Frozen sections accurately predict the IASLC proposed grading system and prognosis in patients with invasive lung adenocarcinomas

Intraoperative frozen section diagnosis of lung specimens: An updated review

Intraoperative frozen section (FS) diagnosis is a critical step in the management of patients with pulmonary lesions, which provides guidance for surgical resection procedures. Intraoperative FS diagnosis requires a comprehensive approach that integrates clinical information, imaging findings, and histopathological evaluation. Effective communication between pathologists and surgeons is vital for achieving the best practice result. Intraoperative FS diagnosis faces new challenges in the era of new lung cancer screening strategy, changes in histological tumor classification and addition of new lung tumor entities. Below we discuss the challenges in pre-intraoperative assessment and intraoperative diagnosis of pulmonary nodules. Key considerations include clinical information and CT imaging findings. Multiple nodules require strategic sampling, focusing on the most malignant-appearing lesion. Intraoperative FS diagnosis involves recognizing growth patterns and cellular atypia that help distinction of preinvasive lesions, minimal invasive, and invasive tumor although it might be challenging. Distinguishing benign from malignant tumors is also discussed, with emphasis on histological and imaging features. Special considerations include spread through air spaces (STAS), margin assessment, lymphoproliferative disorders, infectious diseases, and benign or uncertain-behavior tumors.

Impact of histopathological subtypes on invasive lung adenocarcinoma: from epidemiology to tumour microenvironment to therapeutic strategies

Lung adenocarcinoma is the most prevalent type of lung cancer, with invasive lung adenocarcinoma being the most common subtype. Screening and early treatment of high-risk individuals have improved survival; however, significant differences in prognosis still exist among patients at the same stage, especially in the early stages. Invasive lung adenocarcinoma has different histological morphologies and biological characteristics that can distinguish its prognosis. Notably, several studies have found that the pathological subtypes of invasive lung adenocarcinoma are closely associated with clinical treatment. This review summarised the distribution of various pathological subtypes of invasive lung adenocarcinoma in the population and their relationship with sex, smoking, imaging features, and other histological characteristics. We comprehensively analysed the genetic characteristics and biomarkers of the different pathological subtypes of invasive lung adenocarcinoma. Understanding the interaction between the pathological subtypes of invasive lung adenocarcinoma and the tumour microenvironment helps to reveal new therapeutic targets for lung adenocarcinoma. We also extensively reviewed the prognosis of various pathological subtypes and their effects on selecting surgical methods and adjuvant therapy and explored future treatment strategies.

Prognostic impact of the newly revised IASLC proposed grading system for invasive lung adenocarcinoma: a systematic review and meta-analysis

BACKGROUND

This study aimed to evaluate the prognostic value of the newly revised International Association for the Study of Lung Cancer (IASLC) grading system (2020) on the 5-year overall survival (OS) and recurrence-free survival (RFS) in patients with lung adenocarcinoma (LADC).

METHODS

Clinical studies that investigated the prognostic value of revised IASLC staging system in patients with LADC were retrieved from the PubMed, Web of Science, ScienceDirect, and Cochrane Library databases. This study was conducted in accordance to the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and checklists.

RESULTS

Based on inclusion and exclusion criteria, we included 12 studies for analysis. The grade of LADC was assessed by revised IASLC system, which included three grades. Compared to Grade 3 LADC, grade 1 (total [95% CI]: 1.38 [1.19, 1.60]) and grade 2 (total [95% CI]: 1.29 [1.15, 1.44]) LADC had higher 5-year OS rates. Similarly, Grade 1 (total [95% CI]: 1.76 [1.42, 2.18]) and Grade 2 (total [95% CI]: 1.51 [1.28, 1.77]) had higher 5-year RFS rates Grade 3 LADC. However, 5-year OS and RFS had no significant difference between Grade 1 and Grade 2 patients.

CONCLUSION

This systematic review and meta-analysis provides evidence that the newly revised IASLC grading system is significantly associated with the prognosis of patients with LADC, where Grade 3 indicated unfavorable prognosis.

Development and external validation of a nomogram for predicting lymph node metastasis in 1-3 cm lung adenocarcinoma

Aim: This study aimed to investigate the risk factors for lymph node metastasis in 1-3 cm adenocarcinoma and develop a new nomogram to predict the probability of lymph node metastasis.Materials & methods: This study collected clinical data from 1656 patients for risk factor analysis and an additional 500 patients for external validation. The logistic regression analyses were employed for risk factor analysis. The least absolute shrinkage and selection operator regression was used to select variables, and important variables were used to construct the nomogram and an online calculator.Results: The nomogram for predicting lymph node metastasis comprises six variables: tumor size (mediastinal window), consolidation tumor ratio, tumor location, lymphadenopathy, preoperative serum carcinoembryonic antigen level and pathological grade. According to the predicted results, the risk of lymph node metastasis was divided into low-risk group and high-risk group. We confirmed the exceptional clinical efficacy of the model through multiple evaluation methods.Conclusion: The importance of intraoperative frozen section is increasing. We discussed the risk factors for lymph node metastasis and developed a nomogram to predict the probability of lymph node metastasis in 1-3 cm adenocarcinomas, which can guide lymph node resection strategies during surgery.

