Factors Affecting Sentinel Node Metastasis in Thin (T1) Cutaneous Melanomas: Development and External Validation of a Predictive Nomogram

TPM4 influences the initiation and progression of gastric cancer by modulating ferroptosis via SCD1

Gastric cancer (GC) is a deadly disease with poor prognosis and few treatment options. Tropomyosin 4 (TPM4) is an actin-binding protein that stabilizes the cytoskeleton of cells and has an unclear role in GC. This study aimed to elucidate the role and underlying mechanisms of TPM4 in GC pathogenesis. The expression and diagnostic and prognostic value of TPM4 in GC were analyzed using bioinformatics. A nomogram based on TPM4 expression was created and validated with an external cohort. TPM4-knockdown GC cells and xenograft models in nude mice were used to study the function of TPM4 in vitro and in vivo. Proteomic and rescue experiments confirmed the regulatory effect of TPM4 on stearoyl-CoA desaturase 1 (SCD1) in GC. Immunohistochemistry verified the expression and correlation of the TPM4 and SCD1 proteins in GC tissues. Our study identified TPM4 as an oncogene in GC, suggesting its potential diagnostic and prognostic value. The TPM4-based nomogram showed potential prognostic value for clinical use. TPM4 knockdown inhibited GC cell proliferation, induced ferroptosis, and slowed tumor growth in vivo, which is achieved by inhibiting SCD1 expression. Immunohistochemical analysis of GC tissues revealed elevated expression levels of both TPM4 and SCD1 proteins, with a positive correlation observed between their expression. TPM4 is a promising target for new diagnostic, prognostic, and therapeutic strategies for GC. Downregulation of TPM4 inhibits GC cell growth and induces ferroptosis by suppressing SCD1 expression.

Evaluation and Surgical Management of Pediatric Cutaneous Melanoma and Atypical Spitz and Non-Spitz Melanocytic Tumors (Melanocytomas): A Report From Children's Oncology Group

PURPOSE

The purpose of this study was to develop recommendations for the diagnostic evaluation and surgical management of cutaneous melanoma (CM) and atypical Spitz tumors (AST) and non-Spitz melanocytic tumors (melanocytomas) in pediatric (age 0-10 years) and adolescent (age 11-18 years) patients.

METHODS

A Children's Oncology Group-led panel with external, multidisciplinary CM specialists convened to develop recommendations on the basis of available data and expertise.

RESULTS

Thirty-three experts from multiple specialties (cutaneous/medical/surgical oncology, dermatology, and dermatopathology) established recommendations with supporting data from 87 peer-reviewed publications.

RECOMMENDATIONS

(1) Excisional biopsies with 1-3 mm margins should be performed when feasible for clinically suspicious melanocytic neoplasms. (2) Definitive surgical treatment for CM, including wide local excision and sentinel lymph node biopsy (SLNB), should follow National Comprehensive Cancer Network Guidelines in the absence of data from pediatric-specific surgery trials and/or cohort studies. (3) Accurate classification of ASTs as benign or malignant is more likely with immunohistochemistry and next-generation sequencing. (4) It may not be possible to classify some ASTs as likely/definitively benign or malignant after clinicopathologic and/or molecular correlation, and these Spitz tumors of uncertain malignant potential should be excised with 5 mm margins. (5) ASTs favored to be benign should be excised with 1- to 3-mm margins if transected on biopsy. (6) Re-excision is not necessary if the AST does not extend to the biopsy margin(s) when complete/excisional biopsy was performed. (7) SLNB should not be performed for Spitz tumors unless a diagnosis of CM is favored on clinicopathologic evaluation. (8) Non-Spitz melanocytomas have a presumed increased risk for progression to CM and should be excised with 1- to 3-mm margins if transected on biopsy. (9) Re-excision of non-Spitz melanocytomas is not necessary if the lesion is completely excised on biopsy.

Risk Prediction Models for Sentinel Node Positivity in Melanoma: A Systematic Review and Meta-Analysis

Importance

There is a need to identify the best performing risk prediction model for sentinel lymph node biopsy (SLNB) positivity in melanoma.

