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Background Injectable poly-D,L-lactic acid (PDLLA), under the brand name of AestheFill (REGEN Biotech, Inc. Seoul, South Korea), is a biocompatible, biodegradable, and biostimulatory product utilized to correct soft tissue volume loss. Its efficacy and safety have not been fully studied in a large cohort. Objectives The aim of the study was to evaluate the efficacy and safety of a novel dermal filler injectable, poly-D,L-lactic acid. Methods This was an evaluator-blinded, multicenter, randomized controlled trial to compare the efficacy and safety of PDLLA vs hyaluronic acid in the correction of the nasolabial fold. Two hundred and sixty patients with moderate to severe nasolabial folds were enrolled and randomized to the treatment group (PDLLA) or control group (hyaluronic acid). Each patient received a PDLLA or hyaluronic acid injection for nasolabial fold augmentation and was followed for 52 weeks. The Wrinkle Severity Rating Scale (WSRS) and Global Aesthetic Improvement Scale (GAIS) were applied to evaluate topical nasolabial fold augmentation and overall improvement, respectively. Results At 24 weeks, 67.6% of patients in the PDLLA group had an at least 1-grade improvement on the WSRS, compared to 60.9% of patients in the control group with an at least 1-grade improvement on the WSRS (P <.05). At each visit, the PDLLA group showed more improvement on the baseline WSRS than the control group. PDLLA was safe and well tolerated, with no severe adverse events. Conclusions PDLLA shows noninferior efficacy in correcting nasolabial folds compared to hyaluronic acid. Level of Evidence: 2 (Therapeutic) [ABSTRACT FROM AUTHOR]

Background: The feasibility of diagnosing malnutrition using facial features has been validated. A tool to integrate all facial features associated with malnutrition for disease screening is still demanded. This work aims to develop and evaluate a deep learning (DL) framework to accurately determine malnutrition based on a 3D facial points cloud. Methods: A group of 482 patients were studied in this perspective work. The 3D facial data were obtained using a 3D camera and represented as a 3D facial points cloud. A DL model, PointNet++, was trained and evaluated using the points cloud as inputs and classified the malnutrition states. The performance was evaluated with the area under the receiver operating characteristic curve, accuracy, specificity, sensitivity, and F1 score. Results: Among the 482 patients, 150 patients (31.1%) were diagnosed as having moderate malnutrition and 54 patients (11.2%) as having severe malnutrition. The DL model achieved the performance with an area under the receiver operating characteristic curve of 0.7240 ± 0.0416. Conclusion: The DL model achieved encouraging performance in accurately classifying nutrition states based on a points cloud of 3D facial information of patients with malnutrition. [ABSTRACT FROM AUTHOR]

Background Botulinum toxin is administered to paralyze the gastrocnemius muscle and reduce its size, thereby improving the calf contour and reducing the leg circumference of the calf. Objectives In pursuit of better efficacy, we designed a new injection protocol that targeted both the gastrocnemius and soleus. An algorithm of botulinum toxin injection for calf contouring was proposed based on the results of the study and ultrasonographic data. Methods A prospective, self-controlled, double-blind study was conducted. The gastronemius muscle (GM) group (n = 17) included the patients whose gastrocnemius muscles were treated, and the GM + soleus muscle (SM) group (n = 17) included the patients who had both the gastrocnemius and soleus treated. Parameters including the maximum leg circumference and the subcutaneous fat, gastrocnemius muscle, and soleus muscle thicknesses were collected before and after injection. Results Both GM and GM + SM injection helped improve calf contour. Although the thickness of the gastrocnemius muscle was reduced, reducing the leg circumference, the subcutaneous fat and soleus muscle had compensatory thickening after injection, especially over the long term, which may affect the efficacy. Compared with the GM group, the GM + SM group effectively reduced the tendency of the soleus muscle to thicken. Calf muscle thickness could be roughly estimated by constructing a fitting equation and measuring height, weight, and leg circumference. Conclusions Two injection methods achieved the effect of improving calf contour and reducing the leg circumference with equivalent patient satisfaction. The GM + SM injection group did not show any obvious extra clinical benefit when compared with the GM injection group. Level of Evidence: 2 [ABSTRACT FROM AUTHOR]

