Predictors of recurrence and survival of pathological T1N0M0 invasive adenocarcinoma following lobectomy

Deep-learning-based 3D super-resolution CT radiomics model: Predict the possibility of the micropapillary/solid component of lung adenocarcinoma

Objective

Invasive lung adenocarcinoma（ILA） with micropapillary (MPP)/solid (SOL) components has a poor prognosis. Preoperative identification is essential for decision-making for subsequent treatment. This study aims to construct and evaluate a super-resolution（SR） enhanced radiomics model designed to predict the presence of MPP/SOL components preoperatively to provide more accurate and individualized treatment planning.

Methods

Between March 2018 and November 2023, patients who underwent curative intent ILA resection were included in the study. We implemented a deep transfer learning network on CT images to improve their resolution, resulting in the acquisition of preoperative super-resolution CT (SR-CT) images. Models were developed using radiomic features extracted from CT and SR-CT images. These models employed a range of classifiers, including Logistic Regression (LR), Support Vector Machines (SVM), k-Nearest Neighbors (KNN), Random Forest, Extra Trees, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Multilayer Perceptron (MLP). The diagnostic performance of the models was assessed by measuring the area under the curve (AUC).

Result

A total of 245 patients were recruited, of which 109 (44.5 %) were diagnosed with ILA with MPP/SOL components. In the analysis of CT images, the SVM model exhibited outstanding effectiveness, recording AUC scores of 0.864 in the training group and 0.761 in the testing group. When this SVM approach was used to develop a radiomics model with SR-CT images, it recorded AUCs of 0.904 in the training and 0.819 in the test cohorts. The calibration curves indicated a high goodness of fit, while decision curve analysis (DCA) highlighted the model's clinical utility.

Conclusion

The study successfully constructed and evaluated a deep learning（DL）-enhanced SR-CT radiomics model. This model outperformed conventional CT radiomics models in predicting MPP/SOL patterns in ILA. Continued research and broader validation are necessary to fully harness and refine the clinical potential of radiomics when combined with SR reconstruction technology.

A combination of radiomic features, clinic characteristics, and serum tumor biomarkers to predict the possibility of the micropapillary/solid component of lung adenocarcinoma

BACKGROUND

Invasive lung adenocarcinoma with MPP/SOL components has a poor prognosis and often shows a tendency to recurrence and metastasis. This poor prognosis may require adjustment of treatment strategies. Preoperative identification is essential for decision-making for subsequent treatment.

OBJECTIVE

This study aimed to preoperatively predict the probability of MPP/SOL components in lung adenocarcinomas by a comprehensive model that includes radiomics features, clinical characteristics, and serum tumor biomarkers.

DESIGN

A retrospective case control, diagnostic accuracy study.

METHODS

This study retrospectively recruited 273 patients (males: females, 130: 143; mean age ± standard deviation, 63.29 ± 10.03 years; range 21-83 years) who underwent resection of invasive lung adenocarcinoma. Sixty-one patients (22.3%) were diagnosed with lung adenocarcinoma with MPP/SOL components. Radiomic features were extracted from CT before surgery. Clinical, radiomic, and combined models were developed using the logistic regression algorithm. The clinical and radiomic signatures were integrated into a nomogram. The diagnostic performance of the models was evaluated using the area under the curve (AUC). Studies were scored according to the Radiomics Quality Score and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines.

RESULTS

The radiomics model achieved the best AUC values of 0.858 and 0.822 in the training and test cohort, respectively. Tumor size (T_size), solid tumor size (ST_size), consolidation-to-tumor ratio (CTR), years of smoking, CYFRA 21-1, and squamous cell carcinoma antigen were used to construct the clinical model. The clinical model achieved AUC values of 0.741 and 0.705 in the training and test cohort, respectively. The nomogram showed higher AUCs of 0.894 and 0.843 in the training and test cohort, respectively.

CONCLUSION

This study has developed and validated a combined nomogram, a visual tool that integrates CT radiomics features with clinical indicators and serum tumor biomarkers. This innovative model facilitates the differentiation of micropapillary or solid components within lung adenocarcinoma and achieves a higher AUC, indicating superior predictive accuracy.