Objective

To comprehensively review the characteristics and discriminative performance of existing risk prediction models for SLNB positivity in melanoma.

Data Sources

Embase and MEDLINE were searched from inception to May 1, 2024, for English language articles.

Study Selection

All studies that either developed or validated a risk prediction model (defined as any calculator that combined more than 1 variable to provide a patient estimate for probability of melanoma SLNB positivity) with a corresponding measure of model discrimination were considered for inclusion by 2 reviewers, with disagreements adjudicated by a third reviewer.

Data Extraction and Synthesis

Data were extracted in duplicate according to Data Extraction for Systematic Reviews of Prediction Modeling Studies, Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines. Effects were pooled using random-effects meta-analysis.

Main Outcome and Measures

The primary outcome was the mean pooled C statistic. Heterogeneity was assessed using the I2 statistic.

Results

In total, 23 articles describing the development of 21 different risk prediction models for SLNB positivity, 20 external validations of 8 different risk prediction models, and 9 models that included sufficient information to obtain individualized patient risk estimates in routine preprocedural clinical practice were identified. Among all risk prediction models, the pooled weighted C statistic was 0.78 (95% CI, 0.74-0.81) with significant heterogeneity (I2 = 97.4%) that was not explained in meta-regression. The Memorial Sloan Kettering Cancer Center and Melanoma Institute of Australia models were most frequently externally validated with both having strong and comparable discriminative performance (pooled weighted C statistic, 0.73; 95% CI, 0.69-0.78 vs pooled weighted C statistic, 0.70; 95% CI, 0.66-0.74). Discrimination was not significantly different between models that included gene expression profiles (pooled C statistic, 0.83; 95% CI, 0.76-0.90) and those that only used clinicopathologic features (pooled C statistic, 0.77; 95% CI, 0.73-0.81) (P = .11).

Conclusions and Relevance

This systematic review and meta-analysis found several risk prediction models that have been externally validated with strong discriminative performance. Further research is needed to evaluate the associations of their implementation with preprocedural care.

Using Nomograms Wisely: Predicting Sentinel Node Positivity in Melanoma

BACKGROUND

Four externally validated sentinel node biopsy (SNB) prediction nomograms exist for malignant melanoma that each incorporate different clinical and histopathologic variables, which can result in substantially different risk estimations for the same patient. We demonstrate this variability by using hypothetical melanoma cases.

METHODS

We compared the MSKCC and MIA calculators. Using a random number generator, 300 hypothetical thin melanoma "patients" were created with varying age, tumor thickness, Clark level, location on the body, ulceration, melanoma subtype, mitosis, and lymphovascular invasion (LVI). The chi-square test was used to detect statistically significant differences in risk estimations between nomograms. Multivariate linear regression was used to determine the most relevant contributing pathologic features in cases where the predictions diverged by > 10%.

RESULTS

Of 300 randomly generated cases, 164 were deleted as their clinical scenarios were unlikely. The MSKCC nomogram generally calculated a lower risk than the MIA (p < 0.001). The highest risk score attained for any "patient" using MSKCC calculator was 15% achieved in one of 136 patients (0.7%), whereas using the MIA nomogram, 58 of 136 patients (43%, p < 0.001) had predicted risk >15%. Regression analysis on patients with >10% difference between nomograms revealed LVI (26, p < 0.001), mitosis (14, p < 0.001), and melanoma subtype (8, p < 0.001) were the factors with high coefficients within MIA that were not present in MSKCC.

CONCLUSIONS

Nomograms are useful tools when predicting SNB risk but provide risk outputs that are quite sensitive to included predictors.

Nomogram-based prognostic stratification for patients with large hepatocellular carcinoma: a population study of SEER database and a Chinese cohort

Background

Large hepatocellular carcinoma (HCC) with a diameter ≥5 cm remains a significant challenge of poor survival and raises the need for prognosis evaluation. This study aimed to develop and validate a nomogram-based prognostic stratification to assess overall survival (OS) of patients with large HCC.

