Lung Imaging Research Articles

Advanced lung imaging with photon-counting detectors: Insights from thermoluminescence dosimetry

Rationale and ObjectivesThis study investigates the dose burden of photon-counting detector (PCD) lung CT with ultra-high-resolution (UHR) and standard mode using organ-based tube current modulation (OBTCM). Materials and MethodsAn anthropomorphic Alderson-Rando phantom was scanned in UHR and standard mode with and without OBTCM on three dose levels (IQ 5, 20, 50). Effective radiation dose was determined by thermoluminescent dosimetry in 13 measurement sites and compared with the calculated effective dose derived from the dose-length product. Image quality was evaluated subjectively by six radiologists using an equidistant 7-point scale and objectively by means of modulation transfer function analysis. ResultsMeasured effective radiation exposure was lower in UHR and OBTCM studies than in standard mode (IQ 5: 0.34–0.36, IQ 20: 1.57–1.70, IQ 50: 3.76–3.99 mSv). Compared with the “calculated effective dose”, the radiation exposure measured with thermoluminescence dosimetry was 131–170% higher. Noise in UHR mode was rated lower than in standard (all p≤0.042) and OBTCM images (all p≤0.028) for all dose levels, while image sharpness was deemed highest for UHR protocols (all p≤0.042). The use of OBTCM had no significant effect on either dimension of subjective image quality (all p≥0.999). Modulation transfer function analysis confirmed the highest spatial frequency in UHR datasets (all p≤0.016). ConclusionIn PCD-CT of the lung, full field-of-view UHR imaging entails no dose disadvantage over standard mode despite superior image quality. OBTCM possesses moderate dose saving potential. Thermoluminescence dosimetry yielded considerably higher effective doses than those calculated from dose-length products.

Improved Pulmonary Artery Evaluation Using High-Pitch Photon-Counting CT Compared to High-Pitch Conventional or Routine-Pitch Conventional Dual-Energy CT.

Pulmonary CT angiography (CTA) to detect pulmonary emboli can be performed using conventional dual-source CT with single-energy acquisition at high-pitch (high-pitch conventional CT), which minimizes motion artifacts, or routine-pitch, dual-energy acquisitions (routine-pitch conventional DECT), which maximize iodine signal. We compared iodine signal, radiation dose, and motion artifacts of pulmonary CTA between these conventional CT modalities and dual-source photon-counting detector CT with high-pitch, multienergy acquisitions (high-pitch photon-counting CT). Consecutive clinically indicated pulmonary CTA exams were collected. CT number/noise was measured from the main to right lower lobe segmental pulmonary arteries using 120 kV threshold low, 120 kV, and mixed kV (0.6 linear blend) images. Three radiologists reviewed anonymized, randomized exams, rating them using a 4- or 5-point Likert scale (1 = worst, and 4/5 = best) for contrast enhancement in pulmonary arteries, motion artifacts in aortic root to subsegmental pulmonary arteries, lung image quality; pulmonary blood volume (PBV) map image quality (for multienergy or dual-energy exams), and contribution to reader confidence. One hundred fifty patients underwent high-pitch photon-counting CT (n = 50), high-pitch conventional CT (n = 50), and routine-pitch conventional DECT (n = 50). High-pitch photon-counting CT had lower radiation dose (CTDIvol: 8.1 ± 2.5 vs 9.6 ± 6.8 and 16.2 ± 8.5 mGy, respectively; P < 0.001), and routine-pitch conventional DECT had significantly less contrast (P < 0.009). CT number and CNR measurements were significantly greater at high-pitch photon-counting CT (P < 0.001). Across readers, high-pitch photon-counting CT demonstrated significantly higher subjective contrast enhancement in the pulmonary arteries compared to the other modalities (4.7 ± 0.6 vs 4.4 ± 0.7 vs 4.3 ± 0.7; P = 0.011) and lung image quality (3.4 ± 0.5 vs 3.1 ± 0.5 vs 3.1 ± 0.5; P = 0.013). High-pitch photon-counting CT and high-pitch conventional CT had fewer motion artifacts at all levels compared to DECT (P < 0.001). High-pitch photon-counting CT PBV maps had superior image quality (P < 0.001) and contribution to reader confidence (P < 0.001) compared to routine-pitch conventional DECT. High-pitch photon-counting pulmonary CTA demonstrated higher contrast in pulmonary arteries at lower radiation doses with improved lung image quality and fewer motion artifacts compared to high-pitch conventional CT and routine-pitch conventional dual-energy CT.

