Computer-aided diagnosis of distal metastasis in non-small cell lung cancer by low-dose CT based radiomics and deep learning signatures.

BACKGROUND: This study aimed to develop and validate radiomics and deep learning (DL) signatures for predicting distal metastasis (DM) of non-small cell lung cancer (NSCLC) in low-dose computed tomography (LDCT). METHODS: Images and clinical data were retrospectively collected for 381 NSCLC patients and prospectively collected for 114 patients at the Fifth Affiliated Hospital of Sun Yat-Sen University. Additionally, we enrolled 179 patients from the Jiangmen Central Hospital to externally validate the signatures. Machine-learning algorithms were employed to develop radiomics signature while the DL signature was developed using neural architecture search. The diagnostic efficiency was primarily quantified with the area under receiver operating characteristic curve (AUC). We interpreted the reasoning process of the radiomics signature and DL signature by radiomics voxel mapping and attention weight tracking. RESULTS: A total of 674 patients with pathologically-confirmed NSCLC were included from two institutions, with 143 of them having DM. The radiomics signature achieved AUCs of 0.885, 0.854, and 0.733 in the internal validation, prospective validation, and external validation while those for DL signature were 0.893, 0.786, and 0.780. The proposed signatures achieved a promising performance in predicting the DM of NSCLC and outperformed the approaches proposed in previous studies. Interpretability analysis revealed that both radiomics and DL signatures could detect the variations among voxels inside tumors, which helped in identifying the DM of NSCLC. CONCLUSIONS: Our study demonstrates the potential of LDCT-based radiomics and DL signatures for predicting DM in NSCLC. These signatures could help improve lung cancer screening regarding further diagnostic tests and treatment strategies.

Targeted Nanophotoimmunotherapy Potentiates Cancer Treatment by Enhancing Tumor Immunogenicity and Improving the Immunosuppressive Tumor Microenvironment.

Cancer immunotherapy, such as immune checkpoint blockade, chimeric antigen receptor, and cytokine therapy, has emerged as a robust therapeutic strategy activating the host immune system to inhibit primary and metastatic lesions. However, low tumor immunogenicity (LTI) and immunosuppressive tumor microenvironment (ITM) severely compromise the killing effect of immune cells on tumor cells, which fail to evoke a strong and effective immune response. As an exogenous stimulation therapy, phototherapy can induce immunogenic cell death (ICD), enhancing the therapeutic effect of tumor immunotherapy. However, the lack of tumor targeting and the occurrence of immune escape significantly reduce its efficacy in vivo, thus limiting its clinical application. Nanophotoimmunotherapy (nano-PIT) is a precision-targeted tumor treatment that co-loaded phototherapeutic agents and various immunotherapeutic agents by specifically targeted nanoparticles (NPs) to improve the effectiveness of phototherapy, reduce its phototoxicity, enhance tumor immunogenicity, and reverse the ITM. This review will focus on the theme of nano-PIT, introduce the current research status of nano-PIT on converting "cold" tumors to "hot" tumors to improve immune efficacy according to the classification of immunotherapy targets, and discuss the challenges, opportunities, and prospects.

Radiomics Nomogram with Added Nodal Features Improves Treatment Response Prediction in Locally Advanced Esophageal Squamous Cell Carcinoma: A Multicenter Study.

OBJECTIVE: We aimed to develop and validate a radiomics nomogram and determine the value of radiomic features from lymph nodes (LNs) for predicting pathological complete response (pCR) to neoadjuvant chemoradiotherapy (NCRT) in patients with locally advanced esophageal squamous cell carcinoma (ESCC). METHODS: In this multicenter retrospective study, eligible participants who had undergone NCRT followed by radical esophagectomy were consecutively recruited. Three radiomics models (modelT, modelLN, and modelTLN) based on tumor and LN features, alone and combined, were developed in the training cohort. The radiomics nomogram was developed by incorporating the prediction value of the radiomics model and clinicoradiological risk factors using multivariate logistic regression, and was evaluated using the receiver operating characteristic curve, validated in two external validation cohorts. RESULTS: Between October 2011 and December 2018, 116 patients were included in the training cohort. Between June 2015 and October 2020, 51 and 27 patients from two independent hospitals were included in validation cohorts 1 and 2, respectively. The radiomics modelTLN performed better than the radiomics modelT for predicting pCR. The radiomics nomogram incorporating the predictive value of the radiomics modelTLN and heterogeneous after NCRT outperformed the clinicoradiological model, with an area under the curve (95% confidence interval) of 0.833 (0.765-0.894) versus 0.764 (0.686-0.833) [p = 0.088, DeLong test], 0.824 (0.718-0.909) versus 0.692 (0.554-0.809) [p = 0.012], and 0.902 (0.794-0.984) versus 0.696 (0.526-0.857) [p = 0.024] in all three cohorts. CONCLUSIONS: Radiomic features from LNs could provide additional value for predicting pCR in ESCC patients, and the radiomics nomogram provided an accurate prediction of pCR, which might aid treatment decision.

Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes.

OBJECTIVES: Using contrast-enhanced computed tomography (CECT) and deep learning technology to develop a deep learning radiomics nomogram (DLRN) to preoperative predict risk status of patients with thymic epithelial tumors (TETs). METHODS: Between October 2008 and May 2020, 257 consecutive patients with surgically and pathologically confirmed TETs were enrolled from three medical centers. We extracted deep learning features from all lesions using a transformer-based convolutional neural network and created a deep learning signature (DLS) using selector operator regression and least absolute shrinkage. The predictive capability of a DLRN incorporating clinical characteristics, subjective CT findings and DLS was evaluated by the area under the curve (AUC) of a receiver operating characteristic curve. RESULTS: To construct a DLS, 25 deep learning features with non-zero coefficients were selected from 116 low-risk TETs (subtypes A, AB, and B1) and 141 high-risk TETs (subtypes B2, B3, and C). The combination of subjective CT features such as infiltration and DLS demonstrated the best performance in differentiating TETs risk status. The AUCs in the training, internal validation, external validation 1 and 2 cohorts were 0.959 (95% confidence interval [CI]: 0.924-0.993), 0.868 (95% CI: 0.765-0.970), 0.846 (95% CI: 0.750-0.942), and 0.846 (95% CI: 0.735-0.957), respectively. The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful model. CONCLUSIONS: The DLRN comprised of CECT-derived DLS and subjective CT findings showed a high performance in predicting risk status of patients with TETs. CLINICAL RELEVANCE STATEMENT: Accurate risk status assessment of thymic epithelial tumors (TETs) may aid in determining whether preoperative neoadjuvant treatment is necessary. A deep learning radiomics nomogram incorporating enhancement CT-based deep learning features, clinical characteristics, and subjective CT findings has the potential to predict the histologic subtypes of TETs, which can facilitate decision-making and personalized therapy in clinical practice. KEY POINTS: • A non-invasive diagnostic method that can predict the pathological risk status may be useful for pretreatment stratification and prognostic evaluation in TET patients. • DLRN demonstrated superior performance in differentiating the risk status of TETs when compared to the deep learning signature, radiomics signature, or clinical model. • The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful in differentiating the risk status of TETs.

Improving the accuracy of prognosis for clinical stage I solid lung adenocarcinoma by radiomics models covering tumor per se and peritumoral changes on CT.

OBJECTIVES: To assess methods to improve the accuracy of prognosis for clinical stage I solid lung adenocarcinoma using radiomics based on different volumes of interests (VOIs). METHODS: This retrospective study included patients with postoperative clinical stage I solid lung adenocarcinoma from two hospitals, center 1 and center 2. Three databases were generated: dataset A (training set from center 1), dataset B (internal test set from center 1), and dataset C (external validation test from center 2). Disease-free survival (DFS) data were collected. CT radiomics models were constructed based on four VOIs: gross tumor volume (GTV), 3 mm external to the tumor border (peritumoral volume [PTV]0~+3), 6 mm crossing tumor border (PTV-3~+3), and 6 mm external to the tumor border (PTV0~+6). The area under the receiver operating characteristic curve (AUC) was used to compare the model accuracies. RESULTS: A total of 334 patients were included (204 and 130 from centers 1 and 2). The model using PTV-3~+3 (AUC 0.81 [95% confidence interval {CI}: 0.75, 0.94], 0.81 [0.63, 0.90] for datasets B and C) outperformed the other three models, GTV (0.73 [0.58, 0.81], 0.73 [0.58, 0.83]), PTV0~+3 (0.76 [0.52, 0.87], 0.75 [0.60, 0.83]), and PTV0~+6 (0.72 [0.60, 0.81], 0.69 [0.59, 0.81]), in datasets B and C, all p < 0.05. CONCLUSIONS: A radiomics model based on a VOI of 6 mm crossing tumor border more accurately predicts prognosis of clinical stage I solid lung adenocarcinoma than that based on VOIs including overall tumor or external rims of 3 mm and 6 mm. KEY POINTS: • Radiomics is a useful approach to improve the accuracy of prognosis for stage I solid adenocarcinoma. • The radiomics model based on VOIs that includes 3 mm within and external to the tumor border (peritumoral volume [PTV]-3~+3) outperformed models that included either only the tumor itself or those that only included the peritumoral volume.