The role of extensive lymph node dissection in the new grading system for lung adenocarcinoma

OBJECTIVES

This study evaluates the prognostic impact of the new grading system for lung adenocarcinoma, stratified by lymphadenectomy extent.

MATERIALS AND METHODS

We analyzed 1258 lung adenocarcinoma patients who underwent curative resections between 2006 and 2017. We analyzed overall survival (OS), cancer-specific survival (CSS), and recurrence-free survival (RFS) across tumor grades and lymphadenectomy extent, categorized as IASLC-R0 (complete resection) or R(un) (uncertain resection).

RESULTS

The median age of cohort was 63 and 41.9 % were male. The majority had undergone lobectomy. The distribution of tumors was 274 grade 1, 558 grade 2, and 426 grade 3 cases. After a median follow-up time of 102 months, the 10-year OS/CSS/RFS rates worsened significantly across grade 1-3: 92.4/99.3/92.3 %, 77.8/87.5/71.7 %, and 63.6/70.2/52.0 %, respectively (p < 0.001). Multivariate Cox regression analysis identified grade 3, R(un) lymphadenectomy, higher Charlson Comorbidity Index, smoking history, thoracotomy, higher pathology stage, and angiolymphatic invasion as independent prognostic factors for lower OS, CSS, and RFS. Furthermore, grade 3 patients benefited significantly from IASLC-R0 lymphadenectomy, showing significantly better OS and RFS than those who underwent R(un) lymphadenectomy (p = 0.007 for OS, p = 0.001 for RFS, post-propensity score matching). Among grade 3 tumors underwent R0 or R(un) resections found the incidence rates of local, distant, and simultaneous local and distant recurrence were 8.5 % vs 13.7 %, 11.0 % vs 12.2 %, and 11.0 % vs 20.6 %, respectively.

CONCLUSION

Surgical outcomes for lung adenocarcinoma have declined across grades 1-3. IASLC-R(un) treatment worsens OS and RFS in grade 3. Intensive monitoring and adjuvant therapy should be considered when patients with grade 3 lung adenocarcinoma undergo R(un) lymphadenectomy.

Semi-supervised lung adenocarcinoma histopathology image classification based on multi-teacher knowledge distillation

Objective.In this study, we propose a semi-supervised learning (SSL) scheme using a patch-based deep learning (DL) framework to tackle the challenge of high-precision classification of seven lung tumor growth patterns, despite having a small amount of labeled data in whole slide images (WSIs). This scheme aims to enhance generalization ability with limited data and reduce dependence on large amounts of labeled data. It effectively addresses the common challenge of high demand for labeled data in medical image analysis.Approach.To address these challenges, the study employs a SSL approach enhanced by a dynamic confidence threshold mechanism. This mechanism adjusts based on the quantity and quality of pseudo labels generated. This dynamic thresholding mechanism helps avoid the imbalance of pseudo-label categories and the low number of pseudo-labels that may result from a higher fixed threshold. Furthermore, the research introduces a multi-teacher knowledge distillation (MTKD) technique. This technique adaptively weights predictions from multiple teacher models to transfer reliable knowledge and safeguard student models from low-quality teacher predictions.Main results.The framework underwent rigorous training and evaluation using a dataset of 150 WSIs, each representing one of the seven growth patterns. The experimental results demonstrate that the framework is highly accurate in classifying lung tumor growth patterns in histopathology images. Notably, the performance of the framework is comparable to that of fully supervised models and human pathologists. In addition, the framework's evaluation metrics on a publicly available dataset are higher than those of previous studies, indicating good generalizability.Significance.This research demonstrates that a SSL approach can achieve results comparable to fully supervised models and expert pathologists, thus opening new possibilities for efficient and cost-effective medical images analysis. The implementation of dynamic confidence thresholding and MTKD techniques represents a significant advancement in applying DL to complex medical image analysis tasks. This advancement could lead to faster and more accurate diagnoses, ultimately improving patient outcomes and fostering the overall progress of healthcare technology.

Development and validation of a model for predicting upstage in minimally invasive lung adenocarcinoma in Chinese people

BACKGROUND

Sublobar resection for ground-glass opacity became a recommend surgery choice supported by the JCOG0804/JCOG0802/JCOG1211 results. Sublobar resection includes segmentectomy and wedge resection, wedge resection is suitable for non-invasive lesions, but in clinical practice, when pathologists are uncertain about the intraoperative frozen diagnosis of invasive lesions, difficulty in choosing the appropriate operation occurs. The purpose of this study was to analyze how to select invasive lesions with clinic-pathological characters.