Background Pretarsal rolls are a crucial aesthetic feature. Despite numerous studies conducted on periorbital aesthetics, there remains a dearth of research on the ideal pretarsal rolls. Objectives This study aimed to investigate 4 aesthetic characteristics of ideal pretarsal rolls: presence, width, proportion, and morphology. Methods Respondents (385, including 80 aesthetic specialists) were presented with 7 series of images of pretarsal rolls, including 2 series of pretarsal roll existence, 2 series of varying widths, 2 series of different ratios between pretarsal roll and palpebral fissure height, and 1 series about morphology. Participants were asked to rank each image within a given series from most attractive to least attractive. The rankings were then analyzed according to population demographics. Results The majority of respondents deemed images with pretarsal rolls to be more aesthetically pleasing (P <.001) than those without such features. Additionally, pretarsal rolls with a width of 5 mm (P <.001), a ratio to palpebral fissure height of 0.5:1 (P <.001), and a crescent shape (P <.001) were perceived as the most attractive. Moreover, individuals aged 40 or younger exhibited a significant preference for images with pretarsal rolls compared to older groups (P <.001), and females displayed a greater inclination toward a medium ratio of pretarsal roll to palpebral fissure height than males did (0.5:1 P =.003, 0.618:1 P <.001). Conclusions This study validates the optimal characteristics of pretarsal rolls, which provides insight into pretarsal roll aesthetics and holds significant implications for surgical planning and effect assessment in future periocular procedures involving pretarsal rolls. Level of Evidence: 3 [ABSTRACT FROM AUTHOR]

Background: Large calves are considered unattractive. Botulinum toxin type A (BoNT‐A), acting as a neurotoxin that reduces muscle contraction, is widely used in calf contouring. However, there lacks detailed anatomical data to guide clinical injection. Objectives: This study aims to use ultrasonography to provide depth information of the calf muscles and determine the morphological types of the gastrocnemius muscle. Methods: Ultrasound scanning was performed at six sites on 40 Chinese adults. A real‐time ultrasound scannerwas used to detect the depth of the gastrocnemius muscle and the soleus muscle. Results: The thickness of the gastrocnemius muscle and the soleus muscle was measured. The depth from the skin to the surface, the middle, and the bottom of the gastrocnemius muscle was measured. The morphology of gastrocnemius muscle was classified into four different types under ultrasound according to the muscle bulge pattern. Conclusion: This study took ultrasonic measurements of Asian calves to guide accurate and effective BoNT‐A injection in real clinical practice. [ABSTRACT FROM AUTHOR]

Background: The effect of radiotherapy (RT) for second primary malignancies (SPMs) among prostate cancer survivors is controversial. Methods: Applying logistic regression, competing risk analysis and propensity score matching method, this study analyzed clinical data from the Surveillance, Epidemiology, and End Results program to compare the risk for SPMs between patients receiving RT and non-RT. Results: In this study, prostate cancer patients treated with RT developed more SPMs in the anus, bladder, rectum, liver, lung and bronchus and lymphoma than non-RT groups. Conclusion: More intensive surveillance should be adopted for these cancers among prostate cancer survivors. [ABSTRACT FROM AUTHOR]

Purpose: Keloids are caused by uncontrolled excessive proliferation of fibrous tissue. Multiple treatment strategies including steroid injection, surgical excision, laser therapy and radiation therapy have been reported. Few studies have evaluated the performance of plasma skin regeneration (PSR) in the treatment of keloid. This study aimed to evaluate the effectiveness of PSR combined with radiation therapy for keloids on different body parts. Patients and Methods: A total of 71 patients with 98 keloids were enrolled in this study. Keloids < 4 mm thick underwent single-dose PSR, while keloids ≥ 4 mm thick were administered compound betamethasone injection beforehand. Radiation therapy was administered after 24 hours and again 7 days later after PSR. The outcome was evaluated using the patient and observer scar assessment scale at 12 months post-treatment. Results: Patient-reported average scores for all keloids significantly decreased from 35.05± 9.94 to 21.84± 7.04 (p < 0.05). Keloids on face and neck, chest, and back responded better than those on shoulders and limbs. The recurrence rate was observed to be 15.3% (15 out of 98). Adverse effects were mild. Conclusion: PSR combined with radiation therapy is an effective and safe strategy to treat keloids. Location could be a factor that affects curative effects. [ABSTRACT FROM AUTHOR]