Chamomile: A Review of Its Traditional Uses, Chemical Constituents, Pharmacological Activities and Quality Control Studies.

Matricaria chamomilla L. (MC) and Chamaemelum nobile (L.) All. (CN) are two varieties of Chamomile. These herbs have been used for thousands of years in Greece, Rome and ancient Egypt. Chamomile has been used for the treatment of stomach problems, cramps, dermatitis, and minor infections. The purpose of this study was to introduce the botanical characteristics and geographical distribution, traditional uses, chemical constituents, pharmacological activities, toxicity studies and quality control studies, and lay a theoretical foundation for the rational development and utilization of chamomile. This review powered that chemical constituents include flavonoids, coumarins, volatile oils, terpenes, organic acids, polysaccharides, and others. These compounds possess anticancer, anti-infective, anti-inflammatory, antithrombotic, antioxidant, hypolipidaemic, hypoglycaemic, antihypertensive, antidepressant, neuroprotective activities, among others. Chamomile is a widely used herb in traditional medicine. It brings great economic value due to its numerous pharmacological effects and traditional uses. However, more toxicity tests should be carried out to confirm its safety. There is need for further research to provide concrete scientific evidence and validate its medicinal properties.

Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection.

In this retrospective study, chest CTs of 121 symptomatic patients infected with coronavirus disease-19 (COVID-19) from four centers in China from January 18, 2020 to February 2, 2020 were reviewed for common CT findings in relationship to the time between symptom onset and the initial CT scan (i.e. early, 0-2 days (36 patients), intermediate 3-5 days (33 patients), late 6-12 days (25 patients)). The hallmarks of COVID-19 infection on imaging were bilateral and peripheral ground-glass and consolidative pulmonary opacities. Notably, 20/36 (56%) of early patients had a normal CT. With a longer time after the onset of symptoms, CT findings were more frequent, including consolidation, bilateral and peripheral disease, greater total lung involvement, linear opacities, "crazy-paving" pattern and the "reverse halo" sign. Bilateral lung involvement was observed in 10/36 early patients (28%), 25/33 intermediate patients (76%), and 22/25 late patients (88%).

CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV).

In this retrospective case series, chest CT scans of 21 symptomatic patients from China infected with the 2019 novel coronavirus (2019-nCoV) were reviewed, with emphasis on identifying and characterizing the most common findings. Typical CT findings included bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities, sometimes with a rounded morphology and a peripheral lung distribution. Notably, lung cavitation, discrete pulmonary nodules, pleural effusions, and lymphadenopathy were absent. Follow-up imaging in a subset of patients during the study time window often demonstrated mild or moderate progression of disease, as manifested by increasing extent and density of lung opacities.

CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19).

