The International Association for the Study of Lung Cancer Staging Project for Lung Cancer: Proposals for the Revision of the N Descriptors in the Forthcoming Ninth Edition of the TNM Classification for Lung Cancer

Microcomputed tomography as a diagnostic tool for detection of lymph node metastasis in non-small cell lung cancer: A decision-support approach for pathological examination "A pilot study for method validation"

Background

Non-small cell lung cancer (NSCLC) patients without lymph node (LN) metastases (pN0) may exhibit different survival rates, even when their T stage is similar. This divergence could be attributed to the current pathology practice, wherein LNs are examined solely in two-dimensional (2D). Unfortunately, adhering to the protocols of 2D pathological examination does not ensure the exhaustive sampling of all excised LNs, thereby leaving room for undetected metastatic foci in the unexplored depths of tissues. The employment of micro-computed tomography (micro-CT) facilitates a three-dimensional (3D) evaluation of all LNs without compromising sample integrity. In our study, we utilized quantitative micro-CT parameters to appraise the metastatic status of formalin-fixed paraffin-embedded (FFPE) LNs.

Methods

Micro-CT scans were conducted on 12 FFPEs obtained from 8 NSCLC patients with histologically confirmed mediastinal LN metastases. Simultaneously, whole-slide images from these FFPEs underwent scanning, and 47 regions of interest (ROIs) (17 metastatic foci, 11 normal lymphoid tissues, 10 adipose tissues, and 9 anthracofibrosis) were marked on scanned images. Quantitative structural variables obtained via micro-CT analysis from tumoral and non-tumoral ROIs, were analyzed.

Result

Significant distinctions were observed in linear density, connectivity, connectivity density, and closed porosity between tumoral and non-tumoral ROIs, as indicated by kappa coefficients of 1, 0.90, 1, and 1, respectively. Receiver operating characteristic analysis substantiated the differentiation between tumoral and non-tumoral ROIs based on thickness, linear density, connectivity, connectivity density, and the percentage of closed porosity.

Conclusions

Quantitative micro-CT parameters demonstrate the ability to distinguish between tumoral and non-tumoral regions of LNs in FFPEs. The discriminatory characteristics of these quantitative micro-CT parameters imply their potential usefulness in developing an artificial intelligence algorithm specifically designed for the 3D identification of LN metastases while preserving the FFPE tissue.

Computed tomography-based 3D convolutional neural network deep learning model for predicting micropapillary or solid growth pattern of invasive lung adenocarcinoma

PURPOSE

To investigate the value of a computed tomography (CT)-based deep learning (DL) model to predict the presence of micropapillary or solid (M/S) growth pattern in invasive lung adenocarcinoma (ILADC).

MATERIALS AND METHODS

From June 2019 to October 2022, 617 patients with ILADC who underwent preoperative chest CT scans in our institution were randomly placed into training and internal validation sets in a 4:1 ratio, and 353 patients with ILADC from another institution were included as an external validation set. Then, a self-paced learning (SPL) 3D Net was used to establish two DL models: model 1 was used to predict the M/S growth pattern in ILADC, and model 2 was used to predict that pattern in ≤ 2-cm-diameter ILADC.

RESULTS

For model 1, the training cohort's area under the curve (AUC), accuracy, recall, precision, and F1-score were 0.924, 0.845, 0.851, 0.842, and 0.843; the internal validation cohort's were 0.807, 0.744, 0.756, 0.750, and 0.743; and the external validation cohort's were 0.857, 0.805, 0.804, 0.806, and 0.804, respectively. For model 2, the training cohort's AUC, accuracy, recall, precision, and F1-score were 0.946, 0.858, 0.881,0.844, and 0.851; the internal validation cohort's were 0.869, 0.809, 0.786, 0.794, and 0.790; and the external validation cohort's were 0.831, 0.792, 0.789, 0.790, and 0.790, respectively. The SPL 3D Net model performed better than the ResNet34, ResNet50, ResNeXt50, and DenseNet121 models.

CONCLUSION

The CT-based DL model performed well as a noninvasive screening tool capable of reliably detecting and distinguishing the subtypes of ILADC, even in small-sized tumors.

Prognostic Significance of the Highest Mediastinal Lymph Node Involvement in Patients with Stage III-N2 Non-small Cell Lung Cancer

BACKGROUND

Highest mediastinal lymph node (HMLN) involvement is a category of uncertain resection, yet the prognostic significance of HMLN involvement remains controversial.

METHODS

A total of 486 patients with pathological stage III-N2 disease who underwent radical resection were enrolled from January 2015 to December 2018. Patients were allocated into two groups-HMLN involvement (219 cases) and HMLN-negative (249 cases) groups. Kaplan-Meier analysis and Cox proportional hazard regression models were used to evaluate the impact of HMLN involvement on 5-year recurrence-free survival (RFS) and overall survival (OS).

RESULTS

The proportion of patients with multiple N2 diseases (72.1% vs. 23.7%; p < 0.001) and stage IIIA (87.2% vs. 77.5%; p < 0.009) were greater in the HMLN-involvement group than in the HMLN-negative group, and the survival rates of the HMLN-involvement group were significantly lower than those of the HMLN-negative group (RFS: 27.2% vs. 49.8%, p < 0.001; OS: 42.1% vs. 59.2%, p = 0.001). HMLN status was an independent factor for OS only (RFS: adjusted hazard ratio [aHR] 1.26, 95% confidence interval CI 0.94-1.68; OS: aHR 1.45, 95% CI 1.07-1.99) in the entire stage III cohort. After stratification of patients according to stage, the involvement of HMLN decreased both RFS and OS in the stage IIIA group (RFS: aHR 1.46, 95% CI 1.06-2.02; OS: aHR 1.70, 95% CI 1.19-2.42); however, no such difference was observed within the stage IIIB group.