Background: The lip is of important aesthetic value and highly subjected to aging. Collecting anthropometric baseline data and understanding age‐related changes of labial morphology can help with diagnosis of deformity, assessment of aging, and planning of cosmetic procedures. Many studies have focused on Caucasians, while there is a lack of anthropometric data on Chinese women. Methods: A total of 169 women were enrolled in this cross‐sectional study and divided into four consecutive age groups. Linear distances, angles, and surface area data were obtained via stereophotogrammetry. Intergroup comparisons between different age groups were performed to find age‐related differences. Results: Lip width significantly increased with age while philtrum width seemed to show no obvious change. Cutaneous upper and lower lip height increased, lengthening the lip in the vertical dimension. Decrease of upper vermilion height and changes in angles indicated that aging process shortened the upper vermilion and flattened the vermilion border. Surface area also showed age‐related changes. Intergroup comparison showed no statistical significance in most variables between 20s and 30s or 30s and 40s, while age‐related changes in some variables were significant between 40s and 50s. Conclusion: This study provided anthropometric data of labial morphology across a wide age range. Aging process affected a variety of labial anthropometric variables. Age‐related changes accelerated after 40 among Chinese women. [ABSTRACT FROM AUTHOR]

Background: Full lips are beauty standards. Botulinum toxin A (BTA) paralyzes the orbicularis oris muscle to achieve the eversion of the lip, and thus makes the lip look plump. Aims: This study presents three cases of BTA injection to the lip and evaluates the possible changes in the labial morphology and the lip surface area. Methods: Three patients received a total of 4U BTA injection at the vermilion border of the upper lip. Vectra® H1 3D imaging system was used to capture 3D photographs of the lips before injection and two weeks after injection. Eight linear distances and the upper lip surface area were measured. Anthropometric measurements before and after injection were compared. Results: Patients displayed a larger upper vermillion height (P =.038) and a smaller cutaneous upper lip height (P =.024). There was a trend for a larger upper lip surface area, but not statistically significant (P =.109). Symptoms of slight perioral muscular palsy and mouth incompetence lasted about one month in three patients. Conclusion: BTA helps to enlarge the upper lip and shorten the philtrum. The BTA injection can be an option for lip enhancement with caution. [ABSTRACT FROM AUTHOR]

Background: Central lymph node metastasis (CLNM) occurs frequently in patients with papillary thyroid cancer (PTC), but performing prophylactic central lymph node dissection is still controversial. There are no reliable models for predicting CLNM. This study aimed to develop predictive models for CLNM by machine learning (ML) algorithms. Methods: Patients with PTC who underwent initial thyroid resection at our hospital between January 2018 and December 2019 were enrolled. A total of 22 variables, including clinical characteristics and ultrasonography (US) features, were used for conventional univariate and multivariate analysis and to construct ML-based models. A 5-fold cross validation strategy was used for validation and a feature selection approach was applied to identify risk factors. Results: The areas under the receiver operating characteristic curve (AUC) of 7 models ranged from 0.680 to 0.731. All models performed significantly better than US (AUC=0.623) in predicting CLNM (P<0.05). In decision curve, most of the models also performed better than US. The gradient boosting decision tree model with 7 variables was identified as the best model because of its best performance in both ROC (AUC=0.731) and decision curves. Based on multivariate analysis and feature selection, young age, male sex, low serum thyroid peroxidase antibody and US features such as suspected lymph nodes, microcalcification and tumor size > 1.1 cm were the most contributing predictors for CLNM. Conclusions: It is feasible to develop predictive models of CLNM in PTC patients by incorporating clinical characteristics and US features. The ML algorithm may be a useful tool for the prediction of lymph node metastasis in thyroid cancer. [ABSTRACT FROM AUTHOR]

