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The clinicopathological differences among high-grade fetal lung adenocarcinomas completely comprising tumor cells that resemble fetal lung epithelium (pure type) and those with fetal lung-like components admixed with conventional adenocarcinoma cells (mixed type) remain undetermined. Here, we examined the clinicopathological, immunohistochemical, and molecular features of 11 lung adenocarcinomas with fetal lung-like morphology among 3895 consecutive cases of primary lung cancer based on the expression pattern of transcription factors. According to the current WHO classification, two cases (0.05%) were categorized as low-grade fetal adenocarcinoma, two cases (0.05%) were pure-type high-grade fetal adenocarcinoma, five cases (0.1%) were mixed-type high-grade fetal adenocarcinoma, and the remaining two cases (0.05%) were lung adenocarcinoma with high-grade fetal features (fetal lung-like morphology occupied less than 50%). CTNNB1 mutations were exclusively identified in low-grade fetal adenocarcinomas. In contrast, mixed-type high-grade fetal adenocarcinoma or lung adenocarcinoma with high-grade fetal features frequently harbored mitogenic drivers including EGFR mutations. Furthermore, almost all tumor cells expressed CDX2 and HNF4α in both cases of pure-type high-grade fetal lung adenocarcinoma, but lacked TTF-1 positivity. In contrast, TTF-1 was frequently expressed in mixed-type high-grade fetal lung adenocarcinoma and in lung adenocarcinoma with high-grade fetal features. Our data suggest similar prevalence of low-grade fetal lung adenocarcinoma and pure-type high-grade fetal lung adenocarcinoma, and indicate that pure- and mixed-type high-grade fetal lung adenocarcinomas are distinct, with the former akin to low-grade fetal adenocarcinoma with respect to purely embryonic morphology and absence of common lung adenocarcinoma mitogenic drivers, and the latter being genetically and transcriptionally related to conventional lung adenocarcinoma.

Prenatal evaluation of fetal lung maturity (FLM) is a challenge, and an effective non-invasive method for prenatal assessment of FLM is needed. The study aimed to establish a normal fetal lung gestational age (GA) grading model based on deep learning (DL) algorithms, validate the effectiveness of the model, and explore the potential value of DL algorithms in assessing FLM. A total of 7013 ultrasound images obtained from 1023 normal pregnancies between 20 and 41 + 6 weeks were analyzed in this study. There were no pregnancy-related complications that affected fetal lung development, and all infants were born without neonatal respiratory diseases. The images were divided into three classes based on the gestational week: class I: 20 to 29 + 6 weeks, class II: 30 to 36 + 6 weeks, and class III: 37 to 41 + 6 weeks. There were 3323, 2142, and 1548 images in each class, respectively. First, we performed a pre-processing algorithm to remove irrelevant information from each image. Then, a convolutional neural network was designed to identify different categories of fetal lung ultrasound images. Finally, we used ten-fold cross-validation to validate the performance of our model. This new machine learning algorithm automatically extracted and classified lung ultrasound image information related to GA. This was used to establish a grading model. The performance of the grading model was assessed using accuracy, sensitivity, specificity, and receiver operating characteristic curves. A normal fetal lung GA grading model was established and validated. The sensitivity of each class in the independent test set was 91.7%, 69.8%, and 86.4%, respectively. The specificity of each class in the independent test set was 76.8%, 90.0%, and 83.1%, respectively. The total accuracy was 83.8%. The area under the curve (AUC) of each class was 0.982, 0.907, and 0.960, respectively. The micro-average AUC was 0.957, and the macro-average AUC was 0.949. The normal fetal lung GA grading model could accurately identify ultrasound images of the fetal lung at different GAs, which can be used to identify cases of abnormal lung development due to gestational diseases and evaluate lung maturity after antenatal corticosteroid therapy. The results indicate that DL algorithms can be used as a non-invasive method to predict FLM.

High‐grade fetal lung adenocarcinoma (H‐FLAC) is a rare type of tumor. There have been no reports demonstrating the degree of metastatic susceptibility of this tumor type. In this report, we describe a case in which 15% of the adenocarcinoma components were H‐FLAC diagnosed as the cause of lymph node metastasis. A 75‐year‐old man presented with suspected primary lung cancer (clinical stage IIA, T2bN0M0) and underwent left upper lobectomy and superior mediastinal lymph node dissection. Postoperative histopathology revealed lung cancer with only lobar bronchial lymph node (#11) metastasis. Approximately 60% of the invasive adenocarcinoma showed a papillary morphology, 25% showed a lepidic morphology, and 15% showed a fetal morphology. The histomorphological and immunohistological features of #11 metastasis were similar to those of H‐FLAC. Herein, we report a rare and important case of H‐FLAC with proven lymph node metastasis, showing that even a small amount of H‐FLAC tissue can cause metastasis. This is the first report to demonstrate the histological characteristics of high‐grade fetal lung adenocarcinoma (H‐FLAC) as the cause of N1 in a patient with lung adenocarcinoma containing a small number of H‐FLAC components.

