Pulmonary Nodules Research Articles

Better performance of deep learning pulmonary nodule detection using chest radiography with pixel level labels in reference to computed tomography: data quality matters

Labeling errors can significantly impact the performance of deep learning models used for screening chest radiographs. The deep learning model for detecting pulmonary nodules is particularly vulnerable to such errors, mainly because normal chest radiographs and those with nodules obscured by ribs appear similar. Thus, high-quality datasets referred to chest computed tomography (CT) are required to prevent the misclassification of nodular chest radiographs as normal. From this perspective, a deep learning strategy employing chest radiography data with pixel-level annotations referencing chest CT scans may improve nodule detection and localization compared to image-level labels. We trained models using a National Institute of Health chest radiograph-based labeling dataset and an AI-HUB CT-based labeling dataset, employing DenseNet architecture with squeeze-and-excitation blocks. We developed four models to assess whether CT versus chest radiography and pixel-level versus image-level labeling would improve the deep learning model’s performance to detect nodules. The models' performance was evaluated using two external validation datasets. The AI-HUB dataset with image-level labeling outperformed the NIH dataset (AUC 0.88 vs 0.71 and 0.78 vs.0.73 in two external datasets, respectively; both p < 0.001). However, the AI-HUB data annotated at the pixel level produced the best model (AUC 0.91 and 0.86 in external datasets), and in terms of nodule localization, it significantly outperformed models trained with image-level annotation data, with a Dice coefficient ranging from 0.36 to 0.58. Our findings underscore the importance of accurately labeled data in developing reliable deep learning algorithms for nodule detection in chest radiography.

A human-in-the-loop method for pulmonary nodule detection in CT scans

Automated pulmonary nodule detection using computed tomography scans is vital in the early diagnosis of lung cancer. Although extensive well-performed methods have been proposed for this task, they suffer from the domain shift issue between training and test images. Unsupervised domain adaptation (UDA) methods provide a promising means to mitigate the domain variance; however, their performance is still limited since no target domain supervision is introduced. To make the pulmonary nodule detection algorithm more applicable in clinical practice and further boost the performance across domains, we propose a human-in-the-loop method in a semi-supervised fashion to enhance the model generalization ability when transferred from source domain to target domain. Specifically, we first train a detector model on source domain, and then the pre-trained detector is utilized with our proposed uncertainty-guided sample selection scheme (USSS) to find a few target domain samples worth annotating most and obtain their human annotations. Finally, the annotated and the rest unlabeled target domain samples are used together to refine the pre-trained model via our proposed zoom-in and zoom-out constraint (ZZC) strategy. We evaluate our method on the Nodule Analysis 2016 (LUNA16) and TianChi datasets. Experimental results show that our method surpasses recent competitive methods on source domain and also achieves surprising performance on target domain.

Artificial intelligence-based graded training of pulmonary nodules for junior radiology residents and medical imaging students

BackgroundTo evaluate the efficiency of artificial intelligence (AI)-assisted diagnosis system in the pulmonary nodule detection and diagnosis training of junior radiology residents and medical imaging students.MethodsThe participants were divided into three groups. Medical imaging students of Grade 2020 in the Jinzhou Medical University were randomly divided into Groups 1 and 2; Group 3 comprised junior radiology residents. Group 1 used the traditional case-based teaching mode; Groups 2 and 3 used the ‘AI intelligent assisted diagnosis system’ teaching mode. All participants performed localisation, grading and qualitative diagnosed of 1,057 lung nodules in 420 cases for seven rounds of testing after training. The sensitivity and number of false positive nodules in different densities (solid, pure ground glass, mixed ground glass and calcification), sizes (less than 5 mm, 5–10 mm and over 10 mm) and positions (subpleural, peripheral and central) of the pulmonary nodules in the three groups were detected. The pathological results and diagnostic opinions of radiologists formed the criteria. The detection rate, diagnostic compliance rate, false positive number/case, and kappa scores of the three groups were compared.ResultsThere was no statistical difference in baseline test scores between Groups 1 and 2, and there were statistical differences with Group 3 (P = 0.036 and 0.011). The detection rate of solid, pure ground glass and calcified nodules; small-, medium-, and large-diameter nodules; and peripheral nodules were significantly different among the three groups (P<0.05). After seven rounds of training, the diagnostic compliance rate increased in all three groups, with the largest increase in Group 2. The average kappa score increased from 0.508 to 0.704. The average kappa score for Rounds 1–4 and 5–7 were 0.595 and 0.714, respectively. The average kappa scores of Groups 1,2 and 3 increased from 0.478 to 0.658, 0.417 to 0.757, and 0.638 to 0.791, respectively.ConclusionThe AI assisted diagnosis system is a valuable tool for training junior radiology residents and medical imaging students to perform pulmonary nodules detection and diagnosis.