Methods

Data of patients with large HCC were retrospectively collected from the Surveillance, Epidemiology, and End Results (SEER) database and our hospital, and were divided into the training cohort, internal validation cohort and external validation cohort. Cox analysis was performed to identify independent prognostic factors for the construction of nomogram in training cohort. The predictive ability of the nomogram was validated compared with the tumor node metastasis (TNM) classification staging system. Furthermore, prognostic stratification system based on nomogram was developed.

Results

Independent prognostic factors including histological grade, T stage, M stage, alpha fetoprotein (AFP), fibrosis score and surgery, were incorporated to construct nomogram. C-indexes of nomogram were 0.730, 0.726 and 0.724 in the training, internal and external validation cohorts, respectively. Importantly, nomogram harbored a superior discrimination and clinical benefit than the TNM staging system. Nomogram-based prognostic stratification divided patients into three groups: 345-414 (low-risk group), 415-460 (medium-risk group) and 461-513 (high-risk group). As shown in Kaplan-Meier curves, there were significant differences in OS among low-, medium- and high-risk groups (P<0.01).

Conclusions

Nomogram showed a superior prognostic predictive ability compared with the TNM staging system. The prognostic stratification serves as a valuable tool to assist clinicians on the selection of optimal treatment method and follow-up plan, particularly for the high-risk population.

Prognostic factors of patients with human epidermal growth factor receptor 2-positive breast cancer following neoadjuvant therapy: Development and validation of a predictive nomogram

OBJECTIVE

Human epidermal growth factor receptor 2 (HER2)-positive breast cancer exhibits an aggressive phenotype and poor prognosis. The application of neoadjuvant therapy (NAT) in patients with breast cancer can significantly reduce the risks of disease recurrence and improve survival. By integrating different clinicopathological factors, nomograms are valuable tools for prognosis prediction. This study aimed to assess the prognostic value of clinicopathological factors in patients with HER2-positive breast cancer and construct a nomogram for outcome prediction.

METHODS

We retrospectively analyzed the clinicopathological data from 374 patients with breast cancer admitted to the Fourth Hospital of Hebei Medical University between January 2009 and December 2017, who were diagnosed with invasive breast cancer through preoperative core needle biopsy pathology, underwent surgical resection after NAT, and were HER2-positive. Patients were randomly divided into a training and validation set at a ratio of 7:3. Univariate and multivariate survival analyses were performed using Kaplan-Meier and Cox proportional hazards regression models. Results of the multivariate analysis were used to create nomograms predicting 3-, 5-, and 8-year overall survival (OS) rates. Calibration curves were plotted to test concordance between the predicted and actual risks. Harrell C-index and time-dependent receiver operating characteristic (ROC) curves were used to evaluate the discriminability of the nomogram prediction model.

RESULTS

All included patients were women, with a mean age of 50 ± 10.4 years (range: 26-72 years). In the training set, both univariate and multivariate analyses identified residual cancer burden (RCB) class, tumor-infiltrating lymphocytes(TILs), and clinical stage as independent prognostic factors for OS, and these factors were combined to construct a nomogram. The calibration curves demonstrated good concordance between the predicted and actual risks, and the C-index of the nomogram was 0.882 (95 % CI 0.863-0.901). The 3-, 5-, and 8-year areas under the ROC curve (AUCs) were 0.909, 0.893, and 0.918, respectively, indicating good accuracy of the nomogram. The calibration curves also demonstrated good concordance in the validation set, with a C-index of 0.850 (95 % CI 0.804-0.896) and 3-, 5-, and 8-year AUCs of 0.909, 0.815, and 0.834, respectively, which also indicated good accuracy.

CONCLUSION

The nomogram prediction model accurately predicted the prognostic status of post-NAT patients with breast cancer and was more accurate than clinical stage and RCB class. Therefore, it can serve as a reliable guide for selecting clinical treatment measures for breast cancer.