Background: Patients who have previously undergone surgical resection of initial primary lung cancer (IPLC) are at high risk of developing second primary lung cancer (SPLC). There are still no standard treatments for SPLC. This study aimed to identify the prognostic factors and compare survival between the different SPLC treatment groups. Methods: SPLC patients in the Surveillance, Epidemiology, and End Results (SEER) database between 2007 and 2016 were retrospectively reviewed. Prognostic factors for SPLC were identified, using the least absolute shrinkage and selection operator (LASSO) regression and univariate Cox analysis to select variables for multivariate Cox analysis. Kaplan‐Meier method plus log‐rank test and restricted mean survival time (RMST) were used to compare survival outcome. Results: A total of 665 SPLC patients were finally enrolled into the study. Multivariate Cox regression analysis revealed that male vs. female (HR = 1.82, 95% CI: 1.29–2.59, P = 0.001), tumor size of SPLC ≥1 cm vs. <1 cm (HR = 1.80, 96% CI: 1.07–3.02, P = 1.028), IPLC characteristics of squamous cell carcinoma vs. adenocarcinoma (HR = 1.89, 95% CI: 1.17–3.04, P = 0.009), clinical stage II vs. stage I (HR = 2.60, 95% CI: 1.08–6.27, P = 0.033), and T2 stage vs. T1 stage (HR = 1.68, 95% CI: 1.04–2.72, P = 0.034) indicated worse survival. SPLC patients demonstrated a five‐year survival rate of 68.6% and a five‐year RMST of 49.4 months. The choice of surgical procedure (wedge resection, segmentectomy and lobectomy) for both IPLC and SPLC had no significant effect on prognosis (P > 0.05). Patients that received radiotherapy for SPLC also demonstrated similar survival when compared with those that underwent surgery (P > 0.05). Conclusions: Radiotherapy and sublobar resection can be considered reasonable alternative treatments for SPLC, especially when patients are unable to tolerate lobectomy. [ABSTRACT FROM AUTHOR]

Background: Lymph node metastasis (LNM) is difficult to precisely predict before surgery in patients with early-T-stage non-small cell lung cancer (NSCLC). This study aimed to develop machine learning (ML)-based predictive models for LNM. Methods: Clinical characteristics and imaging features were retrospectively collected from 1,102 NSCLC ≤ 2 cm patients. A total of 23 variables were included to develop predictive models for LNM by multiple ML algorithms. The models were evaluated by the receiver operating characteristic (ROC) curve for predictive performance and decision curve analysis (DCA) for clinical values. A feature selection approach was used to identify optimal predictive factors. Results: The areas under the ROC curve (AUCs) of the 8 models ranged from 0.784 to 0.899. Some ML-based models performed better than models using conventional statistical methods in both ROC curves and decision curves. The random forest classifier (RFC) model with 9 variables introduced was identified as the best predictive model. The feature selection indicated the top five predictors were tumor size, imaging density, carcinoembryonic antigen (CEA), maximal standardized uptake value (SUVmax), and age. Conclusions: By incorporating clinical characteristics and radiographical features, it is feasible to develop ML-based models for the preoperative prediction of LNM in early-T-stage NSCLC, and the RFC model performed best. [ABSTRACT FROM AUTHOR]

Macrophage activation syndrome (MAS), is a severe and fatal complication of various pediatric inflammatory disorders. Kabuki syndrome (KS), mainly caused by lysine methyltransferase 2D ( KMT2D ; OMIM 602113) variants, is a rare congenital disorder with multi-organ deficiencies. To date, there have been no reported cases of MAS in patients with KS. This report describes a case of a 22-year-old male with Kabuki syndrome (KS) who developed MAS. This unique case not only deepens the understanding of the involvement of KMT2D in immune regulation and disease, but expands the phenotype of the adult patient to better understand the natural history, disease burden, and management of patients with KS complicated with autoimmune disorders., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zhang, Kang, Xia, Chong, Long and Shen.)