Examining feature extraction and classification modules in machine learning for diagnosis of low-dose computed tomographic screening-detected in vivo lesions.

Medical imaging-based machine learning (ML) for computer-aided diagnosis of in vivo lesions consists of two basic components or modules of (i)feature extraction from non-invasively acquired medical images and (ii)feature classification for prediction of malignancy of lesions detected or localized in the medical images. This study investigates their individual performances for diagnosis of low-dose computed tomography (CT) screening-detected lesions of pulmonary nodules and colorectal polyps. Three feature extraction methods were investigated. One uses the mathematical descriptor of gray-level co-occurrence image texture measure to extract the Haralick image texture features (HFs). One uses the convolutional neural network (CNN) architecture to extract deep learning (DL) image abstractive features (DFs). The third one uses the interactions between lesion tissues and X-ray energy of CT to extract tissue-energy specific characteristic features (TFs). All the above three categories of extracted features were classified by the random forest (RF) classifier with comparison to the DL-CNN method, which reads the images, extracts the DFs, and classifies the DFs in an end-to-end manner. The ML diagnosis of lesions or prediction of lesion malignancy was measured by the area under the receiver operating characteristic curve (AUC). Three lesion image datasets were used. The lesions' tissue pathological reports were used as the learning labels. Experiments on the three datasets produced AUC values of 0.724 to 0.878 for the HFs, 0.652 to 0.965 for the DFs, and 0.985 to 0.996 for the TFs, compared to the DL-CNN of 0.694 to 0.964. These experimental outcomes indicate that the RF classifier performed comparably to the DL-CNN classification module and the extraction of tissue-energy specific characteristic features dramatically improved AUC value. The feature extraction module is more important than the feature classification module. Extraction of tissue-energy specific characteristic features is more important than extraction of image abstractive and characteristic features.

Effects of pregabalin combined with tramadol/paracetamol on acute pain in patients with CT-guided puncture localization of pulmonary nodules

ObjectiveTo investigate the effects of pregabalin combined with tramadol/paracetamol on acute pain in patients with CT-guided puncture localization of pulmonary nodules. Materials and MethodsIn this randomized, placebo-controlled and single-center study, 120 patients were allocated randomly to four groups: the control group (Group P), the pregabalin-placebo group (Group BP), the tramadol/paracetamol-placebo group (Group AP), and the pregabalin-tramadol/paracetamol group (Group AB). The primary outcome was the NRS (Numerical Rating Scale) score. Other outcomes included systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate (HR), pulse oxygen saturation (SpO2), the incidence of moderate to severe pain, the analgesia recovery ratio, the incidence of adverse drug reactions and patients’ satisfaction. ResultsNo significant interaction was detected between the interventions (P = 0.752). The NRS score of the Taking pregabalin group and the Taking tramadol/paracetamol group were significantly lower than those of the Not-taking pregabalin group and the Not-taking tramadol/paracetamol group respectively (P < 0.05). There was significant difference in the NRS scores among the four groups (P < 0.001). The NRS score of Group AB was significantly lower than that of Group P (P < 0.001), Group BP (P < 0.001) and Group AP (P = 0.001). At the same time, the NRS scores of Group BP (P < 0.001) and Group AP (P < 0.001) were significantly lower than those of Group P, but there was no significant difference between Group BP and Group AP (P = 1.000). The SBP, DBP, HR, the incidence of moderate to severe pain and the analgesia recovery ratio of Group AB were significantly lower than those of Group P (P < 0.05), while the SpO2 and the number of people who were very satisfied were significantly higher than those of Group P (P < 0.05). There was no significant difference in the incidence of adverse drug reactions among the four groups (P = 0.272). ConclusionsThe combination or single use of pregabalin and tramadol/paracetamol can effectively relieve the acute pain after localization. Pregabalin combined with tramadol/paracetamol has the best analgesic effect and significantly reduces the hemodynamic fluctuations, with high safety and low incidence of adverse drug reactions, which has a certain clinical popularization and application value.