Naloxone combined with non-invasive ventilation for the treatment of chronic obstructive pulmonary disease based on radioactive X-ray image inspection

The purpose of this study was to investigate the application of radiography in patients with COPD and analyze its value in diagnosis and evaluation. During radiological X-rays, patients wear specific lead protective equipment to reduce radiation exposure and obtain images from different angles and fields of view. The obtained X-ray images were evaluated and analyzed to examine the structural and morphological features of the lung tissue. In this study, the treatment scheme of naloxone combined with salbutamol combined with non-invasive ventilator was adopted, which improved the effectiveness and accuracy of treatment by supporting the computer medical image segmentation recognition system. The analysis results showed that the respiratory function of patients in naloxone combined with non-invasive ventilation treatment group was significantly improved after treatment, and the lung image characteristics also had positive changes. Compared with the conventional treatment group, naloxone combined with noninvasive ventilation has significant advantages in improving ventilation function and reducing pulmonary disease in COPD patients. Naloxone combined with non-invasive ventilation based on medical image recognition has the potential to improve the condition of COPD patients and predict treatment response. The results of this study provide a new idea for the treatment of COPD patients and provide a reference for further optimization of treatment.

Phase-Resolved Functional Lung MRI for Pulmonary Ventilation and Perfusion (V/Q) Assessment.

Fourier decomposition is a contrast agent-free 1H MRI method for lung perfusion (Q) and ventilation (V) assessment. After image registration, the time series of each voxel is analyzed with regard to the cardiac and breathing frequency components. Using a standard 2D spoiled gradient-echo sequence with a temporal resolution of ~300 ms, an image-sorting algorithm was developed to produce phase-resolved functional lung imaging (PREFUL) with an increased temporal resolution. Thus, it is feasible to evaluate regional flow volume loops (FVL) during tidal volume breathing and depict the propagation of the pulse wave during the cardiac cycle. This method can be applied at 1.5T or 3T with standard MR hardware without the necessity for sequence programming, as the described protocol can be implemented with the default SPGRE sequence on most systems. PREFUL ventilation MRI has been validated using 129Xe and 19F gas imaging with good regional agreement. Perfusion-weighted PREFUL MRI has been validated using SPECT as well as dynamic contrast enhanced (DCE) MRI. PREFUL has been tested in a dual center dual vendor setting and is currently applied in several ongoing multicenter trials. Furthermore, it is feasible across a range of field strengths (0.55T-3T) and different age groups, including newborns. Quantitative V/Q PREFUL MRI has been used in patients with cystic fibrosis, chronic obstructive pulmonary disease, chronic thromboembolic pulmonary hypertension, and corona virus disease-2019 to quantify disease and monitor treatment change after therapy. Furthermore, PREFUL V/Q imaging has been shown to predict transplant loss due to chronic lung allograft dysfunction in patients after lung transplantation. In summary, PREFUL MRI is a validated technique for quantitative ventilation and pulmonary pulse wave/perfusion imaging for regional pulmonary disease detection, quantification, and treatment monitoring with potential added value to the current clinical routine.

Deep learning ensemble approach with explainable AI for lung and colon cancer classification using advanced hyperparameter tuning

