidic predominant adenocarcinomas (NLPAs). All pGGNs in the two groups (MIA/LPA and NLPA) were randomly divided into training and test cohorts. Forty-seven patients from another center formed the external validation cohort. Baseline features, including clinical data and CT morphological and quantitative parameters, were collected to establish a baseline model. The radiomics model was built with the optimal radiomics features. The combined model was developed using the rad_score and independent baseline predictors. The performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC) and compared using the DeLong test. The differential diagnosis performance of the models was compared with three radiologists (with 20+, 10+, and 3 years of experience) in the test cohort.

Results

The radiomics (training AUC: 0.833; test AUC: 0.804; and external validation AUC: 0.792) and combined (AUC: 0.849, 0.820, and 0.775, respectively) models performed better for discriminating than the baseline model (AUC: 0.756, 0.762, and 0.725, respectively) developed by tumor location and mean CT value of the whole nodule. The DeLong test showed that the AUCs of the combined and radiomics models were significantly increased in the training cohort. The highest AUC value of the radiologists was 0.600.

Conclusion

The application of CT radiomics improved the identification performance of lung adenocarcinomas with predominant lepidic growth appearing as pGGNs larger than 10 mm.

Combination of mean CT value and maximum CT value as a novel predictor of lepidic predominant lesions in small lung adenocarcinoma presenting as solid nodules

Lung adenocarcinomas presenting as solid nodules are occasionally diagnosed as lepidic predominant lesions. The aim of this study was to clarify the histological structure and to identify factors predictive of lepidic predominant lesions. We retrospectively reviewed 38 patients that underwent lobectomy for small (≤ 2 cm) adenocarcinoma presenting as solid nodules. Resected tumor slides were reviewed and histological components were evaluated. Clinical and radiological data were analyzed to identify factors predictive of lepidic predominant lesions. Of 38 solid nodules, 9 (23.7%) nodules were lepidic predominant lesions. Five-year disease-free survival (DFS) rates were 100% for lepidic predominant lesions (n = 9) and 74.6% for non-lepidic predominant lesions (n = 29). Mean CT values (p = 0.039) and maximum CT values (p = 0.015) were significantly lower in lepidic predominant lesions compared with non-lepidic predominant lesions. For the prediction of lepidic predominant lesions, the sensitivity and specificity of mean CT value (cutoff, - 150 HU) were 77.8% and 82.8%, respectively, and those of maximum CT value (cutoff, 320 HU) were 77.8% and 72.4%, respectively. A combination of mean and maximum CT values (cutoffs of - 150 HU and 380 HU for mean CT value and maximum CT value, respectively) more accurately predicted lepidic predominant lesions, with a sensitivity and specificity of 77.8% and 86.2%, respectively. The prognosis of lepidic predominant lesions was excellent, even for solid nodules. The combined use of mean and maximum CT values was useful for predicting lepidic predominant lesions, and may help predict prognosis.

Relationship between 18F-FDG PET/CT Semi-Quantitative Parameters and International Association for the Study of Lung Cancer, American Thoracic Society/European Respiratory Society Classification in Lung Adenocarcinomas

Objective

To investigate the relationship between ¹⁸F-FDG PET/CT semi-quantitative parameters and the International Association for the Study of Lung Cancer, American Thoracic Society/European Respiratory Society (IASLC/ATS/ERS) histopathologic classification, including histological subtypes, proliferation activity, and somatic mutations.

Materials and Methods

This retrospective study included 419 patients (150 males, 269 females; median age, 59.0 years; age range, 23.0–84.0 years) who had undergone surgical removal of stage IA–IIIA lung adenocarcinoma and had preoperative PET/CT data of lung tumors. The maximum standardized uptake values (SUVmax), background-subtracted volume (BSV), and background-subtracted lesion activity (BSL) derived from PET/CT were measured. The IASLC/ATS/ERS subtypes, Ki67 score, and epidermal growth factor/anaplastic lymphoma kinase (EGFR/ALK) mutation status were evaluated. The PET/CT semi-quantitative parameters were compared between the tumor subtypes using the Mann–Whitney U test or the Kruskal–Wallis test. The optimum cutoff values of the PET/CT semi-quantitative parameters for distinguishing the IASLC/ATS/ERS subtypes were calculated using receiver operating characteristic curve analysis. The correlation between the PET/CT semi-quantitative parameters and pathological parameters was analyzed using Spearman’s correlation. Statistical significance was set at p < 0.05.