OBJECTIVES: To explore the relationship between the imaging manifestations and clinical classification of COVID-19. METHODS: We conducted a retrospective single-center study on patients with COVID-19 from Jan. 18, 2020 to Feb. 7, 2020 in Zhuhai, China. Patients were divided into 3 types based on Chinese guideline: mild (patients with minimal symptoms and negative CT findings), common, and severe-critical (patients with positive CT findings and different extent of clinical manifestations). CT visual quantitative evaluation was based on summing up the acute lung inflammatory lesions involving each lobe, which was scored as 0 (0%), 1 (1-25%), 2 (26-50%), 3 (51-75%), or 4 (76-100%), respectively. The total severity score (TSS) was reached by summing the five lobe scores. The consistency of two observers was evaluated. The TSS was compared with the clinical classification. ROC was used to test the diagnosis ability of TSS for severe-critical type. RESULTS: This study included 78 patients, 38 males and 40 females. There were 24 mild (30.8%), 46 common (59.0%), and 8 severe-critical (10.2%) cases, respectively. The median TSS of severe-critical-type group was significantly higher than common type (p < 0.001). The ICC value of the two observers was 0.976 (95% CI 0.962-0.985). ROC analysis showed the area under the curve (AUC) of TSS for diagnosing severe-critical type was 0.918. The TSS cutoff of 7.5 had 82.6% sensitivity and 100% specificity. CONCLUSIONS: The proportion of clinical mild-type patients with COVID-19 was relatively high; CT was not suitable for independent screening tool. The CT visual quantitative analysis has high consistency and can reflect the clinical classification of COVID-19. KEY POINTS: • CT visual quantitative evaluation has high consistency (ICC value of 0.976) among the observers. The median TSS of severe-critical type group was significantly higher than common type (p < 0.001). • ROC analysis showed the area under the curve (AUC) of TSS for diagnosing severe-critical type was 0.918 (95% CI 0.843-0.994). The TSS cutoff of 7.5 had 82.6% sensitivity and 100% specificity. • The proportion of confirmed COVID-19 patients with normal chest CT was relatively high (30.8%); CT was not a suitable screening modality.

Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas.

OBJECTIVES: To evaluate the differential diagnostic performance of a computed tomography (CT)-based deep learning nomogram (DLN) in identifying tuberculous granuloma (TBG) and lung adenocarcinoma (LAC) presenting as solitary solid pulmonary nodules (SSPNs). METHODS: Routine CT images of 550 patients with SSPNs were retrospectively obtained from two centers. A convolutional neural network was used to extract deep learning features from all lesions. The training set consisted of data for 218 patients. The least absolute shrinkage and selection operator logistic regression was used to create a deep learning signature (DLS). Clinical factors and CT-based subjective findings were combined in a clinical model. An individualized DLN incorporating DLS, clinical factors, and CT-based subjective findings was constructed to validate the diagnostic ability. The performance of the DLN was assessed by discrimination and calibration using internal (n = 140) and external validation cohorts (n = 192). RESULTS: DLS, gender, age, and lobulated shape were found to be independent predictors and were used to build the DLN. The combination showed better diagnostic accuracy than any single model evaluated using the net reclassification improvement method (p < 0.05). The areas under the curve in the training, internal validation, and external validation cohorts were 0.889 (95% confidence interval [CI], 0.839-0.927), 0.879 (95% CI, 0.813-0.928), and 0.809 (95% CI, 0.746-0.862), respectively. Decision curve analysis and stratification analysis showed that the DLN has potential generalization ability. CONCLUSIONS: The CT-based DLN can preoperatively distinguish between LAC and TBG in patients presenting with SSPNs. KEY POINTS: • The deep learning nomogram was developed to preoperatively differentiate TBG from LAC in patients with SSPNs. • The performance of the deep learning feature was superior to that of the radiomics feature. • The deep learning nomogram achieved superior performance compared to the deep learning signature, the radiomics signature, or the clinical model alone.

Chest computed tomography in children with COVID-19 respiratory infection.

BACKGROUND: Infection with COVID-19 is currently rare in children. OBJECTIVE: To describe chest CT findings in children with COVID-19. MATERIALS AND METHODS: We studied children at a large tertiary-care hospital in China, during the period from 28 January 2019 to 8 February 2020, who had positive reverse transcriptase polymerase chain reaction (RT-PCR) for COVID-19. We recorded findings at any chest CT performed in the included children, along with core clinical observations. RESULTS: We included five children from 10 months to 6 years of age (mean 3.4 years). All had had at least one CT scan after admission. Three of these five had CT abnormality on the first CT scan (at 2 days, 4 days and 9 days, respectively, after onset of symptoms) in the form of patchy ground-glass opacities; all normalised during treatment. CONCLUSION: Compared to reports in adults, we found similar but more modest lung abnormalities at CT in our small paediatric cohort.

A Review of Quality of Life Measures used in Surgical Outcomes for Stage I Lung Cancers.

This review summarizes the literature on QoL in early stage lung cancer patients who underwent surgery. PubMed and PsycINFO were searched. Twelve articles from 10 distinct studies were identified for a total of 992 patients. Five QoL measures were used. One study reported only on pre-surgical QoL, six only on post-surgical QoL and three studies reported on both pre- and post-surgical QoL. Timing for the administration of post-surgical QoL surveys varied. The literature on QoL in Stage I non-small-cell lung cancer patients is very sparse. Additional research is needed to explore the impact of different surgical approaches on QoL.