Impact of an Online Risk Calculator for Sentinel Node Positivity on Management of Patients with T1 and T2 Melanomas

BACKGROUND

Predicting which patients with American Joint Committee on Cancer (AJCC) T1-T2 melanomas will have a positive sentinel lymph node (SLN) is challenging. Melanoma Institute Australia (MIA) developed an internationally validated SLN metastatic risk calculator. This study evaluated the nomogram's impact on T1-T2 melanoma patient management at MIA.

METHODS

SLN biopsy (SLNB) rates were compared for the pre- and post-nomogram periods of 1 July 2018-30 June 2019 and 1 August 2020-31 July 2021, respectively.

RESULTS

Overall, 850 patients were identified (pre-nomogram, 383; post-nomogram, 467). SLNB was performed in 29.0% of patients in the pre-nomogram group and 34.5% in the post-nomogram group (p = 0.091). The overall positivity rate was 16.2% in the pre-nomogram group and 14.9% in the post-nomogram group (p = 0.223). SLNB was performed less frequently in T1a melanoma patients in the pre-nomogram group (1.1%, n = 2/177) than in the post-nomogram group (8.6%, n = 17/198) [p ≤ 0.001]. This increase was particularly for melanomas with a risk score ≥ 5%, with an SLN positivity rate of 11.8% in the post-nomogram group (p = 0.004) compared with zero. For T1b melanomas with a risk score of > 10%, the SLNB rate was 40.0% (8/20) pre-nomogram and 75.0% (12/16) post-nomogram (p = 0.049).

CONCLUSIONS

In this specialized center, the SLN risk calculator appears to influence practice for melanomas previously considered low risk for metastasis, with increased use of SLNB for T1a and higher-risk T1b melanomas. Further evaluation is required across broader practice settings. Melanoma management guidelines could be updated to incorporate the availability of nomograms to better select patients for SLNB than previous criteria.

A nomogram for predicting liver metastasis in patients with gastric gastrointestinal stromal tumor

BACKGROUND

Liver metastasis (LIM) is an important factor in the diagnosis, treatment, follow-up, and prognosis of patients with gastric gastrointestinal stromal tumor (GIST). There is no simple tool to assess the risk of LIM in patients with gastric GIST. Our aim was to develop and validate a nomogram to identify patients with gastric GIST at high risk of LIM.

METHODS

Patient data diagnosed as having gastric GIST between 2010 and 2019 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training cohort and internal validation cohort in a 7:3 ratio. For external validation, retrospective data collection was performed on patients diagnosed as having gastric GIST at Yunnan Cancer Center (YNCC) between January 2015 and May 2023. Univariate and multivariate logistic regression analyses were used to identify independent risk factors associated with LIM in patients with gastric GIST. An individualized LIM nomogram specific for gastric GIST was formulated based on the multivariate logistic model; its discriminative performance, calibration, and clinical utility were evaluated.

RESULTS

In the SEER database, a cohort of 2341 patients with gastric GIST was analyzed, of which 173 cases (7.39%) were found to have LIM; 239 patients with gastric GIST from the YNCC database were included, of which 25 (10.46%) had LIM. Multivariate analysis showed tumor size, tumor site, and sex were independent risk factors for LIM (P < .05). The nomogram based on the basic clinical characteristics of tumor size, tumor site, sex, and age demonstrated significant discrimination, with an area under the curve of 0.753 (95% CI, 0.692-0.814) and 0.836 (95% CI, 0.743-0.930) in the internal and external validation cohort, respectively. The Hosmer-Lemeshow test showed that the nomogram was well calibrated, whereas the decision curve analysis and the clinical impact plot demonstrated its clinical utility.

CONCLUSION

Tumor size, tumor subsite, and sex were significantly correlated with the risk of LIM in gastric GIST. The nomogram for patients with GIST can effectively predict the individualized risk of LIM and contribute to the planning and decision making related to metastasis management in clinical practice.