Establishment and visualization of a model based on high-resolution CT qualitative and quantitative features for prediction of micropapillary or solid components in invasive lung adenocarcinoma

OBJECTIVE

To predict the existence of micropapillary or solid components in invasive adenocarcinoma, a model was constructed using qualitative and quantitative features in high-resolution computed tomography (HRCT).

METHODS

Through pathological examinations, 176 lesions were divided into two groups depending on the presence or absence of micropapillary and/or solid components (MP/S): MP/S- group (n = 128) and MP/S + group (n = 48). Multivariate logistic regression analyses were used to identify independent predictors of the MP/S. Artificial intelligence (AI)-assisted diagnostic software was used to automatically identify the lesions and extract corresponding quantitative parameters on CT images. The qualitative, quantitative, and combined models were constructed according to the results of multivariate logistic regression analysis. The receiver operating characteristic (ROC) analysis was conducted to evaluate the discrimination capacity of the models with the area under the curve (AUC), sensitivity, and specificity calculated. The calibration and clinical utility of the three models were determined using the calibration curve and decision curve analysis (DCA), respectively. The combined model was visualized in a nomogram.

RESULTS

The multivariate logistic regression analysis using both qualitative and quantitative features indicated that tumor shape (P =  0.029 OR =  4.89; 95% CI 1.175-20.379), pleural indentation (P = 0.039 OR =  1.91; 95% CI 0.791-4.631), and consolidation tumor ratios (CTR) (P < 0.001; OR = 1.05; 95% CI 1.036-1.070) were independent predictors for MP/S + . The areas under the curve (AUC) of the qualitative, quantitative, and combined models in predicting MP/S + were 0.844 (95% CI 0.778-0.909), 0.863 (95% CI 0.803-0.923), and 0.880 (95% CI 0.824-0.937). The combined model of AUC was the most superior and statistically better than qualitative model.

CONCLUSION

The combined model could assist doctors to evaluate patient's prognoses and devise personalized diagnostic and treatment protocols for patients.

A Combination of Radiomic Features, Imaging Characteristics, and Serum Tumor Biomarkers to Predict the Possibility of the High-Grade Subtypes of Lung Adenocarcinoma

RATIONALE AND OBJECTIVES

Lung adenocarcinomas (LADC) containing high-grade subtypes have a poorer prognosis. And some studies have shown that high-grade subtypes have been identified as an independent predictor of local recurrence in patients treated with limited resection. The aim of this study was to construct a combined model based on radiomic features, imaging characteristics and serum tumor biomarkers to predict the possibility of preoperative high-grade subtypes.

MATERIALS AND METHODS

156 patients with LADC were retrospectively recruited in this study. These patients were randomly divided into training and validation cohorts. Radiomics features and imaging characteristics were extracted from plain CT images. A nomogram was developed in a training cohort by univariate and multivariate logistic analysis, and its performance was evaluated by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) in the training and validation cohorts.

RESULTS

A total of 1316 radiomic features were extracted from the lesions in plain chest CT images. After applying the mRMR algorithm and the LASSO regression, 4 features were retained. Based on these radiomic features, Radiomic score (Radscore) was calculated for each patient. Spiculation, air bronchogram sign, CYFRA 21-1 and Radscore had been used in the construction of the combined model. The AUC of the combined model was respectively 0.88 (95% CI, 0.82-0.95) and 0.94 (95% CI, 0.86-1.00) in the training and validation cohorts.

CONCLUSION

The combined model based on CT images and serum tumor biomarkers, can predict the high-grade subtypes of LADC in a non-invasive manner, which may influence individual treatment planning, such as the choice of surgical approach and postoperative adjuvant therapy.

Association between quantitative spectral CT parameters, Ki-67 expression, and invasiveness in lung adenocarcinoma manifesting as ground-glass nodules

BACKGROUND

Few studies about lung ground-glass nodules (GGNs) have been done using non-enhancement spectral computed tomography (CT) imaging.

PURPOSE

To examine the association between spectral CT parameters, Ki-67 expression, and invasiveness in lung adenocarcinoma manifesting as GGNs.