CONCLUSIONS

HMLN involvement is a prognostic factor of deteriorating survival in highly advanced N2 disease only in patients with stage IIIA.

Pathological response and tumor stroma immunogenic features predict long-term survival in non-small cell lung cancer after neoadjuvant chemotherapy

PURPOSE

Major pathological response (MPR) has become a surrogate endpoint for overall survival (OS) in non-small cell lung cancer (NSCLC) after neoadjuvant therapy, however, the prognostic histologic features and optimal N descriptor after neoadjuvant therapy are poorly defined.

METHODS

We retrospectively analyzed data from 368 NSCLC patients who underwent surgery after neoadjuvant chemotherapy (NAC) from January 2010 to December 2020. The percentage of residual viable tumors in the primary tumor, lymph nodes (LN), and inflammation components within the tumor stroma were comprehensively reviewed. The primary endpoint was OS.

RESULTS

Of the 368 enrolled patients, 12.0% (44/368) achieved MPR in the primary tumor, which was associated with significantly better OS (HR, 0.36 0.17-0.77, p = 0.008) and DFS (HR = 0.59, 0.36-0.92, p = 0.038). In patients who did not have an MPR, we identified an immune-activated phenotype in primary tumors, characterized by intense tumor-infiltrating lymphocyte or multinucleated giant cell infiltration, that was associated with similar OS and DFS as patients who had MPR. Neoadjuvant pathologic grade (NPG), consisting of MPR and immune-activated phenotype, identified 30.7% (113/368) patients that derived significant OS (HR 0.28, 0.17-0.46, p < 0.001) and DFS (HR 0.44, 0.31-0.61, p < 0.001) benefit from NAC. Moreover, the combination of NPG and the number of positive LN stations (nS) in the multivariate analysis had a higher C-index (0.711 vs. 0.663, p < 0.001) than the ypTNM Stage when examining OS.

CONCLUSION

NPG integrated with nS can provide a simple, practical, and robust approach that may allow for better stratification of patients when evaluating neoadjuvant chemotherapy in clinical practice.

Assessing the impact of structured reporting on learning how to report lung cancer staging CT: A triple cohort study on inexperienced readers

PURPOSE

To assess the clinical utility of chest computed tomography (CT) reports for non-small-cell lung cancer (NSCLC) staging generated by inexperienced readers using structured reporting (SR) templates from the Royal College of Radiologists (RCR-SR) and the Italian Society of Medical and Interventional Radiology (SIRM-SR), compared to traditional non-systematic reports (NSR).

METHODS

In a cohort of 30 NSCLC patients, six third-year radiology residents reported CT examinations in two 2-month-apart separate sessions using NSR in the first and NSR, RCR-SR, or SIRM-SR in the second. Couples of expert radiologists and thoracic oncologists in consensus evaluated completeness, accuracy, and clarity. All the quality indicators were expressed on a 100-point scale. The Wilcoxon signed ranks, and Wilcoxon-Mann Whitney tests were used for statistical analyses.

RESULTS

Results showed significantly higher completeness for RCR-SR (90 %) and SIRM-SR (100 %) compared to NSR (70 %) in the second session (all p < 0.001). SIRM-SR demonstrated superior accuracy (70 % vs. 55 %, p < 0.001) over NSR, while RCR-SR and NSR accuracy did not significantly differ (60 % vs. 62.5 %, p = 0.06). In the second session, RCR-SR and SIRM-SR surpassed NSR in completeness, accuracy, and clarity (all p < 0.001, except p = 0.04 for accuracy between RCR-SR and NSR). SIRM-SR outperformed RCR-SR in completeness (100 % vs. 90 %, p < 0.001) and accuracy (70 % vs. 62.5 %, p = 0.002), with equivalent clarity (90 % for both, p = 0.27).

CONCLUSIONS

Inexperienced readers using RCR-SR and SIRM-SR demonstrated high-quality reporting, indicating their potential in radiology residency programs to enhance reporting skills for NSCLC staging and effective interaction with all the physicians involved in managing NSCLC patients.

Adjuvant and neo-adjuvant therapy for non-small cell lung cancer without EGFR mutations or ALK rearrangements

Surgical resection is the most effective therapeutic option for the cure in early stage resectable non-small-cell lung cancer (NSCLC). However, despite complete resection, up to 70% of patients die within 5 years mainly due to tumor recurrence in extra-thoracic organs. Adjuvant or neoadjuvant platinum-based chemotherapy may improve postoperative survival, but the absolute survival benefit is modest with an around 5% improvement at 5 years. Recent advance in systemic therapy has changed treatment strategy for advanced unresectable NSCLC, and also has provided a paradigm shift in treatment strategy for resectable NSCLC. For NSCLC without oncogenic driver alterations, immunotherapy using immune-checkpoint inhibitors may improve clinical outcomes in preoperative neoadjuvant setting as well as in postoperative adjuvant setting. Here, we overview recent evidence of adjuvant and neoadjuvant therapy and discuss emerging clinical questions in decision-making of treatment for potentially resectable patients with NSCLC harboring no oncogenic alterations.