Development and validation of a novel nomogram model to assess the risk of gastric contents in outpatients undergoing elective sedative gastrointestinal endoscopy procedures

BACKGROUND

Gastric contents may contribute to patients' aspiration during anesthesia. Ultrasound can accurately assess the risk of gastric contents in patients undergoing sedative gastrointestinal endoscopy (GIE) procedures, but its efficiency is limited. Therefore, developing an accurate and efficient model to predict gastric contents in outpatients undergoing elective sedative GIE procedures is greatly desirable.

METHODS

This study retrospectively analyzed 1501 patients undergoing sedative GIE procedures. Gastric contents were observed under direct gastroscopic vision and suctioned through the endoscope. High-risk gastric contents were defined as having solid content or liquid volume > 25 ml and pH < 2.5; otherwise, they were considered low-risk gastric contents. Univariate analysis and multivariate analysis were used to select the independent risk factors to predict high-risk gastric contents. Based on the selected independent risk factors, we assigned values to each independent risk factor and established a novel nomogram. The performance of the nomogram was verified in the testing cohort by the metrics of discrimination, calibration, and clinical usefulness. In addition, an online accessible web calculator was constructed.

RESULTS

We found BMI, cerebral infarction, cirrhosis, male, age, diabetes, and gastroesophageal reflux disease were risk factors for gastric contents. The AUROCs were 0.911 and 0.864 in the development and testing cohort, respectively. Moreover, the nomogram showed good calibration ability. Decision curve analysis and Clinical impact curve demonstrated that the predictive nomogram was clinically useful. The website of the nomogram was https://medication.shinyapps.io/dynnomapp/.

CONCLUSIONS

This study demonstrates that clinical variables can be combined with algorithmic techniques to predict gastric contents in outpatients. Nomogram was constructed from routine variables, and the web calculator had excellent clinical applicability to assess the risk of gastric contents accurately and efficiently in outpatients, assist anesthesiologists in assessment and identify the most appropriate patients for ultrasound.

The Burden of Thin Melanomas in Tuscany, Italy, 1985-2017: Age- and Sex-Specific Temporal Trends in Incidence and Mortality

A steady increase in the incidence and mortality burden correlated to thin melanomas (≤1 mm) has been reported in recent years in some international studies, but there is currently a paucity of data from the Mediterranean area. We aimed to describe the epidemiological characteristics of thin melanoma in Tuscany, Central Italy. A total of 6002 first cutaneous invasive melanomas occurring from 1985 to 2017 were selected for analysis; data were retrieved from the local population-based cancer registry. The standardized incidence rate was 15.0 per 100,000 in the population, higher among men than women (16.5 vs. 14.1). Incidence rates tended to increase over time across all age group-specific population strata, with annual percent changes moderately higher among men (+8.0%) than women (+6.9%), especially among the elderly. Among both sexes and in each age group, the trend toward increasing incidence rates was particularly strong for thin melanomas. Survival was better among women than men across all categories of thickness. Approximately 15% of deaths occurred among patients with thin lesions, with no major temporal changes in recent years. This study contributes to an improved understanding of melanoma epidemiology in Tuscany and underscores the need for primary prevention strategies tackling the growing burden of thin melanomas.

Service implications of the revised 2022 National Institute for Health and Care Excellence (NICE) follow-up guidelines for stage IA-IIC melanoma

BACKGROUND

The 2022 National Institute for Health and Care Excellence melanoma guideline update made significant changes to follow-up. The aim of this study was to assess the impact these changes will have on a national melanoma cohort over a 5-year follow-up interval.

METHODS

Anonymized, individual-level, population-scale, linkable primary and secondary care National Health Service data for an 18-year interval (2000-2018) in Wales, UK were analysed. These data were used to predict the number of patients over a 10-year interval (2020-2030) that would be diagnosed with melanoma. Follow-up schedules for the 2015 and 2022 National Institute for Health and Care Excellence melanoma guidelines were then used to calculate the number of clinician-led appointments, the number of radiological investigations, and the total healthcare cost between 2025 and 2030, corresponding to a 5-year patient follow-up interval, for those with stage IA-IIC melanoma.