Results

SUVmax, BSV, and BSL values were significantly higher in invasive adenocarcinoma (IA) than in minimally IA (MIA), and the values were higher in MIA than in adenocarcinoma in situ (AIS) (all p < 0.05). Remarkably, an SUVmax of 0.90 and a BSL of 3.62 were shown to be the optimal cutoff values for differentiating MIA from AIS, manifesting as pure ground-glass nodules with 100% sensitivity and specificity. Metabolic-volumetric parameters (BSV and BSL) were better potential independent factors than metabolic parameters (SUVmax) in differentiating growth patterns. SUVmax and BSL, rather than BSV, were strongly or moderately correlated with Ki67 in most subtypes, except for the micropapillary and solid predominant groups. PET/CT parameters were not correlated with EGFR/ALK mutation status.

Conclusion

As noninvasive surrogates, preoperative PET/CT semi-quantitative parameters could imply IASLC/ATS/ERS subtypes and Ki67 index and thus may contribute to improved management of precise surgery and postoperative adjuvant therapy.

Which will carry more weight when CTR > 0.5, solid component size, CTR, tumor size or SUVmax?

BACKGROUND

This study was conducted to explore the clinical significance of the maximum standard uptake value (SUVmax) in the clinical stage IA lung adenocarcinoma with tumor size ≤ 2 cm and consolidation to tumor ratio (CTR) > 0.5.

METHODS

We retrospectively reviewed non-small cell lung cancer patients who underwent surgeries between January 2014 and March 2017. Clinical stage IA lung adenocarcinoma patients with tumor of size ≤ 2 cm and CTR > 0.5 were enrolled. The patients were divided into two groups: part-solid and pure-solid based on whether CTR = 1.0 or not. Nodules with any amount of solid or micropapillary components were regarded as the high-risk subtype. Time-dependent ROC curve was used to determine the best cut-off value. Finally, we analyzed the relationship between SUVmax, high-risk subtypes, node metastasis and 5-year relapse-free survival and overall survival.

RESULTS

Totally, 270 patients were included. The distribution of pathological subtypes (p < 0.001), SUVmax (p < 0.001), and pathological N stage (p < 0.001) were different between the two groups. Multivariable analysis indicated that SUVmax could predict high-risk subtypes in cases of part-solid nodules (p < 0.001) and both high-risk subtypes (p = 0.022) and node metastasis (p < 0.001) in cases of pure-solid ones. SUVmax ≥ 2.6 and SUVmax ≥ 5.1 were strongly associated with 5-year relapse-free survival (p < 0.001) and 5-year overall survival (p < 0.001) among all the patients, respectively.

CONCLUSION

Part-solid nodules with 0.5 < CTR < 1 and pure-solid nodules in lung adenocarcinoma show different clinicopathological characteristics, especially in SUVmax. SUVmax is significantly associated with high-risk subtypes, node metastasis, 5-year relapse-free survival and overall survival.

Histogram analysis of diffusion-weighted magnetic resonance imaging as a biomarker to predict LNM in T3 stage rectal carcinoma

BACKGROUND

Preoperative identification of rectal cancer lymph node status is crucial for patient prognosis and treatment decisions. Rectal magnetic resonance imaging (MRI) plays an essential role in the preoperative staging of rectal cancer, but its ability to predict lymph node metastasis (LNM) is insufficient. This study explored the value of histogram features of primary lesions on multi-parametric MRI for predicting LNM of stage T3 rectal carcinoma.

METHODS

We retrospectively analyzed 175 patients with stage T3 rectal cancer who underwent preoperative MRI, including diffusion-weighted imaging (DWI) before surgery. 62 patients were included in the LNM group, and 113 patients were included in the non-LNM group. Texture features were calculated from histograms derived from T2 weighted imaging (T2WI), DWI, ADC, and T2 maps. Stepwise logistic regression analysis was used to screen independent predictors of LNM from clinical features, imaging features, and histogram features. Predictive performance was evaluated by receiver operating characteristic (ROC) curve analysis. Finally, a nomogram was established for predicting the risk of LNM.

RESULTS

The clinical, imaging and histogram features were analyzed by stepwise logistic regression. Preoperative carbohydrate antigen 199 level (p = 0.009), MRN stage (p < 0.001), _T2WIKurtosis (p = 0.010), _DWIMode (p = 0.038), _DWICV (p = 0.038), and _T2-mapP5 (p = 0.007) were independent predictors of LNM. These factors were combined to form the best predictive model. The model reached an area under the ROC curve (AUC) of 0.860, with a sensitivity of 72.8% and a specificity of 85.5%.

CONCLUSION

The histogram features on multi-parametric MRI of the primary tumor in rectal cancer were related to LN status, which is helpful for improving the ability to predict LNM of stage T3 rectal cancer.

Investigating the association between ground-glass nodules glucose metabolism and the invasive growth pattern of early lung adenocarcinoma

Background

To explore the association between the glucose metabolism level of lung ground-glass nodules (GGNs), as revealed by ¹⁸F-flurodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) imaging, and the invasive pathological growth pattern of early lung adenocarcinoma.