Background: Facial anthropometry based on 3-dimensional (3D) imaging technology, or 3D photogrammetry, has gained increasing popularity among surgeons. It outperforms direct measurement and 2-dimensional (2D) photogrammetry because of many advantages. However, a main limitation of 3D photogrammetry is the time-consuming process of manual landmark localization. To address this problem, this study developed a U-NET-based deep learning algorithm to enable automated and accurate anatomical landmark detection on 3D facial models., Methods: The main structure of the algorithm stacked 2 U-NETs. In each U-NET block, we used 3×3 convolution kernel and rectified linear unit (ReLU) as activation function. A total of 200 3D images of healthy cases, acromegaly patients, and localized scleroderma patients were captured by Vectra H1 handheld 3D camera and input for algorithm training. The algorithm was tested to detect 20 landmarks on 3D images. Percentage of correct key points (PCK) and normalized mean error (NME) were used to evaluate facial landmark detection accuracy., Results: Among healthy cases, the average NME was 1.4 mm. The PCK reached 90% when the threshold was set to the clinically acceptable limit of 2 mm. The average NME was 2.8 and 2.2 mm among acromegaly patients and localized scleroderma patients, respectively., Conclusions: This study developed a deep learning algorithm for automated facial landmark detection on 3D images. The algorithm was innovatively validated in 3 different groups of participants. It achieved accurate landmark detection and improved the efficiency of 3D image analysis., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-22-1108/coif). Y.A. and Q.H. report that they were full-time employees of Beijing Li-Med Medical Technology Co., Ltd. during the conduct of the study. The other authors have no conflicts of interest to declare., (2024 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Background: Three-dimensional (3D) imaging is a powerful tool for the analysis of soft tissue morphology. 3D photogrammetry outperforms conventional photogrammetric methods and gains popularity among plastic surgeons. However, commercial 3D imaging systems bundled with analytical software are costly. This study intends to introduce and validate an automatic, low-cost, and user-friendly 3D facial scanner., Methods: An automatic and low-cost 3D facial scanning system was developed. The system consisted of a 3D facial scanner running automatically on a sliding track and a 3D data processing tool. Fifteen human subjects underwent 3D facial imaging by the novel scanner. Eighteen anthropometric parameters were measured on the 3D virtual models and compared with caliper measurements (the gold standard). Further, the novel 3D scanner was compared to the commonly used commercial 3D facial scanner Vectra H1. Heat map analysis was used to evaluate the deviation between the 3D models obtained by the two imaging systems., Results: The 3D photogrammetric results were highly correlated with the direct measurement results (P<0.001). The mean absolute differences (MADs) were less than 2 mm. Bland-Altman analysis indicated that, for 17 of the 18 parameters, the largest differences within the 95% limits of agreement margin were within the 2.0 mm clinical acceptance. Heat map analysis showed the average distance between the 3D virtual models was 0.15 mm, with a root mean square of 0.71 mm., Conclusions: The novel 3D facial scanning system is proven to be highly reliable. It provides a good alternative to commercial 3D facial scanners., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-22-900/coif). The authors have no conflicts of interest to declare., (2023 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-22-1324/coif). The authors have no conflicts of interest to declare.

Background: Cervical cancer is a major health threat for women. Radiotherapy plays an important role in the treatment of cervical cancer. However, its overall benefit has been questioned due to the risk of second primary malignancies., Methods: The Surveillance, Epidemiology, and End Results (SEER) database was used to search for cervical cancer patients diagnosed between January 1975 and November 2011. Factors that could possibly affect the occurrence of second primary malignancies included the year of diagnosis, gender, ethnicity, histologic type, SEER cancer stage, histology, grade, and whether surgery, chemotherapy, or radiotherapy were used. Age-adjusted and propensity scoring matching (PSM)-adjusted competing-risk analysis was applied for analysis., Results: Of the 23,112 patients identified through SEER, 14,800 (64.0%) received radiotherapy. Second malignancies were diagnosed in 2,545 (11.0%) cases. PSM-adjusted competing analysis revealed that patients receiving radiotherapy had a significantly higher risk of developing a second cancer in the colon, rectum and anus [hazard ratio (HR): 1.43; 95% confidence interval (CI): 1.09-1.87; P=0.01], lung and bronchus (HR: 1.41; 95% CI: 1.13-1.76; P=0.002), corpus uteri (HR: 3.71; 95% CI: 1.71-8.06; P<0.001), ovary (HR: 2.79; 95% CI: 1.38-5.64; P=0.004), and urinary bladder (HR: 2.18; 95% CI: 1.35-3.54; P=0.002). However, radiotherapy significantly lowered the risk of second cancers in the female breast (HR: 0.67; 95% CI: 0.52-0.86; P=0.002). Age-adjusted competing-risk analysis showed generally consistent results., Conclusions: Radiotherapy increased the risk of second cancers among cervical cancer patients. Those who underwent radiotherapy had a significantly higher risk of developing a second cancer in the colon, rectum and anus, lung and bronchus, corpus uteri, ovary, and urinary bladder., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://dx.doi.org/10.21037/atm-21-1393). The authors have no conflicts of interest to declare., (2021 Annals of Translational Medicine. All rights reserved.)