RESULTS

Between 2025 and 2030 it is predicted that implementation of the 2022 guidelines would lead to 21 122 (range 19 194-23 083) fewer clinician-led appointments for patients with stage IA-IIC melanoma. However, there would be a significant increase in the number of radiological investigations (7812; range 7444-8189). These changes would lead to a €2.74 million (€1.87 million-€3.61 million) reduction in the total cost of follow-up over the interval 2025-2030.

CONCLUSION

Melanoma follow-up guideline changes will result in a substantial reduction in the number of clinical follow-up appointments, but a significant additional burden to radiological services. The overall cost of follow-up at a national level will be reduced.

Sentinel lymph node assessment in melanoma: current state and future directions

Assessment of sentinel lymph node status is an important step in the evaluation of patients with melanoma for both prognosis and therapeutic management. Pathologists have an important role in this evaluation. The methodologies have varied over time, from the evaluation of dimensions of metastatic burden to determination of the location of the tumour deposits within the lymph node to precise cell counting. However, no single method of sentinel lymph node tumour burden measurement can currently be used as a sole independent predictor of prognosis. The management approach to sentinel lymph node-positive patients has also evolved over time, with a more conservative approach recently recognised for selected cases. This review gives an overview of past and current status in the field with a glimpse into future directions based on prior experiences and clinical trials.

Prognostic analysis of cutaneous Kaposi sarcoma based on a competing risk model

The data regarding the prognosis of cutaneous Kaposi sarcoma (KS) was limited. The current study aimed to explore the risk factors and develop a predictive model for the prognosis of cutaneous KS patients. Data were extracted from Surveillance, Epidemiology, and End Results database from 2000 to 2018 and randomly divided into training and validation cohort. The Kaplan-Meier analysis, cumulative incidence function based on the competing risk model and Fine-Gray multivariable regression model was used to identify the prognostic factors and then construct a 5-, 10-, and 15-year KS-specific death (KSSD) nomogram for patients. The concordance index (C-index), area under the curve (AUC) of operating characteristics and calibration plots were used to evaluate the performance of the model. The clinical utility of the model was measured by decision curve analysis (DCA). In 2257 cutaneous KS patients identified from database, the overall median survival time was about 13 years. Radiotherapy (p = 0.013) and surgery (p < 0.001) could lower the KSSD, while chemotherapy (p = 0.042) and surgery (p < 0.001) could increase the overall survival (OS) of patients with metastatic and localized lesions, respectively. Race, number of lesions, surgery, extent of disease, year of diagnosis and age were identified as risk factors associated with cutaneous KS-specific survival. Performance of the nomogram was validated by calibration and discrimination, with C-index values of 0.709 and AUC for 5-, 10-, and 15-year-KSSD of 0.739, 0.728 and 0.725 respectively. DCA indicated that the nomogram had good net benefits in clinical scenarios. Using a competing-risk model, this study firstly identified the prognostic factors, and constructed a validated nomogram to provide individualized assessment and reliable prognostic prediction for cutaneous KS patients.

The Impact of Clinical and Histopathological Factors on Disease Progression and Survival in Thick Cutaneous Melanomas

Thick cutaneous melanomas (Breslow depth > 4 mm) are locally advanced tumors, generally associated with poor prognosis. Nevertheless, these tumors sometimes display unpredictable behavior. This study aims to analyze clinical and histopathological features that can influence the prognosis of thick melanomas. This is a retrospective study on 94 thick primary cutaneous melanomas diagnosed between 2012 and 2018 that were followed-up for at least five years to assess disease progression and survival. We evaluated the age, gender, tumor location, histological subtype, Breslow depth, Clark level, resection margins, mitotic index, the presence/absence of ulceration, necrosis, regression, microsatellites, neurotropism, lymphovascular invasion, and the pattern of tumor-infiltrating lymphocytes, and their association with disease progression and survival. By conducting univariate analysis, we found that progression-free survival (PFS) was significantly associated with female gender, the superficial spreading melanoma (SSM) subtype, mitotic index, necrosis, microsatellites, and perineural invasion. Overall survival (OS) was significantly associated with female gender, Breslow depth, SSM subtype, necrosis, microsatellites, and perineural invasion. Through multivariate Cox proportional hazards regression, we found that the only factors associated with PFS were Breslow depth, necrosis, microsatellites, and perineural invasion, while the factors associated with OS were Breslow depth, necrosis, microsatellites, and perineural invasion. Certain histopathological features such as Breslow depth, necrosis, microsatellites, and perineural invasion could explain differences in disease evolution. This is one of the first studies to demonstrate an association between necrosis and perineural invasion and outcomes in patients with thick melanomas. By identifying high-risk patients, personalized therapy can be provided for improved prognosis.