MATERIAL AND METHODS

Spectral CT parameters were analyzed in 106 patients with lung GGNs. The Ki-67 labeling index (Ki-67 LI) was measured, and patients were divided into low expression and high expression groups according to the number of positive-stained cells (low expression ≤10%; high expression >10%). Spectral CT parameters were compared between low and high expression groups. The correlation between spectral CT parameters and Ki-67 LI was estimated by Spearman correlation analysis. Cases were divided into a preinvasive and minimally invasive adenocarcinoma (MIA) group (atypical adenomatous hyperplasia, adenocarcinoma in situ, and MIA) and invasive adenocarcinoma (IA) group. Spectral CT parameters were compared between the two groups. The diagnostic performance was evaluated using receiver operating characteristic analysis.

RESULTS

There were significant differences in water concentration of lesions (WCL) and monochromatic CT values between the low and high expression groups. CT 40 keV had the highest correlation coefficient with Ki-67 LI. WCL and monochromatic CT values were significantly higher in the IA group than in the pre/MIA group. The value of area under the curve of CT 40 keV was 0.946 (95% confidence interval=0.905-0.988) for differentiating the two groups; the cutoff was -280.66 Hu.

CONCLUSION

Spectral CT is an effective non-invasive method for the prediction of proliferation and invasiveness in lung adenocarcinoma manifesting as GGNs.

Establishment and Validation of a Predictive Nomogram for Postoperative Survival of Stage I Non-Small Cell Lung Cancer

Background

Surgical procedure is the preferred option for people with early-stage non-small cell lung cancer (NSCLC), while nearly 30% of patients experienced metastatic or recurrent tumor after operation. The primary intention of this context is to summarize high-risk prognostic factors and set up a novel nomogram to predict the overall survival of individuals with stage I NSCLC after resection.

Methods

Research objects, 10,218 patients with stage I NSCLC after operation from 2010 to 2015, were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors, confirmed by Cox regression analyses, were integrated into a nomogram, to predict the 3-and 5-year overall survival of these individuals. The model experienced internal validation of testing cohorts above and external validation crewed by 160 patients from China. Finally, the nomogram was evaluated through several verification methods such as concordance index (C-index), calibration plots and receiver operating characteristic curve (ROC).

Results

Multivariate analysis identified that age, gender, histologic type, differentiation class, type of operation, T stage and treatment were significant predictive factors for the survival of stage I NSCLC. Based on these factors, a nomogram was constructed to predict the 3- and 5-year overall survival of these individuals. Meanwhile, in the training set, this nomogram displayed excellent superiority over the TNM staging system with abroad application, especially in C-index (0.669 vs 0.580) and the AUC (the Area Under ROC Curve) for the 3- and 5-year survival (0.678 vs 0.582; 0.650 vs 0.576). In the calibration curve, the curve representing predicted survival tended to align with the line representing actual survival as well.

Conclusion

A nomogram was successfully created and verified to achieve the goal that made a rounded accurate prediction on the survival of postoperative I NSCLC patients in terms of the SEER database.

Genomics Analysis and Nomogram Risk Prediction of Occult Lymph Node Metastasis in Non-Predominant Micropapillary Component of Lung Adenocarcinoma Measuring ≤ 3 cm

Background

The efficacy of sublobar resection and selective lymph node dissection is gradually being accepted by thoracic surgeons for patients within early-stage non-small cell lung cancer (NSCLC). Nevertheless, there are still some NSCLC patients develop lymphatic metastasis at clinical T1 stage. Lung adenocarcinoma with a micropapillary (MP) component poses a higher risk of lymph node metastasis and recurrence even when the MP component is not predominant. Our study aimed to explore the genetic features and occult lymph node metastasis (OLNM) risk factors in patients with a non-predominant micropapillary component (NP-MPC) in a large of patient’s cohort with surgically resected lung adenocarcinoma.

Methods

Between January 2019 and December 2021, 6418 patients who underwent complete resection for primary lung adenocarcinoma at the Qilu Hospital of Shandong University. In our study, 442 patients diagnosed with lung adenocarcinoma with NP-MPC with a tumor size ≤3 cm were included. Genetic alterations were analyzed using amplification refractory mutation system-polymerase chain reaction (ARMS-PCR). Abnormal protein expression of gene mutations was validated using immunohistochemistry. A nomogram risk model based on clinicopathological parameters was developed to predict OLNM. This model was invalidated using the calibration plot and concordance index.