Controversies on lung cancers manifesting as part-solid nodules.

PURPOSE: Summarise survival of patients with resected lung cancers manifesting as part-solid nodules (PSNs). METHODS: PubMed/MEDLINE and EMBASE databases were searched for all studies/clinical trials on CT-detected lung cancer in English before 21 December 2015 to identify surgically resected lung cancers manifesting as PSNs. Outcome measures were lung cancer-specific survival (LCS), overall survival (OS), or disease-free survival (DFS). All PSNs were classified by the percentage of solid component to the entire nodule diameter into category PSNs <80% or category PSNs ≥80%. RESULTS: Twenty studies reported on PSNs <80%: 7 reported DFS and 2 OS of 100%, 6 DFS 96.3-98.7%, and 11 OS 94.7-98.9% (median DFS 100% and OS 97.5%). Twenty-seven studies reported on PSNs ≥80%: 1 DFS and 2 OS of 100%, 19 DFS 48.0%-98.0% (median 82.6%), and 16 reported OS 43.0%-98.0% (median DFS 82.6%, OS 85.5%). Both DFS and OS were always higher for PSNs <80%. CONCLUSION: A clear definition of the upper limit of solid component of a PSN is needed to avoid misclassification because cell-types and outcomes are different for PSN and solid nodules. The workup should be based on the size of the solid component. KEY POINTS: • Lung cancers manifesting as PSNs are slow growing with high cure rates. • Upper limits of the solid component are important for correct interpretation. • Consensus definition is important for the management of PSNs. • Median disease-free-survival (DFS) increased with decreasing size of the nodule.

Lung Cancers Manifesting as Part-Solid Nodules in the National Lung Screening Trial.

OBJECTIVE: The objective of our study was to determine how often death occurred from lung cancers that manifested as part-solid nodules in the National Lung Screening Trial (NLST). MATERIALS AND METHODS: NLST radiologists classified nodules as solid, ground-glass, or mixed. All lung cancers classified as mixed nodules by NLST radiologists were reviewed by four experienced radiologists and reclassified as solid, nonsolid, or part-solid nodules. When possible, volume doubling times (VDTs) were calculated separately for the entire nodule and for the solid component of the nodule. RESULTS: Of 88 screening-diagnosed lung cancer cases identified by the NLST radiologists as mixed nodules, study radiologists confirmed that 19 were part-solid nodules. All the part-solid nodules were present at baseline (time 0), and none of the patients with a part-solid nodule had lymph node enlargement at CT before diagnosis or metastases at resection. Multilobar stage IV (T4N0M1) bronchioloalveolar carcinoma was diagnosed in one patient 25.0 months after study randomization, and the patient died 67.9 months after randomization. All 18 patients with a solitary or dominant part-solid nodule underwent surgery, and none died of lung cancer. From randomization, the average time to diagnosis was 18.6 months and the average time of follow-up was 79.2 months. On the last CT examination performed before diagnosis, the average size of the solid component of the part-solid nodules was 9.2 mm (SD, 4.9); the solid component was larger than 10 mm in five patients. The median VDT based on the entire nodule was 476 days, and the median VDT based on the solid component alone was 240 days. CONCLUSION: None of the patients with lung cancer manifesting as a solitary or dominant part-solid nodule had lymph node enlargement or metastases at pathology, and none died of lung cancer within the follow-up time of the NLST.

Lung Cancer Deaths in the National Lung Screening Trial Attributed to Nonsolid Nodules.