Lung and colon cancers are leading contributors to cancer-related fatalities globally, distinguished by unique histopathological traits discernible through medical imaging. Effective classification of these cancers is critical for accurate diagnosis and treatment. This study addresses critical challenges in the diagnostic imaging of lung and colon cancers, which are among the leading causes of cancer-related deaths worldwide. Recognizing the limitations of existing diagnostic methods, which often suffer from overfitting and poor generalizability, our research introduces a novel deep learning framework that synergistically combines the Xception and MobileNet architectures. This innovative ensemble model aims to enhance feature extraction, improve model robustness, and reduce overfitting.Our methodology involves training the hybrid model on a comprehensive dataset of histopathological images, followed by validation against a balanced test set. The results demonstrate an impressive classification accuracy of 99.44%, with perfect precision and recall in identifying certain cancerous and non-cancerous tissues, marking a significant improvement over traditional approach.The practical implications of these findings are profound. By integrating Gradient-weighted Class Activation Mapping (Grad-CAM), the model offers enhanced interpretability, allowing clinicians to visualize the diagnostic reasoning process. This transparency is vital for clinical acceptance and enables more personalized, accurate treatment planning. Our study not only pushes the boundaries of medical imaging technology but also sets the stage for future research aimed at expanding these techniques to other types of cancer diagnostics.

Intraoperative Lung Mechanics In Post-Covid Healthy Pregnants Who Required Cesarean Section: A Randomized Controlled Trial

Objective: This study aimed to investigate whether lung mechanics were affected in patients recovering from Covid-19 without ARDS who did not undergo lung imaging during the active infection period. Methods: The study included pregnant patients undergoing cesarean section operations. Participants were divided into two groups: those who had recovered from Covid-19 within the past year (group 1, n=50) and those who had never been infected with Covid-19 (group 2, n=50). The study included 100 patients divided into two groups: those who had recovered from Covid-19 within the last year (group 1, n=50) and those who had never experienced Covid-19 infection (group 2, n=50). Peak pressure (Ppeak), plateau pressure (Pplato), dynamic compliance (Cdyn), and positive end-expiratory pressure (PEEP) values, measured by the anesthesia machine, were recorded at specified time intervals following intubation. Results: Comparisons of Ppeak, Pplato, ΔP, Cdyn, and R data at specified times (1 min, 5 min, 10 min, 20 min, 30 min, and 40 min) showed no significant differences between the groups (p&gt;0.05). Conclusion: No significant differences in lung mechanics were found between the Covid-19-recovered patient group and the control group. Differences observed in SpO2, MAP, and Cdyn values in both groups are considered to be expected changes associated with routine procedures of general anesthesia

Impact of routine bedside lung ultrasound implementation on chest x-ray requests for mechanically ventilated children: a before-after uncontrolled quality improvement project.

Lung imaging techniques are crucial for managing ventilated patients in pediatric intensive care units (PICUs). Bedside chest x-ray has limitations such as low sensitivity and radiation exposure risks. Recently, lung ultrasound has emerged as a promising technology offering advantages such as real-time monitoring and radiation-free imaging. However, the integration of lung ultrasound into clinical practice raises questions about its impact on chest x-ray prescriptions. This study aims to assess whether implementing lung ultrasound reduces reliance on chest x-rays for ventilated pediatric patients in the PICU. This before-and-after uncontrolled quality improvement project was conducted from January 2022 to December 2023 in a referral PICU. The study included three phases: retrospective evaluation, learning phase, and prospective evaluation. Patients aged under 14years, intubated, and ventilated for ≤ 30days were included. Lung ultrasound was performed using a standardized protocol, and chest x-rays were conducted as per clinical indications. During the study period, 430 patients were admitted to the PICU, with 142 requiring mechanical ventilation. Implementation of routine bedside lung ultrasound led to a 39% reduction in chest x-ray requests (p < 0.001). Additionally, there was a significant decrease in irradiation exposure and a 27% reduction in costs associated with chest x-rays.Conclusion: Routine bedside lung ultrasound is a valuable tool in the modern PICU, it reduces the number of chest x-rays, with reduced radiation exposure and a potential cost savings. What is known: • Bedside chest x-ray is the main imaging study in ventilated pediatric patients • Chest x-ray is a valuable tool in pediatric critical care but it is associated with irradiation exposure What is new: • Implementation of bedside lung ultrasound in pediatric critical care unites reduces the chest x-rays requests and therefore patient-irradiation.