Results

In our retrospective cohort, the incidence rate of the micropapillary component was 11.17%, and OLNM was observed in 20.13% of the patients in our study. ARMS-PCR suggested that EGFR exon 19 del was the most frequent alteration in NP-MCP patients compared with other gene mutations (frequency: 21.2%, P<0.001). Patients harboring exon 19 del showed significantly higher risk of OLNM (P< 0.001). A nomogram was developed based on five risk parameters, which showed good calibration and reliable discrimination ability (C-index = 0.84) for evaluating OLNM risk.

Conclusions.

Intense expression of EGFR exon 19 del characterizes lung adenocarcinoma in patients with NP-MCP and it’s a potential risk factor for OLNM. We firstly established a nomogram based on age, CYFRA21-1 level, tumor size, micropapillary and solid composition, that was effective in predicting OLNM among NP-MCP of lung adenocarcinoma measuring ≤ 3 cm.

[Pattern of Recurrence and Metastasis after Radical Resection of Non-small Cell Lung Cancer]

The rate of recurrence and metastasis of non-small cell lung cancer after radical resection is still very high. The risk factors for recurrence and metastasis have been extensively studied, but the dynamic pattern of postoperative recurrence hazard over time is relatively lacking. The dynamic recurrence hazard rate curve is applied to describe the rate of recurrence at any point time among the "at-risk" patients. In this article, by reviewing the previous literature, the characteristics of the dynamic recurrence and metastasis pattern after radical resection of non-small cell lung cancer and the clinical factors affecting the recurrence and metastasis pattern are summarized, in order to screen out specific populations with high recurrence risk and give them personalized follow-up strategy and diagnosis and treatment.
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Different pulmonary adenocarcinoma growth patterns significantly affect survival

OBJECTIVE

Adenocarcinoma (AC) is the number one pathological entity of lung cancer with approximately 30-40% of cases. It is known to be heterogeneous and has 5 histopathological growth patterns. We evaluated the long-term survival rates of patients with predominant subtypes.

METHODS

290 patients with AC underwent pulmonary resection between 2012 and 2017 at our institution. We excluded all patients with lymph node involvement and distant metastases. Hence, 163 patients were included for further analysis. Predominant growth pattern was defined if more than 10% of cells showed a growth pattern. 1, 3, and 5-year survival rates were evaluated. Survival was assessed by Kaplan-Meier curves and the Cox proportional hazards model was used to identify prognostic factors for overall survival.

RESULTS

Predominant growth patterns >10% were compared to <10% growth patterns of the same subtype. 1-year, 3-year, and 5-year overall survival rates of patients with predominant solid tumor growth >10% differed significantly from patients with <10% (88.4% vs. 97.6%, p = 0.04; 65.8% vs. 87.4% p = 0.001, 36.4% vs. 65.9% p = 0.01). Survival rates did not differ between >10% papillary and acinar growth compared to <10%. Kaplan-Meier curves showed reduced overall survival for patients with solid tumor growth >10% (log-rank 0.002). Solid tumor growth >10% was an independent prognostic factor for worse long-term survival (Hazard ratio: 3.05, p = 0.01).

CONCLUSION

Our study demonstrates that the presence of a predominant solid pattern in pulmonary adenocarcinoma is a factor for an unfavorable prognosis. This should be kept in mind in daily clinical practice.

Different pathologic types of early stage lung adenocarcinoma have different post-operative recurrence patterns

Objectives

To accurately describe the pattern, timing and predictors of disease recurrence after curative resection for different types of early‐stage lung adenocarcinoma (LUAD).

Methods

A total of 1962 patients with early‐stage LUAD were included. The presence of micropapillary, solid components or poorly differentiated cancer as a clinical variable was named “high‐grade” adenocarcinoma (HGADC), while others were classified as “low‐grade” adenocarcinoma (LGADC). Predictive factors for specific recurrence patterns were assessed by univariate and multivariate analyses using Cox‐proportional hazard regression models. Event dynamics, based on the hazard rate, were evaluated.

