Three dimensional computed tomography texture analysis of pulmonary lesions: Does radiomics allow differentiation between carcinoma, neuroendocrine tumor and organizing pneumonia?

[Pulmonary neuroendocrine tumors : Current review]

CLINICAL ISSUE

Neuroendocrine neoplasms of the lung are a heterogenous tumor group. The pathological classification comprises diffuse idiopathic pulmonary neuroendocrine cell hyperplasia, classic neuroendocrine tumors, and neuroendocrine carcinoma. Classic neuroendocrine tumors include typical and atypical carcinoid tumors.

DIAGNOSTIC WORK-UP

Imaging plays an important role in diagnosis and can help in identifying the tumor biology. Overall, this tumor group is rare, comprising less than 2% of all thoracic tumors.

PRACTICAL RECOMMENDATIONS

In the current review, the various tumors are presented and important aspects regarding pathological classification, imaging modalities, and treatment are described.

CT radiomics to differentiate neuroendocrine neoplasm from adenocarcinoma in patients with a peripheral solid pulmonary nodule: a multicenter study

Purpose

To construct and validate a computed tomography (CT) radiomics model for differentiating lung neuroendocrine neoplasm (LNEN) from lung adenocarcinoma (LADC) manifesting as a peripheral solid nodule (PSN) to aid in early clinical decision-making.

Methods

A total of 445 patients with pathologically confirmed LNEN and LADC from June 2016 to July 2023 were retrospectively included from five medical centers. Those patients were split into the training set (n = 316; 158 LNEN) and external test set (n = 129; 43 LNEN), the former including the cross-validation (CV) training set and CV test set using ten-fold CV. The support vector machine (SVM) classifier was used to develop the semantic, radiomics and merged models. The diagnostic performances were evaluated by the area under the receiver operating characteristic curve (AUC) and compared by Delong test. Preoperative neuron-specific enolase (NSE) levels were collected as a clinical predictor.

Results

In the training set, the AUCs of the radiomics model (0.878 [95% CI: 0.836, 0.915]) and merged model (0.884 [95% CI: 0.844, 0.919]) significantly outperformed the semantic model (0.718 [95% CI: 0.663, 0.769], p both<.001). In the external test set, the AUCs of the radiomics model (0.787 [95% CI: 0.696, 0.871]), merged model (0.807 [95%CI: 0.720, 0.889]) and semantic model (0.729 [95% CI: 0.631, 0.811]) did not exhibit statistical differences. The radiomics model outperformed NSE in sensitivity in the training set (85.3% vs 20.0%; p <.001) and external test set (88.9% vs 40.7%; p = .002).

Conclusion

The CT radiomics model could non-invasively, effectively and sensitively predict LNEN and LADC presenting as a PSN to assist in treatment strategy selection.

Application of uniportal video-assisted thoracoscopic surgery for segmentectomy in early-stage non-small cell lung cancer: A narrative review

Uniportal video-assisted thoracoscopic surgery (UVATS) segmentectomy has emerged as an effective approach for managing early-stage non-small-cell lung cancer (NSCLC). Compared to conventional open and thoracoscopic surgeries, this minimally invasive surgical technique offers multiple benefits, including reduced postoperative discomfort, shorter hospital stays, expedited recovery, fewer complications, and superior cosmetic outcomes. Particularly advantageous in preserving lung function, UVATS segmentectomy is a compelling option for patients with compromised lung capabilities or limited pulmonary reserve. Notably, it demonstrates promising oncological results in early-stage NSCLC, with long-term survival rates comparable to those of lobectomies. Skilled thoracic surgeons can ensure a safe and effective execution of UVATS despite the potential technical challenges posed by complex tumor locations that may hinder visibility and maneuverability within the thoracic cavity. This study provided a comprehensive review of the literature and existing studies on UVATS segmentectomies. It delves into the evolution of the technique, its current applications, and the balance between its benefits and limitations. This discussion extends the technical considerations, challenges, and prospects of UVATS segmentectomy. Furthermore, it aimed to update advancements in segmentectomy for treating early-stage NSCLC, offering in-depth insights to thoracic surgeons to inform more scientifically grounded and patient-specific surgical decisions.

Next generation phenotyping for diagnosis and phenotype-genotype correlations in Kabuki syndrome

The field of dysmorphology has been changed by the use Artificial Intelligence (AI) and the development of Next Generation Phenotyping (NGP). The aim of this study was to propose a new NGP model for predicting KS (Kabuki Syndrome) on 2D facial photographs and distinguish KS1 (KS type 1, KMT2D-related) from KS2 (KS type 2, KDM6A-related). We included retrospectively and prospectively, from 1998 to 2023, all frontal and lateral pictures of patients with a molecular confirmation of KS. After automatic preprocessing, we extracted geometric and textural features. After incorporation of age, gender, and ethnicity, we used XGboost (eXtreme Gradient Boosting), a supervised machine learning classifier. The model was tested on an independent validation set. Finally, we compared the performances of our model with DeepGestalt (Face2Gene). The study included 1448 frontal and lateral facial photographs from 6 centers, corresponding to 634 patients (527 controls, 107 KS); 82 (78%) of KS patients had a variation in the KMT2D gene (KS1) and 23 (22%) in the KDM6A gene (KS2). We were able to distinguish KS from controls in the independent validation group with an accuracy of 95.8% (78.9-99.9%, p < 0.001) and distinguish KS1 from KS2 with an empirical Area Under the Curve (AUC) of 0.805 (0.729-0.880, p < 0.001). We report an automatic detection model for KS with high performances (AUC 0.993 and accuracy 95.8%). We were able to distinguish patients with KS1 from KS2, with an AUC of 0.805. These results outperform the current commercial AI-based solutions and expert clinicians.