Methods

We retrospectively analyzed patients who underwent PET/CT examination and surgical resection due to persistent GGNs, which were confirmed to be early lung adenocarcinoma by postoperative pathology examination. After adjusting for confounding factors and performing stratified analysis, we explored the association between the maximum standard uptake value of PET (SUVmax) and the invasive pathological growth pattern of early stage lung adenocarcinoma.

Results

The proportions of invasive adenocarcinoma (INV) in the SUVmax of Tertile 1, Tertile 2, and Tertile 3 were 52.7%, 73.3%, and 87.1%, respectively. After adjusting for potential confounding factors, the risk of INV gradually increased as the GGN SUVmax increased [odds ratio (OR): 1.520, 95% confidence interval (CI): 1.044-2.213, P=0.029]. This trend was statistically significant (OR: 1.678, 95% CI: 1.064-2.647, P=0.026), especially in Tertile 3 vs. Tertile 1 (OR: 4.879, 95% CI: 1.349-17.648, P=0.016). Curve fitting showed that the SUVmax and INV risk were linearly and positively associated. The association was consistent in different subgroups based on GGN number, type, shape, edge, bronchial sign, vacuole sign, pleural depression sign, diameters, and consolidation-to-tumor ratio, suggesting that there was no significant interaction between different grouping parameters and the association (P for interaction range = 0.129-0.909).

Conclusions

In FDG PET, the glucose metabolism level (SUVmax) of lung GGNs is independently associated with INV risk, and this association is linear and positive.

[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。

[Progress in Single-cell RNA Sequencing of Lung Adenocarcinoma]

肺腺癌（lung adenocarcinoma, LUAD）是临床上肺癌最常见的亚型，是癌症相关死亡最主要的原因之一。过去十几年中，随着薄层计算机断层扫描（computed tomography, CT）广泛用于常规肺癌筛查，影像学上表现为小结节的LUAD发病率显著增高，其发生发展机制复杂，个体预后差异显著。尽管近年来针对LUAD的靶向和免疫疗法取得了重大进展，但肿瘤细胞的耐药性始终未得到有效解决，从而限制了患者获益。随着人类基因组计划的完成，以测序为基本手段的基因组学及转录组学进入临床和科研人员的视野。单细胞测序作为近年来受到高度关注的新型测序手段，与二代测序相比，其能在单细胞水平上对细胞群体进行特异性分析，揭示出每种细胞类型独特的变化，在单细胞水平上对许多异质基质细胞和癌细胞进行较精准地评估，从而揭示了分子成分的复杂性以及与非恶性组织中相应成分的区别。综上，通过单细胞测序深入了解LUAD发生发展机制和肿瘤微环境（tumor microenvironment, TME）的异质性及其耐药性形成机制，从而发现新的治疗靶点是临床医生和基础科学家迫切的需求。本文综合论述了单细胞测序在LUAD中的具体应用和研究进展。

Managing screening-detected subsolid nodules-the Asian perspective

The broad application of low-dose computed tomography (CT) screening has resulted in the detection of many small pulmonary nodules. In Asia, a large number of these detected nodules with a radiological ground glass pattern are reported as lung adenocarcinomas or premalignant lesions, especially among female non-smokers. In this review article, we discuss controversial issues and conditions involving these subsolid pulmonary nodules that we often face in Asia, including a lack or insufficiency of current guidelines; the roles of preoperative biopsy and imaging; the location of lesions; appropriate selection of localization techniques; the roles of dissection and sampling of frozen sections and lymph nodes; multifocal lesions; and the roles of non-surgical treatment modalities. For these complex issues, we have tried to present up-to-date evidence and our own opinions regarding the management of subsolid nodules. It is our hope that this article helps surgeons and physicians to manage the complex issues involving ground glass nodules (GGNs) in a balanced manner in their daily practice and provokes further discussion towards better guidelines and/or algorithms.

Management of pulmonary ground-glass opacities: a position paper from a panel of experts of the Italian Society of Thoracic Surgery (SICT)

OBJECTIVES

A significant gap in our knowledge of how to manage pulmonary ground-glass opacities (GGOs) still exists. Accordingly, there is a lack of consensus among clinicians on this topic. The Italian Society of Thoracic Surgery (Società Italiana di Chirurgia Toracica, SICT) promoted a national expert meeting to provide insightful guidance for clinical practice. Our goal was to publish herein the final consensus document from this conference.

METHODS

The working panel of the PNR group (Pulmonary Nodules Recommendation Group, a branch of the SICT) together with 5 scientific supervisors (nominated by the SICT) identified a jury of expert thoracic surgeons who organized a multidisciplinary meeting to propose specific statements (n = 29); 73 participants discussed and voted on statements using a modified Delphi process (repeated iterations of anonymous voting over 2 rounds with electronic support) requiring 70% agreement to reach consensus on a statement.