Results

At a median follow‐up of 36.0 months, 137 (6.98%) of 1962 patients suffered from recurrence. Multivariable Cox analysis revealed that HGADC was an independent predictor for overall recurrence (hazard ratio [HR] 3.08, 95% confidence interval [CI] 2.09–4.52, p < 0.001), local recurrence (HR 2.77, 95% CI 1.38–5.55, p < 0.001), distant metastasis (HR 3.22, 95% CI 2.03–5.11, p < 0.001), chest recurrence (HR 2.80, 95% CI 1.65–4.75, p < 0.001) and brain recurrence (HR 4.11, 95% CI 1.83–9.22, p < 0.001). However, HGADC (HR 1.56, 95% CI 0.63–3.86, p = 0.335 in univariate analysis) was not a risk factor for bone recurrence. The hazard curve of the whole group presented a double‐peaked pattern. Different types of LUAD had different hazard curves. HGADC patients exhibited higher hazard rates than LGADC patients during the whole follow‐up. In addition, the recurrence hazard curve in HGADC patients showed a typical “double‐peaked” pattern, while the curve in LGADC patients displayed a smooth curve after surgery.

Conclusions

Different postoperative recurrence patterns were seen in HGADC and LGADC. Site‐specific recurrence patterns were also different in HGADC and LGADC types.

The Prognostic Value of Non-Predominant Micropapillary Pattern in a Large Cohort of Resected Invasive Lung Adenocarcinoma Measuring ≤3 cm

The aim of this study was to analyze the influence of non-predominant micropapillary pattern in small sized invasive lung adenocarcinoma. A total of 986 lung adenocarcinoma patients with tumor size ≤3 cm were identified and classified according to the IALSC/ATS/ERS classification. Emphasis was placed on the impact of non-predominant micropapillary pattern on disease-free survival (DFS) and overall survival (OS). The relationship between lung adenocarcinoma subtype and lymph node involvement, EGFR mutation and KRAS mutation was also evaluated. A nomogram was developed to predict the probability of 3- and 5-year OS for these patients. The concordance index and calibration plot were used to validate this model. Among all 986 patients, the percentages of lymph node involvement were: 58.1, 50.0, 33.5, 21.4, 21.1, 10.9, 0, and 0% for micropapillary predominant, solid predominant, acinar predominant, papillary predominant, invasive mucinous adenocarcinoma (IMA), lepidic predominant, minimally invasive adenocarcinoma (MIA), adenocarcinoma in situ (AIS), respectively. The frequency of EGFR mutation in the cases of lepidic predominant, acinar predominant, MIA, micropapillary predominant, papillary predominant, solid predominant, IMA, and AIS were 51.1, 45.2, 44.4, 36.8, 29.3, 26.8, 8.3, and 0%, respectively. A non-predominant micropapillary pattern was observed in 344 (38.4%) invasive adenocarcinoma (IAC), and its presence predicted a poorer DFS (median: 56.0 months vs. 66.0 months, P <0.001) and OS (median: 61.0 months vs. 70.0 months, P <0.001). After propensity score matching, non-predominant micropapillary pattern retained its unfavorable effect on DFS (P = 0.007) and OS (P = 0.001). Multivariate analysis showed that non-predominant micropapillary pattern was identified as an independent prognostic factor for DFS (P = 0.003) and OS (P <0.001) in IAC. The nomogram showed good calibration and reliable discrimination ability (C-index = 0.775) to evaluated the 3- and 5-year OS. This retrospective analysis of patients with small sized IAC suggests the value of non-predominant micropapillary pattern to predict poor prognosis. A reliable nomogram model was constructed to provide personalized survival predictions.

Clinical implication of minimal presence of solid or micropapillary subtype in early-stage lung adenocarcinoma

BACKGROUND

We investigated the clinical features and surgical outcomes of lung adenocarcinoma with minimal solid or micropapillary (S/MP) components, with a focus on stage IA.

METHODS

We enrolled 506 patients with lung adenocarcinoma who underwent curative resection in this study. Clinical features and surgical outcomes were compared between the groups with and without the S/MP subtype (S/MP+ and S/MP-, respectively), and between the group with an S/MP proportion of ≤5% (S/MP5) and the S/MP-.