Pulmonary nodule detection in low dose computed tomography using a medical-to-medical transfer learning approach.

Lung cancer is the second most common cancer and the leading cause of cancer death globally. Low dose computed tomography (LDCT) is the recommended imaging screening tool for the early detection of lung cancer. A fully automated computer-aided detection method for LDCT will greatly improve the existing clinical workflow. Most of the existing methods for lung detection are designed for high-dose CTs (HDCTs), and those methods cannot be directly applied to LDCTs due to domain shifts and inferior quality of LDCT images. In this work, we describe a semi-automated transfer learning-based approach for the early detection of lung nodules using LDCTs. In this work, we developed an algorithm based on the object detection model, you only look once (YOLO) to detect lung nodules. The YOLO model was first trained on CTs, and the pre-trained weights were used as initial weights during the retraining of the model on LDCTs using a medical-to-medical transfer learning approach. The dataset for this study was from a screening trial consisting of LDCTs acquired from 50 biopsy-confirmed lung cancer patients obtained over 3 consecutive years (T1, T2, and T3). About 60 lung cancer patients' HDCTs were obtained from a public dataset. The developed model was evaluated using a hold-out test set comprising 15 patient cases (93 slices with cancerous nodules) using precision, specificity, recall, and F1-score. The evaluation metrics were reported patient-wise on a per-year basis and averaged for 3 years. For comparative analysis, the proposed detection model was trained using pre-trained weights from the COCO dataset as the initial weights. A paired t-test and chi-squared test with an alpha value of 0.05 were used for statistical significance testing. The results were reported by comparing the proposed model developed using HDCT pre-trained weights with COCO pre-trained weights. The former approach versus the latter approach obtained a precision of 0.982 versus 0.93 in detecting cancerous nodules, specificity of 0.923 versus 0.849 in identifying slices with no cancerous nodules, recall of 0.87 versus 0.886, and F1-score of 0.924 versus 0.903. As the nodule progressed, the former approach achieved a precision of 1, specificity of 0.92, and sensitivity of 0.930. The statistical analysis performed in the comparative study resulted in a -value of 0.0054 for precision and a -value of 0.00034 for specificity. In this study, a semi-automated method was developed to detect lung nodules in LDCTs using HDCT pre-trained weights as the initial weights and retraining the model. Further, the results were compared by replacing HDCT pre-trained weights in the above approach with COCO pre-trained weights. The proposed method may identify early lung nodules during the screening program, reduce overdiagnosis and follow-ups due to misdiagnosis in LDCTs, start treatment options in the affected patients, and lower the mortality rate.

Different clinicopathological features between young and older patients with pulmonary adenocarcinoma and ground-glass opacity

After the recommendation of computed tomography as a routine procedure for lung cancer screening, an increasing number of young adults have been diagnosed with pulmonary ground-glass opacity (GGO). Up to 63% of pulmonary nodules with a GGO component can be malignant. Since young cancer patients have limited exposure to environmental mutagens, they have special characteristics and needs. This study sought to compare the clinicopathological characteristics of young and old patients with GGO-associated lung adenocarcinoma (GGO-LUAD). Clinicopathological data from 203 patients who underwent video-assisted thoracoscopic surgery between January 2018 and April 2020 for pulmonary GGO component nodules were reviewed. Lung nonmucinous adenocarcinoma patients younger than 40 years old and older than 40 years old were enrolled: 103 patients ≤ 40 years old and 100 patients > 40 years old. The relevant clinicopathological features, including sex, smoking status, tumor size, pathological characteristics, radiographic features and prognosis of pulmonary nodules, were evaluated. Univariate analyses were applied for comparisons between groups. The differences in baseline characteristics (sex, smoking status, tumor location) between the different age groups were not significant. Young patients were more likely to have tumors < 1 cm in size, while older patients predominantly had tumors > 2 cm in size. The mean percentage of invasive adenocarcinoma was greater in the elderly group. Young and older patients seemed to have similar subtypes of adenocarcinoma (p > 0.05) but had different degrees of differentiation (p < 0.001). The 3-year overall survival (OS) and recurrence-free survival (RFS) of the young group were 100% and 99.03%, respectively, while the 3-years OS and RFS of the older group were 99% and 98%, respectively. Our work revealed that young patients with malignant pulmonary nodules and GGOs have distinct pathological subtypes. Patients with GGOs of different ages have different clinicopathological characteristics. The 3-year prognosis of young patients with malignant pulmonary nodules with GGOs is satisfactory.