METHODS

A retrospective study was conducted on 134 cases of pulmonary nodules diagnosed with minimally invasive adenocarcinoma by intraoperative freezing examination. The patients were divided into two groups according to intraoperative frozen results: the minimally invasive adenocarcinoma group and the at least minimally invasive adenocarcinoma group. A variety of clinical features were collected. Chi-square tests and multiple regression logistic analysis were used to screen out independent risk factors related to pathological upstage, and then ROC curves were established. In addition, an independent validation set included 1164 cases was collected.

RESULTS

Independent risk factors related to pathological upstage were CT value, maximum tumor diameter, and frozen result of AL-MIA. The AUC of diagnostic mode was 71.1% [95%CI: 60.8-81.3%]. The independent validation included 1164 patients, 417 (35.8%) patients had paraffin-based pathology of invasive adenocarcinoma. The AUC of diagnostic mode was 75.7% [95%CI: 72.9-78.4%].

CONCLUSIONS

The intraoperative frozen diagnosis was AL-MIA, maximum tumor diameter larger than 15 mm and CT value is more than - 450Hu, highly suggesting that the lung GGO was invasive adenocarcinoma which represent a higher risk to recurrence. For these patients, sublobectomy would be insufficient, lobectomy or complementary treatment is encouraged.

Whole-lesion iodine map histogram analysis versus single-slice spectral CT parameters for determining novel International Association for the Study of Lung Cancer grade of invasive non-mucinous pulmonary adenocarcinomas

PURPOSE

The purpose of this study was to evaluate and compare the performances of whole-lesion iodine map histogram analysis to those of single-slice spectral computed tomography (CT) parameters in discriminating between low-to-moderate grade invasive non-mucinous pulmonary adenocarcinoma (INMA) and high-grade INMA according to the novel International Association for the Study of Lung Cancer grading system of INMA.

MATERIALS AND METHODS

Sixty-one patients with INMA (34 with low-to-moderate grade [i.e., grade I and grade II] and 27 with high grade [i.e., grade III]) were evaluated with spectral CT. There were 28 men and 33 women, with a mean age of 56.4 ± 10.5 (standard deviation) years (range: 29-78 years). The whole-lesion iodine map histogram parameters (mean, standard deviation, variance, skewness, kurtosis, entropy, and 1st, 10th, 25th, 50th, 75th, 90th, and 99th percentile) were measured for each INMA. In other sessions, by placing regions of interest at representative levels of the tumor and normalizing them, spectral CT parameters (iodine concentration and normalized iodine concentration) were obtained. Discriminating capabilities of spectral CT and histogram parameters were assessed and compared using area under the ROC curve (AUC) and logistic regression models.

RESULTS

The 1st, 10th, and 25th percentiles of the iodine map histogram analysis, and iodine concentration and normalized iodine concentration of single-slice spectral CT parameters were significantly different between high-grade and low-to-moderate grade INMAs (P < 0.001 to P = 0.002). The 1st percentile of histogram parameters (AUC, 0.84; 95% confidence interval [CI]: 0.73-0.92) and iodine concentration (AUC, 0.78; 95% CI: 0.66-0.88) from single-slice spectral CT parameters had the best performance for discriminating between high-grade and low-to-moderate grade INMAs. At ROC curve analysis no significant differences in AUC were found between histogram parameters (AUC = 0.86; 95% CI: 0.74-0.93) and spectral CT parameters (AUC = 0.81; 95% CI: 0.74-0.93) (P = 0.60).

CONCLUSION

Both whole-lesion iodine map histogram analysis and single-slice spectral CT parameters help discriminate between low-to-moderate grade and high-grade INMAs according to the novel International Association for the Study of Lung Cancer grading system, with no differences in diagnostic performances.

Randomized controlled trials in lung cancer surgery: How are we doing?

Objective

Randomized control trials are considered the highest level of evidence, yet the scalability and practicality of implementing randomized control trials in the thoracic surgical oncology space are not well described. The aim of this study is to understand what types of randomized control trials have been conducted in thoracic surgical oncology and ascertain their success rate in completing them as originally planned.

Methods

The ClinicalTrials.gov database was queried in April 2023 to identify registered randomized control trials performed in patients with lung cancer who underwent surgery (by any technique) as part of their treatment.

Results

There were 68 eligible randomized control trials; 33 (48.5%) were intended to examine different perioperative patient management strategies (eg, analgesia, ventilation, drainage) or to examine different intraoperative technical aspects (eg, stapling, number of ports, port placement, ligation). The number of randomized control trials was relatively stable over time until a large increase in randomized control trials starting in 2016. Forty-four of the randomized control trials (64.7%) were open-label studies, 43 (63.2%) were conducted in a single facility, 66 (97.1%) had 2 arms, and the mean number of patients enrolled per randomized control trial was 236 (SD, 187). Of 21 completed randomized control trials (31%), the average time to complete accrual was 1605 days (4.4 years) and average time to complete primary/secondary outcomes and adverse events collection was 2125 days (5.82 years).

Conclusions

Given the immense investment of resources that randomized control trials require, these findings suggest the need to scrutinize future randomized control trial proposals to assess the likelihood of successful completion. Future study is needed to understand the various contributing factors to randomized control trial success or failure.