High‐grade fetal lung adenocarcinoma (H‐FLAC) is a rare tumor, with little known of its response to chemotherapy with or without an immune checkpoint inhibitor or of its molecular profile. We report the first case of a 56‐year‐old man with stage IV H‐FLAC who was successfully treated with carboplatin plus nab‐paclitaxel in combination with atezolizumab. In addition, the tumor was found to be positive for amplification of the human epidermal growth factor receptor 2 gene. High‐grade fetal lung adenocarcinoma (H‐FLAC) is a rare tumor, with little known of its response to chemotherapy with or without an immune checkpoint inhibitor or of its molecular profile. We report the first case of a 56‐year‐old man with stage IV H‐FLAC who was successfully treated with carboplatin plus nab‐paclitaxel in combination with atezolizumab. In addition, the tumor was found to be positive for amplification of the human epidermal growth factor receptor 2 gene.

Neonatal respiratory distress syndrome (RDS) is a leading cause of neonatal respiratory failure and neonatal mortality. It is frequent in preterm infants, because deficient surfactant needed to keep the airways (alveoli) open to assist infants breathe after birth. Nonetheless, it was also seen in full-term pregnancies. Noninvasive approaches for predicting the development of neonatal respiratory distress (RD) in preterm newborns include comparing the prenatal clinical outcome with the pulmonary artery resistance index (PA-RI) and fetal lung capacity as assessed by the virtual organ computer-aided analysis (VOCAL). Our study aimed to estimate optimal cutoff values and compare measurements of fetal pulmonary artery resistance index (PA-RI) and fetal lung volume (LV) assessed by VOCAL as noninvasive measures to predict neonatal RD development in preterm pregnancies to show which is more accurate. Out of the examined 147 women who delivered 147 living newborns, 59 of newborn (40.1%) developed neonatal RD. PA-RI has a higher value in 45 (76.27%), while fetal lung volume (FLV) was significantly lower in 43 (72.88%) of neonates who developed RD. Combining both measurements of PA-RI and FLV could predict all cases of RDS 59 (100%). Thirty of RDS neonates had mechanical ventilation and died (50.85%). Cutoff values of PA-RI [greater than or equal to] 0.75 with 76.27% sensitivity, 82.95% specificity and 81.5% accuracy, whereas a cutoff of FLV [less than or equal to] 28 cm.sup.3 with sensitivity of 72.88%, specificity of 65.91% and accuracy of 74.8%, for prediction of RDS. Combining both cutoffs generated a more accurate detection 100%, specificity of 65.91% and 66.3% positive predictive value (PPV) and 100% negative predictive value (NPV) and 83% accuracy. Both PA-RI and FLV are promising noninvasive tools which help in predicting RD fetuses with high sensitivity and specificity. PA-RI is more accurate than FLV cm.sup.3 in prediction of neonatal RDS. Combining these parameters increases the predictive value.

Purpose of StudyThis study aimed to compare the prenatal ultrasound parameters- fetal lung biometry and pulmonary artery Doppler in preterm newborns for prediction of respiratory distress (RD).MethodsA prospective analytic study was conducted in Department of Obstetrics and Gynecology in collaboration with Department of Neonatalogy. Fetal ultrasound and Doppler parameters were evaluated in women predisposed to have preterm delivery at or before 34 weeks. The neonates were followed for occurrence of RD. ResultOut of 100 study population, neonates who developed RD were taken as cases (n = 36) and rest were grouped as controls (n = 64). The gestational age at delivery, mean birth weight and Apgar score were significantly less in cases than controls. All the fetal lung biometric parameters were significantly less in cases than controls (p value < 0.001). The fetal lung volume had highest sensitivity (72.22%) and negative predictive value (83%). The right lung area had highest specificity (89%) and positive predictive value (72%). Among the Doppler parameters, the At/Et ratio showed high degree of accuracy (68%). The sensitivity and specificity were 55.56% and 75%, respectively. The positive and negative predictive values were 72% and 60%, respectively.ConclusionsBoth fetal lung biometry and pulmonary artery Doppler offer an excellent noninvasive approach for assessment of fetal lung maturity, clinically assessed by RD. On comparison of all the ultrasound parameters, fetal lung volume and At/Et ratio showed highest degree of accuracy in prediction of RD.