Prediction of the Benign or Malignant Nature of Pulmonary Pure Ground-Glass Nodules Based on Radiomics Analysis of High-Resolution Computed Tomography Images.

To evaluate the efficacy of radiomics features extracted from preoperative high-resolution computed tomography (HRCT) scans in distinguishing benign and malignant pulmonary pure ground-glass nodules (pGGNs), a retrospective study of 395 patients from 2016 to 2020 was conducted. All nodules were randomly divided into the training and validation sets in the ratio of 7:3. Radiomics features were extracted using MaZda software (version 4.6), and the least absolute shrinkage and selection operator (LASSO) was employed for feature selection. Significant differences were observed in the training set between benign and malignant pGGNs in sex, mean CT value, margin, pleural retraction, tumor-lung interface, and internal vascular change, and then the mean CT value and the morphological features model were constructed. Fourteen radiomics features were selected by LASSO for the radiomics model. The combined model was developed by integrating all selected radiographic and radiomics features using logistic regression. The AUCs in the training set were 0.606 for the mean CT value, 0.718 for morphological features, 0.756 for radiomics features, and 0.808 for the combined model. In the validation set, AUCs were 0.601, 0.692, 0.696, and 0.738, respectively. The decision curves showed that the combined model demonstrated the highest net benefit.

Volumetric Analysis: Effect on Diagnosis and Management of Indeterminate Solid Pulmonary Nodules in Routine Clinical Practice.

To evaluate the effect of volumetric analysis on the diagnosis and management of indeterminate solid pulmonary nodules in routine clinical practice. This was a retrospective study with 107 computed tomography (CT) cases of solid pulmonary nodules (range, 6-15 mm), 57 pathology-proven malignancies (lung cancer, n = 34; metastasis, n = 23), and 50 benign nodules. Nodules were evaluated on a total of 309 CT scans (average number of CTs/nodule, 2.9 [range, 2-7]). CT scans were from multiple institutions with variable technique. Nine radiologists (attendings, n = 3; fellows, n = 3; residents, n = 3) were asked their level of suspicion for malignancy (low/moderate or high) and management recommendation (no follow-up, CT follow-up, or care escalation) for baseline and follow-up studies first without and then with volumetric analysis data. Effect of volumetry on diagnosis and management was assessed by generalized linear and logistic regression models. Volumetric analysis improved sensitivity (P = 0.009) and allowed earlier recognition (P < 0.05) of malignant nodules. Attending radiologists showed higher sensitivity in recognition of malignant nodules (P = 0.03) and recommendation of care escalation (P < 0.001) compared with trainees. Volumetric analysis altered management of high suspicion nodules only in the fellow group (P = 0.008). κ Statistics for suspicion for malignancy and recommended management were fair to substantial (0.38-0.66) and fair to moderate (0.33-0.50). Volumetric analysis improved interobserver variability for identification of nodule malignancy from 0.52 to 0.66 (P = 0.004) only on the second follow-up study. Volumetric analysis of indeterminate solid pulmonary nodules in routine clinical practice can result in improved sensitivity and earlier identification of malignant nodules. The effect of volumetric analysis on management recommendations is variable and influenced by reader experience.

Histoplasmosis in non-immunosuppressed patients from an endemic area in Northeastern Brazil.