A systematic literature review on machine learning and deep learning-based covid-19 detection frameworks using X-ray Images

Coronavirus is an endangered disease to kills more than millions of people, but it has also put tremendous pressure on the whole medical system. The initial stage of identification of COVID-19 is necessary to isolate the patients with positive cases in order to stop the disease from spreading. The amalgamation of imaging techniques and deep learning algorithms takes less time and leads to more accurate outcomes for COVID-19 detection. Deep learning techniques have been employed by scientists to identify coronavirus infection in lung images during the COVID-19 worldwide epidemic. In this review, a review of the Covid-19 detection framework based on machine learning and deep learning techniques using X-ray images is done. First, the review of existing Covid-19 detection models is done. For this purpose, a detailed literature survey is carried out on Covid-19 detection papers from 2019 to 2023. Following the literature survey, the pre-processing procedures, the segmentation process, and the classification techniques used for Covid-19 detection using deep learning, machine learning, and optimization algorithms are reviewed and categorized. After that, the dataset and the implementation tool which are utilized for Covid-19 detection works are analyzed and grouped. Finally, the performance metrics validation such as accuracy, recall, F1-score, NPV, precision, sensitivity, and specificity is carried out. The research gaps in the existing Covid-19 detection techniques are provided further as references to aid in future works.

Explaining transfer learning models for the detection of COVID-19 on X-ray lung images

Amidst the coronavirus disease 2019 (COVID-19) pandemic, researchers are exploring innovative approaches to enhance diagnostic accuracy. One avenue is utilizing deep learning models to analyze lung X-ray images for COVID-19 diagnosis, complementing existing tests like reverse transcription polymerase chain reaction (RT-PCR). However, trusting these models, often viewed as black boxes, presents a challenge. To address this, six explainable artificial intelligence (XAI) techniques: local interpretable model agnostic explanations (LIME), Shapley additive explanations (SHAP), integrated gradients, smooth-grad, gradient-weighted class activation mapping (Grad-CAM), and Layer-CAM are applied to interpret four transfer learning models. These models: VGG16, ResNet50, InceptionV3, and DenseNet121 are analyzed to understand their workings and the rationale behind their predictions. Validating the results with medical experts poses difficulties due to time and resource constraints, alongside the scarcity of annotated X-ray datasets. To address this, a voting mechanism employing different XAI methods across various models is proposed. This approach highlights regions of lung infection, potentially reducing individual model biases stemming from their structures. If successful, this research could pave the way for an automated system for annotating infection regions, bolstering confidence in predictions and aiding in the development of more effective diagnostic tools for COVID-19.

Study on lung CT image segmentation algorithm based on threshold-gradient combination and improved convex hull method

Lung images often have the characteristics of strong noise, uneven grayscale distribution, and complex pathological structures, which makes lung image segmentation a challenging task. To solve this problems, this paper proposes an initial lung mask extraction algorithm that combines threshold and gradient. The gradient used in the algorithm is obtained by the time series feature extraction method based on differential memory (TFDM), which is obtained by the grayscale threshold and image grayscale features. At the same time, we also proposed a lung contour repair algorithm based on the improved convex hull method to solve the contour loss caused by solid nodules and other lesions. Experimental results show that on the COVID-19 CT segmentation dataset, the advanced lung segmentation algorithm proposed in this article achieves better segmentation results and greatly improves the consistency and accuracy of lung segmentation. Our method can obtain more lung information, resulting in ideal segmentation effects with improved accuracy and robustness.

Comparison of Deep Learning Methods for Detecting Tuberculosis Through Chest X-Rays

Chronic diseases are the leading cause of death worldwide, accounting for 73% of deaths in 2020. Tuberculosis (TB), caused by the bacterium Mycobacterium tuberculosis, is one of these diseases and has a significant impact on countries with a high TB burden due to a lack of radiologists and medical equipment. Early diagnosis of TB is crucial but challenging because of its similarity to lung cancer and the shortage of radiologists. A semi-automatic TB detection system is needed to support medical diagnosis and improve public health services. Deep learning technology, such as Convolutional Neural Networks (CNN), offers an effective solution for disease diagnosis with high accuracy. This study compares deep learning methods using an 8-layer CNN and VGG-19, both enhanced with Histogram Equalization (HE) for improved image quality. The study utilizes chest X-ray images of normal lungs and TB-affected lungs from Kaggle. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results indicate that the VGG-19 model outperforms the 8-layer CNN across all evaluation metrics, achieving an accuracy of 72.00% compared to 65.00% for the 8-layer CNN. VGG-19 also demonstrates better precision, recall, and F1-score, making it a more suitable choice for TB detection with enhanced image quality.