RESULTS

Consensus was reached on several critical points in GGO management, in particular on the definition of GGO, radiological and radiometabolic evaluation, indications for a non-surgical biopsy, GGO management based on radiological characteristics, surgical strategies (extension of pulmonary resection and lymphadenectomy) and radiological surveillance. A list of 29 statements was finally approved.

CONCLUSIONS

The participants at this national expert meeting analysed this challenging topic and provided a list of suggestions for health institutions and physicians with practical indications for GGO management.

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.

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.

Imaging patterns of early stage lung cancer for the thoracic surgeon

In the modern era, thoracic surgeons are experiencing an increase interest in imaging patterns of early stage lung cancer due to the introduction of the ground glass opacity in clinical practice, and for the necessity to an accurate cancer localization to perform the appropriate type of resection. In this brief review we analyze the latest news regarding imaging patterns of early pulmonary nodules with special emphasis to ground glass opacity.

18F-FDG PET/CT in lung adenosquamous carcinoma and its correlation with clinicopathological features and prognosis

OBJECTIVE

Lung adenosquamous carcinoma (ASC) is a rare histological subtype of non-small cell lung cancer (NSCLC). Due to its rarity, the studies about ¹⁸F-FDG PET/CT in this kind of pulmonary tumor were quite limited. Thus, this study investigated ¹⁸F-FDG PET/CT findings in ASC and its correlation with clinicopathological features and clinical outcomes.

METHODS

Preoperative ¹⁸F-FDG PET/CT findings and parameters of maximum standard uptake value (SUV_max), metabolic tumor volume and total lesion glycolysis of primary lesion (MTV-P, TLG-P), combination of primary lesion and metastases (MTV-C, TLG-C), and clinicopathological features were retrospectively investigated in patients with ASC. Moreover, progression-free survival (PFS) was also analyzed.

RESULTS

All 41 patients (25 men; 16 women; age: 60 ± 7 years) had single ASC with the mean diameter of 33 ± 14 mm. Six lesions were located centrally and 35 peripherally. Serum tumor markers were abnormally increased sporadically. Twenty-two cases were at TNM stage I, 9 at II, and 10 at III. The primary tumors were FDG-avid in all cases, with the average SUV_max of 11.5 ± 6.0. SUV_max was significantly associated with tumor location, size, and TNM stage (P < 0.05). Forty-one lesions were subgrouped into 23 AC-predominant and 18 SCC-predominant lesions, and significant differences were observed for age, tumor size, and SUV_max in two groups (P < 0.05). The median PFS of 41 cases was 19 months, and 12-month and 24-month PFS rates were 72.1% and 36.1%, respectively. SUV_max, MTV-P, and TLG-C were significantly associated with PFS (P < 0.05).

CONCLUSIONS

ASC of the lung displayed high SUV_max on ¹⁸F-FDG PET/CT, which was associated with tumor location, size, TNM stage, and predominant histologic component. Moreover, metabolic parameters of ¹⁸F-FDG PET/CT were independent prognostic factors of this rare lung malignancy.

Computed Tomography Histogram Approach to Predict Lymph Node Metastasis in Patients With Clinical Stage IA Lung Cancer

BACKGROUND

Quantitative computed tomography (CT) histogram analysis of tumors is reported to help distinguish between invasive and less invasive lung cancers. This study aimed to clarify whether CT histogram analysis of tumors can be used to classify patients with clinical stage 0 to IA non-small cell lung cancer according to pathologic lymph node (pN) status.

METHODS

Predictive factors associated with pN metastasis were identified from the derivation dataset including 629 patients with clinical stage 0 to IA non-small cell lung cancer who underwent complete resection with lymph node dissection (surgeries between 2008 and 2013). The validation dataset including 238 patients (surgeries between 2014 and 2015) were subsequently reevaluated. Clinicosurgical factors, including CT histogram analysis of tumors (CT value percentiles 2.5, 25, 50, 75, and 97.5, skewness, and kurtosis) were assessed.

RESULTS

Seventy-three patients (12%) in the derivation cohort and 35 patients (15%) in the validation cohort had positive nodes. The pN status significantly affected survival in the entire population: 5-year overall survival of 93.1% vs 71.1% and 5-year disease-free survival of 85.9% vs 43.1% for negative vs positive (both P < .001). On multivariate analysis in the derivation cohort, the 75th percentile CT value (P < .001), age (P = .003), and comorbidities (P = .006) were significantly associated with pN metastasis. The area under the curve and the cutoff level of the 75th percentile CT value relevant to pN metastasis were 0.729 and 1.5 HU, respectively, and the threshold value provided accuracy of 71% for the validation cohort.

CONCLUSIONS

Histogram analysis of CT imaging metrics of tumors contributes to noninvasive prediction of pN metastasis in patients with clinical stage 0 to IA non-small cell lung cancer.