Differently from immunocompromised patients, very little information is available in the literature regarding the clinical presentation, epidemiology, and outcomes of histoplasmosis in non-immunosuppressed individuals living in endemic areas. This retrospective case series study was carried out by reviewing the medical records of non-immunocompromised patients with histoplasmosis, residents in a hyperendemic area in northeastern Brazil, between 2011 and 2022. Thirty HIV-negative patients were identified with histoplasmosis, and 19 cases met the inclusion criteria: three had acute, five subacute and one chronic pulmonary forms; two with mediastinal picture and eight had disseminated disease (two with severe symptoms). The median age of our sample was 32.7 years old [interquartile range: 24-45]. Most of the patients were male (male-to-female ratio=15:4) and resided in the state capital (n=9). The majority had a previous history of exposure to well-known risk factors for Histoplasma infection. Pulmonary nodules were observed in all subacute form, two patients (acute and subacute forms) were initially treated empirically for pulmonary tuberculosis; one death was registered in the subacute form. The chronic pulmonary form of histoplasmosis was diagnosed in one patient only after the symptoms persisted despite specific treatment. The primary clinical manifestations of the moderate form of DH were enlarged lymph nodes, with histopathology being the main diagnostic method. The cases were detected as isolated occurrences and not as an outbreak, suggesting that exposure to Histoplasma can be more widespread than presumed. Despite the self-limiting nature of the disease, death can occur even in previously heathy patients.

Contemporary Concise Review 2023: Advances in lung cancer and interventional pulmonology.

Eligibility criteria for lung cancer screening increasingly need to consider family history of lung cancer, as well as age and smoking status. Lung cancer screening will reveal a multitude of incidental findings, of variable clinical significance, and with a need for clear pathways of management. Pulmonary nodule sampling is enhanced by intra-procedural imaging and cutting-edge robotic technology. Systematic thoracic lymph node sampling has implications for treatment efficacy. Bronchoscopic ablative techniques are feasible for peripheral lung cancers. Bronchoscopic sampling continues to have a high yield for lung cancer molecular characterization. Immunotherapy indications have expanded to include early stage and resectable lung cancer.

The diagnostic value of CT-based radiomics nomogram for solitary indeterminate smoothly marginated solid pulmonary nodules.

This study aimed to explore the value of radiomics nomogram based on computed tomography (CT) on the diagnosis of benign and malignant solitary indeterminate smoothly marginated solid pulmonary nodules (SMSPNs). This study retrospectively reviewed 205 cases with solitary indeterminate SMSPNs on CT, including 112 cases of benign nodules and 93 cases of malignant nodules. They were divided into training (n=143) and validation (n=62) cohorts based on different CT scanners. Radiomics features of the nodules were extracted from the lung window CT images. The variance threshold method, SelectKBest, and least absolute shrinkage and selection operator were used to select the key radiomics features to construct the rad-score. Through multivariate logistic regression analysis, a nomogram was built by combining rad-score, clinical factors, and CT features. The nomogram performance was evaluated by the area under the receiver operating characteristic curve (AUC). A total of 19 radiomics features were selected to construct the rad-score, and the nomogram was constructed by the rad-score, one clinical factor (history of malignant tumor), and three CT features (including calcification, pleural retraction, and lobulation). The nomogram performed better than the radiomics model, clinical model, and experienced radiologists who specialized in thoracic radiology for nodule diagnosis. The AUC values of the nomogram were 0.942 in the training cohort and 0.933 in the validation cohort. The calibration curve and decision curve showed that the nomogram demonstrated good consistency and clinical applicability. The CT-based radiomics nomogram achieved high efficiency in the preoperative diagnosis of solitary indeterminate SMSPNs, and it is of great significance in guiding clinical decision-making.

Assessing the impact of nodule features and software algorithm on pulmonary nodule measurement uncertainty for nodules sized 20 mm or less.