Technical challenges and benefits of photon counting detector computed tomography

X-ray detector is the essential part of a computed tomography (CT) system that controls dose efficiency and image quality. All clinical CT scanners utilized scintillating detectors up until the first clinical photon-counting-detector (PCD) system was approved in 2021. These detectors do not record information about individual photons during the two-step detection process. PCDs, on the other hand, employ a single step process that transforms X-ray radiation straight into an electrical signal. This retains information on individual photons, allowing one to count the quantity of X-rays in various energy ranges. Better spatial resolution, reduced dosages of iodinated contrast material, enhanced iodine signal, enhanced radiation dose efficiency, and the lack of electronic noise are the main benefits of PCDs. PCDs with multiple energy thresholds are able to separate detected photons into two or more energy bins, allowing for the availability of energy-resolved data for each record. In the event of dual-source CT, this permits high pitch or high temporal resolution acquisitions in addition to high spatial resolution for tasks involving material quantification or classification. PCDCT imaging of anatomy, where excellent spatial resolution provides clinical benefit, is one of the most promising uses of the technology. Inner ear, bone, small blood artery, heart, and lung imaging are among them. Photon-counting CT will become the wave of the future for workhorse CT imaging systems. An overview of the PCDCT principle, possible clinical benefits, and limitations of conventional CT is provided in this review paper, along with potential future developments for this CT imaging technology.

Radiation-free and injection-free imaging of the paediatric chest using a magnetic resonance imaging protocol including zero time echo sequence (3D-ZTE).

Despite its overall lower image quality compared to CT, and the additional years of experience required for accurate interpretation, the 3D-ZTE MRI sequence demonstrated excellent sensitivity for several lesions, making it an appropriate imaging method in certain indications. • Chest radiography and CT are the main imaging modalities for paediatric thoracic diseases but involve radiation exposure and CT often requires IV contrast. • MRI is promising for radiation-free lung imaging in children but faces challenges of low signal-to-noise ratio and motion artefacts. • An MRI protocol including a 3D zero echo time (ZTE) sequence allows satisfactory visualisation of lung parenchyma in 82% of children. • Despite overall inferior image quality compared to CT, MRI demonstrated excellent sensitivity for several lesions, making it an appropriate imaging method in certain indications.

Impact of undersampling on preclinical lung T2* mapping with 3D radial UTE MRI at 7 T

Lung diseases are almost invariably heterogeneous and progressive, making it imperative to capture temporally and spatially explicit information to understand the disease initiation and progression. Imaging the lung with MRI—particularly in the preclinical setting—has historically been challenging because of relatively low lung tissue density, rapid cardiac and respiratory motion, and rapid transverse (T2*) relaxation. These limitations can largely be mitigated using ultrashort-echo-time (UTE) sequences, which are intrinsically robust to motion and avoid significant T2* decay. A significant disadvantage of common radial UTE sequences is that they require inefficient, center-out k-space sampling, resulting in long acquisition times relative to conventional Cartesian sequences. Therefore, pulmonary images acquired with radial UTE are often undersampled to reduce acquisition time. However, undersampling reduces image SNR, introduces image artifacts, and degrades true image resolution. The level of undersampling is further increased if offline gating techniques like retrospective gating are employed, because only a portion (∼40–50%) of the data is used in the final image reconstruction. Here, we explore the impact of undersampling on SNR and T2* mapping in mouse lung imaging using simulation and in-vivo data. Increased scatter in both metrics was noticeable at around 50% sampling. Parenchymal apparent SNR only decreased slightly (average decrease ∼ 1.4) with as little as 10% sampling. Apparent T2* remained similar across undersampling levels, but it became significantly increased (p < 0.05) below 80% sampling. These trends suggest that undersampling can generate quantifiable, but moderate changes in the apparent value of T2*. Moreover, these approaches to assess the impact of undersampling are straightforward to implement and can readily be expanded to assess the quantitative impact of other MR acquisition and reconstruction parameters.