Abstract Most of our knowledge of human lung development is derived from morphologic studies and extrapolations of the underlying molecular mechanisms from animal models. Here we describe developmental changes in human fetal lungs during the pseudoglandular and early canalicular period, detailing this critical but previously poorly described transition period. We report the cellular composition and cell-to-cell communication in a single-nuclei dataset from nine human fetal lungs between 14 and 19 weeks of gestation. We identified 9 main populations and 19 subpopulations, including the rare pulmonary neuroendocrine cells. For each population, marker genes were reported, and selected markers were validated. Enrichment analysis were performed to explore the potential molecular mechanisms and pathways within individual populations according to gestational age. Finally, cell-to-cell communication was studied using ligand-receptor analysis among the different cell types. General developmental pathways, as well as pathways involved in vasculogenesis, neurogenesis, and immune regulation, were identified. This study provides an important background to generate research hypotheses in projects studying normal or impaired lung development and help to validate surrogate models (e.g., lung organoids) to study human lung development.

Localized cystic lung lesions in pediatric patients encompass a spectrum of benign and rare malignant conditions that are quite distinct from cystic lung disease arising in adulthood. The majority have historically fallen under the diagnostic category of “congenital pulmonary airway malformation,” a term that has been used to denote a diverse group of diseases ranging in etiology from ectopia to bronchial atresia to mosaic oncogenic mutation or neoplasia. This article reviews the clinical characteristics, gross and histologic features, and pathogenetic underpinnings of congenital pulmonary airway malformation as well as lesions that enter its histologic differential diagnosis. In light of ongoing advances in the field, previously proposed pathology-based classification schemes are critically appraised, and a new diagnostic framework is considered.

A novel placenta–fetal lung organ-on-a-chip platform enables direct analysis of how maternal treatments influence fetal lung development.The system replicates placental drug transfer and fetal lung surfactant production, offering mechanistic insight into how antenatal corticosteroid therapy promotes fetal lung maturation.Using this platform, we identified that steroid concentrations above 5 mM compromise placental cell viability without further increasing surfactant output, revealing a threshold for safe, effective dosing.These findings inform optimization of antenatal steroid therapy to maximize preterm infant lung maturation benefits while minimizing placental and fetal side effects. Antenatal corticosteroids are recommended for preterm births to enhance lung maturity; however, the guidelines are based on limited studies. Here, we present a placenta–fetal lung microphysiological analysis platform (MAP) to study how corticosteroids promote fetal lung maturation and determine their optimal concentration with minimal side effects. We create trophoblast–capillary–pneumocyte MAP on chips to analyze the transport of corticosteroids from mother to fetus through the placenta. We assessed surfactant production from the pneumocyte after exposure to different concentrations, types, and durations of corticosteroids in the trophoblast layer. We found the concentrations of corticosteroids over 5 mM reduced trophoblast viability and did not increase the surfactant production from pneumocytes. Our research on placenta–fetal lung MAP provides insight into how corticosteroids improve the production of surfactant from immature pneumocytes as they transition from the placenta and suggests that determining the appropriate dosage of corticosteroids to maximize effectiveness while preventing damage to trophoblasts is crucial. [Display omitted] The placenta–fetal lung microphysiological analysis platform (MAP) is at a proof-of-concept stage, meaning its feasibility has been demonstrated in a lab setting. This integrated trophoblast–capillary–fetal lung system models maternal corticosteroid transfer across a placental barrier and the resultant induction of fetal lung surfactant production. However, as a nascent technology, its implementation faces key challenges. The current platform relies on immortalized cell lines for placental and fetal lung tissues, which lack full phenotypic fidelity of primary cells (e.g., limited surfactant production or barrier function). Drug administration in the device is also simplified (directly spiking the microfluidic channels) rather than mimicking physiological delivery routes via maternal circulation and metabolism. These limitations underscore the need for further refinement before advancing beyond laboratory validation. Moving this MAP toward higher readiness will require several improvements. Incorporating primary human cells or iPSC-derived trophoblast and alveolar cells would better recapitulate native placental and fetal lung functions. Integrating 3D organoid structures (placental villi or fetal lung organoids) and simulating multi-route drug delivery (e.g., maternal intravenous dosing with metabolic processing) could more faithfully recreate in vivo conditions. Such enhancements are expected to boost the platform’s predictive power for drug transport and efficacy. With these refinements, the placenta–fetal lung MAP holds significant translational potential. It could reduce reliance on animal studies by providing human-specific data on maternal–fetal drug transfer and fetal outcomes, informing evidence-based antenatal corticosteroid dosing guidelines and fulfilling regulatory needs for safety testing in pregnant populations. Notably, this approach aligns with emerging regulatory initiatives encouraging human-relevant in vitro models in drug development, positioning the platform as a valuable tool for future clinical and regulatory applications. We developed a placenta–fetal lung microphysiological platform to evaluate antenatal corticosteroid transport and fetal lung maturation. Our results indicate corticosteroid concentrations above 5 mM reduce placental cell viability without enhancing lung surfactant production, highlighting the importance of identifying optimal corticosteroid dosages to maximize therapeutic efficacy while minimizing adverse effects.