Measurements are not exact, so that if a measurement is repeated, one would get a different value each time. The spread of these values is the measurement uncertainty. Understanding measurement uncertainty of pulmonary nodules is important for proper interpretation of size and growth measurements. Larger amounts of measurement uncertainty may require longer follow-up intervals to be confident that any observed growth is due to actual growth rather than measurement uncertainty. We examined the influence of nodule features and software algorithm on measurement uncertainty of small, solid pulmonary nodules. Volumes of 107 nodules were measured on 4-6 repeated computed tomography (CT) scans (Siemens Somatom AS, 100 kVp, 120 mA, 1.0 mm slice thickness reconstruction) prospectively obtained during CT-guided fine needle aspiration biopsy between 2015-2021 at Department of Diagnostic, Molecular, and Interventional Radiology in Icahn School of Medicine at Mount Sinai, using two different automated volumetric algorithms. For each, the coefficient of variation (standard deviation divided by the mean) of nodule volume measurements was determined. The following features were considered: diameter, location, vessel and pleural attachments, nodule surface area, and extent of the nodule in the three acquisition dimensions of the scanner. Median volume of 107 nodules was 515.23 and 535.53 mm3 for algorithm #1 and #2, respectively with excellent agreement (intraclass correlation coefficient =0.98). Median coefficient of variation of nodule volume was low for the two algorithms, but significantly different (4.6% vs. 8.7%, P<0.001). Both algorithms had a trend of decreasing coefficient of variation of nodule volume with increasing nodule diameter, though only significant for algorithm #2. Coefficient of variation of nodule volume was significantly associated with nodule volume (P=0.02), attachment to blood vessels (P=0.02), and nodule surface area (P=0.001) for algorithm #2 using a multiple linear regression model. Correlation between the coefficient of variation (CoV) of nodule volume and the CoV of the x, y, z measurements for algorithm #1 were 0.29 (P=0.0021), 0.25 (P=0.009), and 0.80 (P<0.001) respectively, and for algorithm #2, 0.46 (P<0.001), 0.52 (P<0.001), and 0.58 (P<0.001), respectively. Even in the best-case scenario represented in this study, using the same measurement algorithm, scanner, and scanning protocol, considerable measurement uncertainty exists in nodule volume measurement for nodules sized 20 mm or less. We found that measurement uncertainty was affected by interactions between nodule volume, algorithm, and shape complexity.

A multiscale 3D network for lung nodule detection using flexible nodule modeling.

Lung cancer is the most common type of cancer. Detection of lung cancer at an early stage can reduce mortality rates. Pulmonary nodules may represent early cancer and can be identified through computed tomography (CT) scans. Malignant risk can be estimated based on attributes like size, shape, location, and density. Deep learning algorithms have achieved remarkable advancements in this domain compared to traditional machine learning methods. Nevertheless, many existing anchor-based deep learning algorithms exhibit sensitivity to predefined anchor-box configurations, necessitating manual adjustments to obtain optimal outcomes. Conversely, current anchor-free deep learning-based nodule detection methods normally adopt fixed-size nodule models like cubes or spheres. To address these technical challenges, we propose a multiscale 3D anchor-free deep learning network (M3N) for pulmonary nodule detection, leveraging adjustable nodule modeling (ANM). Within this framework, ANM empowers the representation of target objects in an anisotropic manner, with a novel point selection strategy (PSS) devised to accelerate the learning process of anisotropic representation. We further incorporate a composite loss function that combines the conventional L2 loss and cosine similarity loss, facilitating M3N to learn nodules' intensity distribution in three dimensions. Experiment results show that the M3N achieves 90.6% competitive performance metrics (CPM) with seven predefined false positives per scan on the LUNA 16 dataset. This performance appears to exceed that of other state-of-the-art deep learning-based networks reported in their respective publications. Individual test results also demonstrate that M3N excels in providing more accurate, adaptive bounding boxes surrounding the contours of target nodules. The newly developed nodule detection system reduces reliance on prior knowledge, such as the general size of objects in the dataset, thus it should enhance overall robustness and versatility. Distinct from traditional nodule modeling techniques, the ANM approach aligns more closely with the morphological characteristics of nodules. Time consumption and detection results demonstrate promising efficiency and accuracy which should be validated in clinical settings.

Patient satisfaction with letter-based communication of LCS pulmonary nodule results.