Purpose To validate the recommendation of performing annual follow-up of nonsolid nodules (NSNs) identified by computed tomographic (CT) screening for lung cancer, all cases of lung cancer manifesting as NSN in the National Lung Screening Trial (NLST) were reviewed. Materials and Methods Institutional review board and informed consent were waived for this study. The NLST database was searched to identify all participants with at least one NSN on CT scan with lung cancer as the cause of death (COD) documented by the NLST endpoint verification process. Among the 26 722 participants, 2534 (9.4%) had one or more NSNs, and lung cancer as the COD occurred for 48 participants. On review, 21 of the 48 patients had no NSN in the cancerous lobe, which left 27 patients whose CT scans were reviewed by four radiologists: Group A (n = 12) were cases of lung cancer as the COD because of adenocarcinoma, and group B (n = 15) were cases of lung cancer as the COD because of other cell types. Frequency of lung cancer as the COD because of NSN and the time from randomization to diagnosis within these groups was determined. Results Six of the 12 patients in group A had no NSN in the cancerous lobe whereas the remaining six patients had a dominant solid or part-solid nodule in the lobe that rapidly grew in four patients, was multifocal in one patient, and had a growing NSN in one patient in whom diagnosis was delayed for over 3 years. Five of the 15 patients in group B had no NSN, and for the remaining 10 patients, lung cancer as the COD was not because of NSN. Conclusion It seems unlikely that patients with lung cancer as the COD occurred with solitary or dominant NSN as long as annual follow-up was performed. This lends further support that lung cancers that manifest as NSNs have an indolent course and can be managed with annual follow-up. © RSNA, 2016.

Maximizing arsenic trioxide's anticancer potential: Targeted nanocarriers for solid tumor therapy.

Arsenic trioxide (ATO) has gained significant attention due to its promising therapeutic effects in treating different diseases, particularly acute promyelocytic leukemia (APL). Its potent anticancer mechanisms have been extensively studied. Despite the great efficacy ATO shows in fighting cancers, drawbacks in the clinical use are obvious, especially for solid tumors, which include rapid renal clearance and short half-life, severe adverse effects, and high toxicity to normal cells. Recently, the emergence of nanomedicine offers a potential solution to these limitations. The enhanced biocompatibility, excellent targeting capability, and desirable effectiveness have attracted much interest. Therefore, we summarized various nanocarriers for targeted delivery of ATO to solid tumors. We also provided detailed anticancer mechanisms of ATO in treating cancers, its clinical trials and shortcomings as well as the combination therapy of ATO and other chemotherapeutic agents for reduced drug resistance and synergistic effects. Finally, the future study direction and prospects were also presented.

Clerodendranthus Spicatus: A review of its active compounds, mechanisms of action, and clinical studies in urinary diseases.

Clerodendranthus spicatus (Thunb.) C.Y.Wu (CS) is a widely studied plant that shows potential in treating urinary diseases. Previous studies have focused on its chemical composition, pharmacological effects, and clinical applications. This review aims to provide a comprehensive summary and evaluation of the existing literature on CS. It also suggests future research directions to increase our understanding of its medicinal value. 129 pieces of literature were selected from several databases, including PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), Wan-fang Database, and Google Scholar, and were analyzed. Forty-five active compounds of CS have pharmacological effects such as lowering uric acid, anti-inflammation, anti-oxidation, and kidney protection. The potential mechanisms of these effects may be related to inhibiting transforming growth factor ß1 (TGF-ß1) activation, reducing inflammatory factors such as IL-8, IL-1ß, TNF-α, PGE2, IFN-γ, and IL-6 levels, suppressing the activation of NF-κB, JAK/STAT pathway, enhancing the clearance of ROS, MDA DPPH·, and O2 ̇ -, and regulating the expression of apoptosis-related pathways and proteins. This paper also discusses the quality control of CS and its efficacy and safety in treating urinary diseases. The study concludes that CS has a high potential for treating urinary diseases. Future studies should focus on observing the metabolic changes of CS active compounds in vivo and investigating the effects of CS on key signaling pathways. Additionally, more standardized and reasonable clinical studies and safety evaluation experiments should be conducted to obtain more clinical data.

A CT-based deep learning model: visceral pleural invasion and survival prediction in clinical stage IA lung adenocarcinoma.

Pathologic visceral pleural invasion (VPI) in patients with early-stage lung cancer can result in the upstaging of T1 to T2, in addition to having implications for surgical resection and prognostic outcomes. This study was designed with the goal of establishing and validating a CT-based deep learning (DL) model capable of predicting VPI status and stratifying patients based on their prognostic outcomes. In total, 2077 patients from three centers with pathologically confirmed clinical stage IA lung adenocarcinoma were enrolled. DL signatures were extracted with a 3D residual neural network. DL model was able to effectively predict VPI status. VPI predicted by the DL models, as well as pathologic VPI, was associated with shorter disease-free survival. The established deep learning signature provides a tool capable of aiding the accurate prediction of VPI in patients with clinical stage IA lung adenocarcinoma, thus enabling prognostic stratification.