RESULTS

The S/MP subtype was present in 247 patients (48.8%); 129 (25.5%) were grouped as the S/MP5 group. The S/MP+ and S/MP5 groups had larger tumors, higher frequency of lymph node metastasis, and more advanced stages of disease than the S/MP- group (P < 0.001, all comparisons). Pleural, lymphatic, and vascular invasions occurred more frequently in the S/MP+ and S/MP5 groups (P < 0.001, all comparisons for S/MP+ vs. S/MP-; P ≤ 0.01, all comparisons for S/MP5 vs. S/MP-). The S/MP+ and S/MP5 groups showed a shorter time to recurrence and cancer-related death than the S/MP- group(P < 0.001, both comparisons). For stage I, the presence or absence of the S/MP subtype defined prognostic subgroups better than the stage IA/IB classification. Notably, in the multivariate analysis, the minimal S/MP component was a significant predictor of recurrence, even in stage IA.

CONCLUSIONS

The presence of the minimal S/MP component was a significant predictor of poor prognosis after surgery, even in stage IA patients. Clinical trials to evaluate the advantages of adjuvant chemotherapy for this subset of patients and further investigations to understand underlying biological mechanisms of poor prognosis are needed.

KEY POINTS

Significant findings of the study: We demonstrated that only minimal presence of solid or micropapillary component was profoundly associated with aggressive clinicopathological features and poor prognosis after complete resection even in stage IA lung adenocarcinoma.

WHAT THIS STUDY ADDS

Our results suggest that minimal presence of these subtypes is a strong prognostic factor which should be taken into account in the risk assessment for adjuvant chemotherapy in lung adenocarcinoma.

Prognostic impact of micropapillary component in patients with node-negative subcentimeter lung adenocarcinoma: A Chinese cohort study

Background

In this study, we investigated the prognostic significance of a micropapillary (MP) component in patients with subcentimeter lung adenocarcinoma.

Methods

A total of 311 patients with subcentimeter lung adenocarcinoma who underwent surgical resection between January 2009 to December 2012 from seven medical centers were included. Recurrence‐free survival (RFS) and overall survival (OS) were analyzed.

Results

The five‐year RFS was 79.8% in 97 (97/311, 31%) cases of adenocarcinoma with a MP component and 93.5% in the 214 (214/311, 69%) cases without. In multivariate analysis, MP was an independent risk factor for worse RFS (hazard ratio [HR], 3.73; 95% confidence interval [CI]: 1.87–7.42; P < 0.001) and OS (HR, 5.84; 95% CI: 2.20–15.49; P < 0.001). There was no significant difference among wedge resection, segmentectomy and lobectomy on RFS (P = 0.256) and OS (P = 0.103) in patients without MP. Regarding patients with a MP component, lobectomy achieved equivalent prognosis than segmentectomy, and both were better than wedge resection (P = 0.001).

Conclusions

A MP component still suggest a poor prognosis in subcentimeter lung adenocarcinoma. Patients with subcentimeter lung adenocarcinoma with a MP component of 5% or greater treated with wedge resection were at higher risk of recurrence than patients treated with anatomical resection.

Value of TSCT Features for Differentiating Preinvasive and Minimally Invasive Adenocarcinoma From Invasive Adenocarcinoma Presenting as Subsolid Nodules Smaller Than 3 cm

BACKGROUND

To distinguish preinvasive (adenocarcinoma in situ/atypical adenomatous hyperplasia) and minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IA) appearing as solitary subsolid nodules (SSNs) less than 3 cm based on thin-section computed tomography (TSCT) features to guide therapeutic approaches.

METHODS

A total of 154 lesions that were histopathologically confirmed to have pre/minimally invasive adenocarcinoma (hereafter pre/MIA) and IA presenting as part-solid nodules (PSNs) or pure ground-glass nodules (pGGNs) were retrospectively reviewed. The TSCT features, including diameter, area, CT value, shape, air bronchogram, margins, and location, were compared and assessed. Receiver operating characteristic analyses were conducted to determine the cut-off values for the qualitative variables and their diagnostic performances.