Imaging the Lung in ARDS: A Primer.

Despite periodic changes in the clinical definition of ARDS, imaging of the lung remains a central component of its diagnostic identification. Several imaging modalities are available to the clinician to establish a diagnosis of the syndrome, monitor its clinical course, or assess the impact of treatment and management strategies. Each imaging modality provides unique insight into ARDS from structural and/or functional perspectives. This review will highlight several methods for lung imaging in ARDS, emphasizing basic operational and physical principles for the respiratory therapist. Advantages and disadvantages of each modality will be discussed in the context of their utility for clinical management and decision-making.

Far-Red Aggregation-Induced Emission Hydrogel-Reinforced Tissue Clearing for 3D Vasculature Imaging of Whole Lung and Whole Tumor.

Understanding the vascular formation and distribution in metastatic lung tumors is a significant challenge due to autofluorescence, antibody/dye diffusion in dense tumor, and fluorophore stability when exposed to solvent-based clearing agents. Here, an approach is presented that redefines 3D vasculature imaging within metastatic tumor, peritumoral lung tissue, and normal lung. Specifically, a far-red aggregation-induced emission nanoparticle with surface amino groups (termed as TSCN nanoparticle, TSCNNP) is designed for in situ formation of hydrogel (TSCNNP@Gel) inside vasculatures to provide structural support and enhance the fluorescence in solvent-based tissue clearing method. Using this TSCNNP@Gel-reinforced tissue clearing imaging approach, the critical challenges are successfully overcome and comprehensive visualization of the whole pulmonary vasculature up to 2µm resolution is enabled, including its detailed examination in metastatic tumors. Importantly, features of tumor-associated vasculature in 3D panoramic views are unveiled, providing the potential to determine tumor stages, predict tumor progression, and facilitate the histopathological diagnosis of various tumor types.

Mapping the strain-stiffening behavior of the lung and lung cancer at microscale resolution using the crystal ribcage.

Lung diseases such as cancer substantially alter the mechanical properties of the organ with direct impact on the development, progression, diagnosis, and treatment response of diseases. Despite significant interest in the lung's material properties, measuring the stiffness of intact lungs at sub-alveolar resolution has not been possible. Recently, we developed the crystal ribcage to image functioning lungs at optical resolution while controlling physiological parameters such as air pressure. Here, we introduce a data-driven, multiscale network model that takes images of the lung at different distending pressures, acquired via the crystal ribcage, and produces corresponding absolute stiffness maps. Following validation, we report absolute stiffness maps of the functioning lung at microscale resolution in health and disease. For representative images of a healthy lung and a lung with primary cancer, we find that while the lung exhibits significant stiffness heterogeneity at the microscale, primary tumors introduce even greater heterogeneity into the lung's microenvironment. Additionally, we observe that while the healthy alveoli exhibit strain-stiffening of ∼1.75 times, the tumor's stiffness increases by a factor of six across the range of measured transpulmonary pressures. While the tumor stiffness is 1.4 times the lung stiffness at a transpulmonary pressure of three cmH2O, the tumor's mean stiffness is nearly five times greater than that of the surrounding tissue at a transpulmonary pressure of 18 cmH2O. Finally, we report that the variance in both strain and stiffness increases with transpulmonary pressure in both the healthy and cancerous lungs. Our new method allows quantitative assessment of disease-induced stiffness changes in the alveoli with implications for mechanotransduction.

Deep learning in pulmonary nodule detection and segmentation: a systematic review.