Background: Three-dimensional (3D) photography plays an important role in surgical planning and postoperative evaluation. Commercial 3D facial scanners are expensive, and they require patients to come to the clinics for 3D photography. To solve this problem, we developed an iPad/iPhone application to enable patients to capture 3D images of themselves on their own. This study aimed to evaluate the validity and reproducibility of this novel imaging system., Methods: 3D images were taken on 20 volunteers using the novel imaging system. Twenty-one anthropometric parameters were measured using calipers (direct measurement) and 3D photographs (3D photogrammetry). The results were compared to assess the accuracy and bias of 3D photogrammetry. The reproducibility was evaluated by testing intra- and interobserver reliabilities. Furthermore, 3D virtual models obtained by the novel imaging system and Vectra H1 camera were compared by performing heat map analysis., Results: The 3D photogrammetric results showed excellent correlations with direct measurements. Most anthropometric parameters did not show statistically significant differences between the two methods. The 95% limits of agreement exceeded 2 mm in some parameters, especially those with large numbers, although their relative error measurements were very small. Intra- and interobserver reliabilities were high enough to ensure good reproducibility. The comparison of 3D models obtained by the novel imaging system and Vectra H1 camera showed that the mean distance and the mean RMS were 0.08 and 0.67 mm, respectively., Conclusions: The novel 3D facial scanning system is validated to enable patients to take 3D images on their own. The imaging quality of the subnasale region needs further improvement. Future clinical applications include surgical planning, postoperative evaluation, and early diagnosis of diseases that affect facial appearance., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://dx.doi.org/10.21037/atm-21-1620). YC and XL collaborated with engineers from Beijing MeiXuan 3D Technology Co., Ltd. in the development of the MeiXuan application. The other authors have no conflicts of interest to declare., (2021 Annals of Translational Medicine. All rights reserved.)

Background: Lymph node metastasis (LNM) status can be a critical decisive factor for clinical management of lung cancer. Accurately evaluating the risk of LNM during or after the surgery can be helpful for making clinical decisions. This study aims to incorporate clinicopathological characteristics to develop reliable machine learning (ML)-based models for predicting LNM in patients with early-stage lung adenocarcinoma., Methods: A total of 709 lung adenocarcinoma patients with tumor size ≤2 cm were enrolled for analysis and modeling by multiple ML algorithms. The receiver operating characteristic (ROC) curve and decision curve were used for evaluating model's predictive performance and clinical usefulness. Feature selection based on potential models was performed to identify most-contributed predictive factors., Results: LNM occurred in 11.3% (80/709) of patients with lung adenocarcinoma. Most models reached high areas under the ROC curve (AUCs) >0.9. In the decision curve, all models performed better than the treat-all and treat-none lines. The random forest classifier (RFC) model, with a minimal number of five variables introduced (including carcinoembryonic antigen, solid component, micropapillary component, lymphovascular invasion and pleural invasion), was identified as the optimal model for predicting LNM, because of its excellent performance in both ROC and decision curves., Conclusions: The cost-efficient application of RFC model could precisely predict LNM during or after the operation of early-stage adenocarcinomas (sensitivity: 87.5%; specificity: 82.2%). Incorporating clinicopathological characteristics, it is feasible to predict LNM intraoperatively or postoperatively by ML algorithms., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://dx.doi.org/10.21037/jtd-21-98). The authors have no conflicts of interest to declare., (2021 Journal of Thoracic Disease. All rights reserved.)

Background: Keloids remain troublesome for clinicians because of the lack of standard therapy, and the underlying mechanisms are still unclear. Bibliometric analysis could be a powerful tool to comprehensively review research trends, evaluate publication performances and provide future perspectives. There is no bibliometric analysis focusing on keloid research., Methods: Keloid related publications were searched in Web of Science Core Collection from the publication year 1990 to 2019. Bibliometric data were provided on document type, annual publication number, most productive journals, publication geography, top potential authors, and highly cited articles. The distribution of single words in article titles was analyzed to evaluate the main research focuses and determine their development trends. Word cluster analysis was further performed to detect emerging trends in keloid research., Results: The number of annual articles increased from 24 in 1990 to 63 in 2006 and then increased sharply, reaching 139 in 2019. Dermatologic Surgery published the highest number of articles followed by Plastic and Reconstructive Surgery . The USA ranked top with six publication indicators and China had a dramatic increase in the annual number of articles since 2012. Recent research hotspots include underlying pathogenetic mechanisms, keloid treatment, and therapeutic effect evaluation., Discussion: Keloids remain a research focus. Efforts will be continuously made to understand the underlying mechanism of keloid formation. Despite many treatment modalities, there is no gold standard for keloid treatment, and many efforts are being made in the exploration of new therapies. Moreover, it is foreseeable that objective measurement tools will have a higher status in the assessment of keloids and scars., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/atm-21-508). The authors have no conflicts of interest to declare., (2021 Annals of Translational Medicine. All rights reserved.)