RESULTS

Of 154 nodules, 89 IA, 53 MIA, eight adenocarcinoma in situ, and four atypical adenomatous hyperplasia lesions were found. Univariate and multivariate logistic regression of the pre/MIA and IA lesions were compared and analyzed among PSNs and pGGNs. Among pGGNs, a significant difference was found in the area (p = 0.004, odds ratio [OR] = 0.124, 95% confidence interval [CI] = 0.300-0.515) between the pre/MIA and IA groups. In PSNs, significant differences were found in the diameter (p = 0.001, OR = 0.171, 95% CI = 0.063-0.467) and CT value (p = 0.001, OR = 0.996, 95% CI = 0.993-0.998) between the pre/MIA and IA groups. According to the corresponding receiver operating characteristic curves, the optimal cut-off tumor area in pGGNs to differentiate pre/MIA from IA was 0.595 cm². A higher CT value of the lesion (≥ -298.500 HU) and a larger diameter (≥1.450 cm) in PSNs were significantly associated with IA.

CONCLUSION

Imaging features from TSCT contribute to distinguishing pre/MIA from IA in solitary subsolid nodules and may contribute to guide the clinical management of these lesions.

Clinical significance of the cribriform pattern in invasive adenocarcinoma of the lung

PURPOSE

According to the WHO, the cribriform pattern is a subtype of acinar (Aci) predominance in invasive adenocarcinoma (ADC) of the lung. Recently, several studies have demonstrated poor prognosis in patients with cribriform predominance. This study was performed to examine the correlations of cribriform pattern with the clinicopathology, molecular features and prognosis in patients with invasive ADC.

METHODS

Histological subtypes were evaluated in 279 patients who underwent complete resection for invasive ADC. Patients of the Aci-predominant subtype were divided into two subgroups according to the percentage of cribriform cancer (≥5% vs <5%). Clinicopathological characteristics, overall survival (OS), disease-free survival (DFS) and molecular changes were compared. In addition, both OS and DFS were compared between patients with cribriform-predominant (n=33) and pure Aci-predominant (n=88) ADCs.

RESULTS

A cribriform pattern was found in 111 (39.8%) cases and ranged from 5 % to 100 % of the total tumour volume (mean±SEM, 30%±2%). Of 117 patients with Aci predominance, 79 showed the cribriform pattern, while the remaining 38 did not. The cribriform pattern was associated with aggressive pathological behaviour, including advanced stages of cancer, nuclear atypia, mitoses, lymph node invasion, metastasis and larger tumour size. The subgroup with cribriform cancer (≥5%) had significantly poorer OS and DFS compared with the cribriform-negative (<5%) group. In addition, Cox multivariate analyses revealed that the cribriform pattern was an independent predictor of OS but not DFS. Moreover, OS was significantly lower in the cribriform-predominant group than in the Aci-predominant group.

CONCLUSION

The cribriform pattern is associated with aggressive pathological behaviour and is an independent poor prognostic indicator in patients with Aci-predominant ADC of the lung.

Adjuvant Chemotherapy Improves Survival in Surgically Resected Stage IB Squamous Lung Cancer

BACKGROUND

At present there is a significant lack of clinical data for patients with surgically resected stage I squamous lung cancer. The purpose of this study was to investigate the impact of postoperative chemotherapy in this specific population.

METHODS

We retrospectively identified patients who had undergone complete squamous lung cancer resection at the Shanghai Chest Hospital between January 2008 and January 2014.

RESULTS

A total of 596 patients (236 stage IA, 360 stage IB) were included in this study. Results demonstrated that adjuvant chemotherapy (ACT) could provide longer overall survival for patients with p-stage IB disease (hazard ratio [HR], 0.56; 95% confidence interval [CI], 0.34-0.90; p = 0.017). Among p-stage IB patients the ACT-treated cohort trended toward a benefit (HR, 0.69; 95% CI, 0.45-1.04) in recurrence-free survival but failed to reach statistical significance (p = 0.076). After propensity score matching the HRs of recurrence-free survival and overall survival were 0.58 (95% CI, 0.35-0.96; p = 0.033) and 0.49 (95% CI, 0.27-0.88; p = 0.017), respectively. With regards to patients with p-stage IA disease, neither overall survival (HR, 0.87; 95% CI, 0.34-2.27; p = 0.783) nor recurrence-free survival (HR, 0.79; 95% CI, 0.38-1.65; p = 0.534) was significantly different when compared between patients receiving ACT and those who did not. Similar results were also achieved after propensity score matching.

CONCLUSIONS

The data presented herein demonstrated that ACT might provide survival benefits for squamous lung cancer patients with p-stage IB disease.