The accurate detection and precise segmentation of lung nodules on computed tomography are key prerequisites for early diagnosis and appropriate treatment of lung cancer. This study was designed to compare detection and segmentation methods for pulmonary nodules using deep-learning techniques to fill methodological gaps and biases in the existing literature. This study utilized a systematic review with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, searching PubMed, Embase, Web of Science Core Collection, and the Cochrane Library databases up to May 10, 2023. The Quality Assessment of Diagnostic Accuracy Studies 2 criteria was used to assess the risk of bias and was adjusted with the Checklist for Artificial Intelligence in Medical Imaging. The study analyzed and extracted model performance, data sources, and task-focus information. After screening, we included nine studies meeting our inclusion criteria. These studies were published between 2019 and 2023 and predominantly used public datasets, with the Lung Image Database Consortium Image Collection and Image Database Resource Initiative and Lung Nodule Analysis 2016 being the most common. The studies focused on detection, segmentation, and other tasks, primarily utilizing Convolutional Neural Networks for model development. Performance evaluation covered multiple metrics, including sensitivity and the Dice coefficient. This study highlights the potential power of deep learning in lung nodule detection and segmentation. It underscores the importance of standardized data processing, code and data sharing, the value of external test datasets, and the need to balance model complexity and efficiency in future research. Deep learning demonstrates significant promise in autonomously detecting and segmenting pulmonary nodules. Future research should address methodological shortcomings and variability to enhance its clinical utility. Deep learning shows potential in the detection and segmentation of pulmonary nodules. There are methodological gaps and biases present in the existing literature. Factors such as external validation and transparency affect the clinical application.

Prediction of early-phase cytomegalovirus pneumonia in post-stem cell transplantation using a deep learning model.

Diagnostic challenges exist for CMV pneumonia in post-hematopoietic stem cell transplantation (post-HSCT) patients, despite early-phase radiographic changes. The study aims to employ a deep learning model distinguishing CMV pneumonia from COVID-19 pneumonia, community-acquired pneumonia, and normal lungs post-HSCT. Initially, 6 neural network models were pre-trained with COVID-19 pneumonia, community-acquired pneumonia, and normal lung CT images from Kaggle's COVID multiclass dataset (Dataset A), then Dataset A was combined with the CMV pneumonia images from our center, forming Dataset B. We use a few-shot transfer learning strategy to fine-tune the pre-trained models and evaluate model performance in Dataset B. 34 cases of CMV pneumonia were found between January 2018 and December 2022 post-HSCT. Dataset A contained 1681 images of each subgroup from Kaggle. Combined with Dataset A, Dataset B was initially formed by 98 images of CMV pneumonia and normal lung. The optimal model (Xception) achieved an accuracy of 0.9034. Precision, recall, and F1-score all reached 0.9091, with an AUC of 0.9668 in the test set of Dataset B. This framework demonstrates the deep learning model's ability to distinguish rare pneumonia types utilizing a small volume of CT images, facilitating early detection of CMV pneumonia post-HSCT.

Epithelioid trophoblastic tumor with lung metastasis: A case report and literature review.

Epithelioid trophoblastic tumor (ETT) is an extremely rare variant of gestational trophoblastic neoplasms (GTNs). The biological behavior and therapeutic schedule of ETT remains to be defined which frequently poses diagnostic and therapeutic challenges. Although ETT is a relatively indolent malignancy tumor, the therapeutic efficacy and survival rate decrease significantly when presented with metastases. The lung is the most common site of ETT metastasis. A 39-year-old female patient presented with irregular vaginal bleeding and slight distention pain in lower abdomen. The patient was diagnosed ETT with lung metastasis after surgery and immunohistochemical staining. A total abdominal hysterectomy plus bilateral salpingectomy and histopathology were performed. The patient received 3 cycles of etoposide, methotrexate, actinomycin-D/etoposide, cisplatin (EMA/EP) regimen chemotherapy after surgery. Due to the presence of lung metastasis, she received pulmonary lesion resection and another cycle of postoperative chemotherapy. The patients showed a good response to treatment initially. However, the patient did not complete the full initial treatment for family reasons and had signs of recurrence after 2.5 months. The serum β-hCG level gradually elevated and the lung imaging showed that the lesion area gradually expanded. After 15 months of follow-up, the patient declined further treatment due to a lack of presenting symptoms. The diagnosis of ETT should be taken into consideration in patients with abnormal vaginal bleeding and low levels of β-hCG. Patients with metastatic disease should be treated with complete surgical resection and intensive combination chemotherapy to maximize the opportunity for cure. Targeted biological agents might be potential therapeutic strategies for chemotherapy-resistant or recurrent patients.