Background and Objectives: N3-positive non-small cell lung cancer (NSCLC) is usually regarded as inoperable. There were very few studies that focused on N3-NSCLC patients. This study aims to analyze prognosis of NSCLC patients with N3 disease and provides retrospective indications. Methods: NSCLC patients staged as N3 were retrospectively reviewed from the Surveillance, Epidemiology, and End Results database. Univariate and multivariate Cox regression were used for identifying prognostic factors. The selected predictive parameters by the least absolute shrinkage and selection operator (LASSO) regression were used to develop predictive nomogram models for overall survival (OS) and lung cancer-specific survival (CSS). The C-index values were calculated to assess the models' predictive ability, while calibration curves were plotted to evaluate the agreement between the predicted and the actual survival. Survival curves were plotted by Kaplan-Meier method and were compared by log-rank test. Propensity score matching (PSM) was used to balance the baseline characteristics between treatment groups. Results: A total of 24,747 N3-NSCLC patients were enrolled. The 1-, 3-, and 5-year OS rates were 35.8, 6.8, and 1.7%, respectively, while the corresponding CSS rates was 36.6, 6.9, and 1.8%, respectively. The nomogram models were developed using 11 significant prognostic parameters, including age, sex, race, histology, stage, T stage, bone, brain and liver metastases, surgery, and chemotherapy. Both of them demonstrated great predictive ability and performed well in the calibration curves. After PSM, patients receiving surgery demonstrated significantly better survival than those who did not. Besides, there was no significant difference of survival between patients receiving chemotherapy with and without radiotherapy. Conclusions: The nomogram models for predicting survival outcome of N3-NSCLC patients can be clinically used. Surgery may be beneficial to the survival for these patients, while radiotherapy may not have additional survival benefits in patients receiving chemotherapy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Han, Wu, Sun, Chong, Kang, Liu and Zhang.)

Background and Objectives: Total knee arthroplasty (TKA) is widely performed to improve mobility and quality of life for symptomatic knee osteoarthritis patients. The accurate prediction of patients' length of hospital stay (LOS) can help clinicians for rehabilitation decision-making and bed assignment planning, which thus makes full use of medical resources. Methods: Clinical characteristics were retrospectively collected from 1,298 patients who received TKA. A total of 36 variables were included to develop predictive models for LOS by multiple machine learning (ML) algorithms. The models were evaluated by the receiver operating characteristic (ROC) curve for predictive performance and decision curve analysis (DCA) for clinical values. A feature selection approach was used to identify optimal predictive factors. Results: The areas under the ROC curve (AUCs) of the nine models ranged from 0.710 to 0.766. All the ML-based models performed better than models using conventional statistical methods in both ROC curves and decision curves. The random forest classifier (RFC) model with 10 variables introduced was identified as the best predictive model. The feature selection indicated the top five predictors: tourniquet time, distal femoral osteotomy thickness, osteoporosis, tibia component size, and post-operative values of Hb within 24 h. Conclusions: By analyzing clinical characteristics, it is feasible to develop ML-based models for the preoperative prediction of LOS for patients who received TKA, and the RFC model performed the best., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Han, Liu, Wu, Chong, Chai and Weng.)

Background: Second primary lung cancer (SPLC) occurs not rarely in recent years. The effect of radiotherapy on SPLC remains unclear. This study aims to explore the survival outcome of SPLC patients with clinical stage T1 lung cancer previously treated with radiotherapy., Methods: A total of 705 SPLC patients that previously underwent radiotherapy for first primary lung cancer (FPLC) were identified from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2016. Univariate and multivariate Cox regression analyses were performed to find prognostic factors. The survival outcomes were plotted using Kaplan-Meier (KM) method and compared by log-rank test. Additionally, propensity score matching (PSM) analyses were used to compare overall survival (OS) and lung cancer-specific survival (CSS) between radiotherapy and other treatment groups for SPLC., Results: According to Cox analyses, age >62 years [hazard ratio (HR): 1.48, 95% confidence interval (CI): 1.10-1.99; P=0.010], SPLC tumor size >1 cm (HR: 1.95, 95% CI: 1.51-2.53; P<0.001), and treatments for SPLC as chemotherapy (HR: 1.39, 95% CI: 1.13-1.71; P=0.002), no surgery (HR: 2.00, 95% CI: 1.34-2.98; P=0.001) and no radiotherapy (HR: 1.73, 95% CI: 1.39-2.15; P<0.001) independently indicated worse survival. After PSM, patients treated with radiotherapy for SPLC had significantly better OS and CSS than the none-treatment (OS: P=0.004; CSS: P<0.001), chemotherapy (P<0.001) or radiotherapy plus chemotherapy (OS: P=0.032; CSS: P=0.008) groups, but demonstrated a worse OS than the surgery group (P=0.034)., Conclusions: Surgery may be more beneficial to survival than radiotherapy and chemotherapy and should be considered first if possible. When patients cannot tolerate surgery, radiotherapy can be an effective alternative., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/jtd-20-2024). The authors have no conflicts of interest to declare., (2020 Journal of Thoracic Disease. All rights reserved.)