To analyze patient satisfaction with letter-based communication of lung cancer screening (LCS) pulmonary nodule results. Prospective randomized controlled trial of LCS between May and December 2019. All participants came from a prospective randomized controlled study on pulmonary nodule results in LCS with low-dose CT (LDCT) to analyze patient satisfaction, perception of information received via letters, preferred methods of receiving results, and dissatisfaction-relatedcharacteristics. A total of 153 patients were detected to have pulmonary nodules among 600 recruited participants in the lung cancer high-risk group screened using LDCT. Most of the patients were satisfied with receiving pulmonary nodule results via letters (78.4%; n = 120) and agreed that the letters contained an appropriate amount of information (83.7%; n = 128). Univariate logistic regression analysis revealed that satisfaction was related to age (OR, 0.905; 95% CI, 0.832-0.985), education level (OR, 0.367; 95% CI, 0.041-3.250), no family history of cancer (OR, 0.100; 95% CI, 0.011-0.914), and the number of nodules (OR, 6.028; 95% CI, 1.641-22.141). Of the patients who reported dissatisfaction with letter-based communication (7.2%; n = 11), the most common reasons cited were that they contained insufficient patient education materials and that it was difficult to comprehend the medical terminology. The majority of participants (61.4%; n = 94) reported that they would prefer the letter-based communication. No correlation was identified between satisfaction and gender, smoking status, alcohol consumption, risk factors, nodule size, or nodule location. Patients were generally satisfied with receiving their LCS pulmonary nodule results via letters, reporting that the letters included adequate information about their diagnosis and follow-up steps. This may provide a basis for feasible result communication via letters for cancer screening programs in underdeveloped regions inChina.

A rare family outbreak of Mycobacterium abscessus infection in immunocompetent fraternal triplets

BackgroundMycobacterium abscessus (M. abscessus) infection is rare in children who were previously healthy, particularly in infants. We present the first report of a family outbreak of M. abscessus infection among immunocompetent infant triplets. MethodsWe reviewed triplets’ demographic data, laboratory tests and imaging examinations to describe their clinical features. We performed whole-exome sequencing to rule out primary immunodeficiency disorders. We used DNA sequencing for M. abscessus subspecies identification. ResultsThe fraternal triplets (triples A, B and C) presented with a 10-day history of cough. Triple A also experienced a brief episode of fever, and triple B had tachypnea. Chest CT scans showed pulmonary masses and nodules in triples A and C, and cavities in triple B. Cultures of sputum and bronchoalveolar lavage fluid from all triplets yielded M. abscessus. Further subspecies identification showed that isolates from triples A and C were M. abscessus subsp. massiliense, and isolates from triple B were M. abscessus subsp. abscessus (MAA). After eight months of combination therapy, the pulmonary lesions of the triplets improved significantly. ConclusionOur study confirms that M. abscessus pulmonary disease can occur in immunocompetent infants. We hypothesize that the simultaneous infection of the triplets may be associated with their prematurity and extensive environmental exposure. This study highlights the importance to include M. abscessus infection in the differential diagnosis of pulmonary masses and/or cavities, regardless of the age of onset or the presence of underlying pathology or susceptible genes.

Construction and validation of a risk score system for diagnosing invasive adenocarcinoma presenting as pulmonary pure ground-glass nodules: a multi-center cohort study in China.

Anxiety-driven clinical interventions have been queried due to the nondeterminacy of pure ground-glass nodules (pGGNs). Although radiomics and radiogenomics aid diagnosis, standardization and reproducibility challenges persist. We aimed to assess a risk score system for invasive adenocarcinoma in pGGNs. In a retrospective, multi-center study, 772 pGGNs from 707 individuals in The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital were grouped into training (509 patients with 558 observations) and validation (198 patients with 214 observations) sets consecutively from January 2017 to November 2021. An additional test set of 143 observations in Hainan Cancer Hospital was analyzed in the same period. Computed tomography (CT) signs and clinical features were manually collected, and the quantitative parameters were achieved by artificial intelligence (AI). The positive cutoff score was ≥3. Risk scores system 3 combined carcinoma history, chronic obstructive pulmonary disease (COPD), maximum diameters, nodule volume, mean CT values, type II or III vascular supply signs, and other radiographic characteristics. The evaluation included the area under the curves (AUCs), accuracy, sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV) for both the risk score systems 1, 2, 3 and the AI model. The risk score system 3 [AUC, 0.840; 95% confidence interval (CI): 0.789-0.890] outperformed the AI model (AUC, 0.553; 95% CI: 0.487-0.619), risk score system 1 (AUC, 0.802; 95% CI: 0.754-0.851), and risk score system 2 (AUC, 0.816; 95% CI: 0.766-0.867), with 88.0% (0.850-0.904) accuracy, 95.6% (0.932-0.972) PPV, 0.620 (0.535-0.702) NPV, 89.6% (0.864-0.920) sensitivity, and 80.6% (0.717-0.872) specificity in the training sets. In the validation and test sets, risk score system 3 performed best with AUCs of 0.769 (0.678-0.860) and 0.801 (0.669-0.933). An AI-based risk scoring system using quantitative image parameters, clinical features, and radiographic characteristics effectively predicts invasive adenocarcinoma in pulmonary pGGNs.