Background: Lymph node metastasis (LNM) status is critical to the treatment. Fewer studies has focused on LNM in patients with small-size non-small cell lung cancer (NSCLC). This study aims to investigate clinicopathological characteristics associated with skip N2 (SN2) and non-skip N2 (NSN2) metastasis, and their metastatic patterns in NSCLC with tumor size of 1-2 cm. Methods: We reviewed the records of NSCLC patients with tumor size of 1-2 cm who underwent lobectomy with systematic lymph node dissection (LND) between January 2013 and June 2019. Clinical, radiographical, and pathological characteristics were compared among N1, SN2, and NSN2 groups. Metastatic patterns of mediastinal lymph node were analyzed based on final pathology. Results: A total of 63 NSCLC patients with tumor size of 1-2 cm were staged as pN2, including 25 (39.7%) SN2 and 38 (60.3%) NSN2. The incidence rates of SN2 and NSN2 were 2.8% (25/884) and 4.3% (38/884), respectively. For all clinicopathological characteristics, no significant difference was observed among the groups of N1, SN2, and NSN2. For the tumor located in each lobe, specific nodal drainage stations were identified: 2R/4R for right upper lobe; 2R/4R and subcarinal node (#7) for right middle lobe and right lower lobe; 4L and subaortic node (#5) for left upper lobe; #7 for left lower lobe. However, there were still a few patients (10.9%, 5/46) had the involvement of lower zone for tumors of upper lobe and the involvement of upper zone for lower lobe. Conclusions: SN2 occurs frequently in patients with small-size NSCLC. Whether lobe-specific selective LND is suitable for all small-size patients deserves more studies to confirm. Surgeons should be more careful when performing selective LND for tumors located in the lower and upper lobes., (Copyright © 2020 Wu, Han, Gong, Wang, Liu, Liu, Chen, Chong, Liang and Li.)

Background: Lymph node metastasis (LNM) status is of key importance for the decision-making on treatment and survival prediction. There is no reliable method to precisely evaluate the risk of LNM in NSCLC patients. This study aims to develop and validate a dynamic nomogram to evaluate the risk of LNM in small-size NSCLC. Methods: The NSCLC ≤ 2 cm patients who underwent initial pulmonary surgery were retrospectively reviewed and randomly divided into a training cohort and a validation cohort as a ratio of 7:3. The training cohort was used for the least absolute shrinkage and selection operator (LASSO) regression to select optimal variables. Based on variables selected, the logistic regression models were developed, and were compared by areas under the receiver operating characteristic curve (AUCs) and decision curve analysis (DCA). The optimal model was used to plot a dynamic nomogram for calculating the risk of LNM and was internally and externally well-validated by calibration curves. Results: LNM was observed in 12.0% (83/774) of the training cohort and 10.1% (33/328) of the validation cohort ( P = 0.743). The optimal model was used to plot a nomogram with six variables incorporated, including tumor size, carcinoembryonic antigen, imaging density, pathological type (adenocarcinoma or non-adenocarcinoma), lymphovascular invasion, and pleural invasion. The nomogram model showed excellent discrimination (AUC = 0.895 vs. 0.931) and great calibration in both the training and validation cohorts. At the threshold probability of 0-0.8, our nomogram adds more net benefits than the treat-none and treat-all lines in the decision curve. Conclusions: This study firstly developed a cost-efficient dynamic nomogram to precisely and expediently evaluate the risk of LNM in small-size NSCLC and would be helpful for clinicians in decision-making., (Copyright © 2020 Wu, Han, Wang, Gong, Liu, Chong, Liu, Liang and Li.)