Nomogram for predicting invasive lung adenocarcinoma in small solitary pulmonary nodules.

This study aimed to construct a clinical prediction model and nomogram to differentiate invasive from non-invasive pulmonary adenocarcinoma in solitary pulmonary nodules (SPNs). We analyzed computed tomography and clinical features as well as preoperative biomarkers in 1,106 patients with SPN who underwent pulmonary resection with definite pathology at Qilu Hospital of Shandong University between January 2020 and December 2021. Clinical parameters and imaging characteristics were analyzed using univariate and multivariate logistic regression analyses. Predictive models and nomograms were developed and their recognition abilities were evaluated using receiver operating characteristic (ROC) curves. The clinical utility of the nomogram was evaluated using decision curve analysis (DCA). The final regression analysis selected age, carcinoembryonic antigen, bronchus sign, lobulation, pleural adhesion, maximum diameter, and the consolidation-to-tumor ratio as associated factors. The areas under the ROC curves were 0.844 (95% confidence interval [CI], 0.817-0.871) and 0.812 (95% CI, 0.766-0.857) for patients in the training and validation cohorts, respectively. The predictive model calibration curve revealed good calibration for both cohorts. The DCA results confirmed that the clinical prediction model was useful in clinical practice. Bias-corrected C-indices for the training and validation cohorts were 0.844 and 0.814, respectively. Our predictive model and nomogram might be useful for guiding clinical decisions regarding personalized surgical intervention and treatment options.

Reinforcement learning for individualized lung cancer screening schedules: A nested case–control study

AbstractBackgroundThe current guidelines for managing screen‐detected pulmonary nodules offer rule‐based recommendations for immediate diagnostic work‐up or follow‐up at intervals of 3, 6, or 12 months. Customized visit plans are lacking.PurposeTo develop individualized screening schedules using reinforcement learning (RL) and evaluate the effectiveness of RL‐based policy models.MethodsUsing a nested case–control design, we retrospectively identified 308 patients with cancer who had positive screening results in at least two screening rounds in the National Lung Screening Trial. We established a control group that included cancer‐free patients with nodules, matched (1:1) according to the year of cancer diagnosis. By generating 10,164 sequence decision episodes, we trained RL‐based policy models, incorporating nodule diameter alone, combined with nodule appearance (attenuation and margin) and/or patient information (age, sex, smoking status, pack‐years, and family history). We calculated rates of misdiagnosis, missed diagnosis, and delayed diagnosis, and compared the performance of RL‐based policy models with rule‐based follow‐up protocols (National Comprehensive Cancer Network guideline; China Guideline for the Screening and Early Detection of Lung Cancer).ResultsWe identified significant interactions between certain variables (e.g., nodule shape and patient smoking pack‐years, beyond those considered in guideline protocols) and the selection of follow‐up testing intervals, thereby impacting the quality of the decision sequence. In validation, one RL‐based policy model achieved rates of 12.3% for misdiagnosis, 9.7% for missed diagnosis, and 11.7% for delayed diagnosis. Compared with the two rule‐based protocols, the three best‐performing RL‐based policy models consistently demonstrated optimal performance for specific patient subgroups based on disease characteristics (benign or malignant), nodule phenotypes (size, shape, and attenuation), and individual attributes.ConclusionsThis study highlights the potential of using an RL‐based approach that is both clinically interpretable and performance‐robust to develop personalized lung cancer screening schedules. Our findings present opportunities for enhancing the current cancer screening system.

Deep learning in distinguishing pulmonary nodules as benign and malignant

Deep learning in distinguishing pulmonary nodules as benign and malignant