Development and validation of a risk prediction model for cage subsidence after instrumented posterior lumbar fusion based on machine learning: a retrospective observational cohort study

Background

Interbody cage subsidence is a common complication after instrumented posterior lumbar fusion surgery, several previous studies have shown that cage subsidence is related to multiple factors. But the current research has not combined these factors to predict the subsidence, there is a lack of an individualized and comprehensive evaluation of the risk of cage subsidence following the surgery. So we attempt to identify potential risk factors and develop a risk prediction model that can predict the possibility of subsidence by providing a Cage Subsidence Score (CSS) after surgery, and evaluate whether machine learning-related techniques can effectively predict the subsidence.

Methods

This study reviewed 59 patients who underwent posterior lumbar fusion in our hospital from 2014 to 2019. They were divided into a subsidence group and a non-subsidence group according to whether the interbody fusion cage subsidence occurred during follow-up. Data were collected on the patient, including age, sex, cage segment, number of fusion segments, preoperative space height, postoperative space height, preoperative L4 lordosis Angle, postoperative L4 lordosis Angle, preoperative L5 lordosis Angle, postoperative PT, postoperative SS, postoperative PI. The conventional statistical analysis method was used to find potential risk factors that can lead to subsidence, then the results were incorporated into stepwise regression and machine learning algorithms, respectively, to build a model that could predict the subsidence. Finally the diagnostic efficiency of prediction is verified.

Results

Univariate analysis showed significant differences in pre-/postoperative intervertebral disc height, postoperative L4 segment lordosis, postoperative PT, and postoperative SS between the subsidence group and the non-subsidence group (p < 0.05). The CSS was trained by stepwise regression: 2 points for postoperative disc height > 14.68 mm, 3 points for postoperative L4 segment lordosis angle >16.91°, and 4 points for postoperative PT > 22.69°. If the total score is larger than 0.5, it is the high-risk subsidence group, while less than 0.5 is low-risk. The score obtains the area under the curve (AUC) of 0.857 and 0.806 in the development and validation set, respectively. The AUC of the GBM model based on the machine learning algorithm to predict the risk in the training set is 0.971 and the validation set is 0.889. The AUC of the avNNet model reached 0.931 in the training set and 0.868 in the validation set, respectively.

Conclusion

The machine learning algorithm has advantages in some indicators, and we have preliminarily established a CSS that can predict the risk of postoperative subsidence after lumbar fusion and confirmed the important application prospect of machine learning in solving practical clinical problems.

A Clinical Decision Tool to Calculate Pretest Probability of Sentinel Lymph Node Metastasis in Primary Cutaneous Melanoma

BACKGROUND

Although sentinel lymph node biopsy (SLNB) status is a strong prognostic indicator for cutaneous melanoma, unnecessary SLNBs have substantial cost and morbidity burden.

OBJECTIVE

This study was designed to develop, validate, and present a personalized, clinical, decision-making tool using nationally representative data with clinically actionable probability thresholds (Expected Lymphatic Metastasis Outcome [ELMO]).

METHODS

Data from the Surveillance, Epidemiology, and End Results (SEER) Registry from 2000 to 2017 and the National Cancer Database (NCDB) from 2004 to 2015 were used to develop and internally validate a logistic ridge regression predictive model for SLNB positivity. External validation was done with 1568 patients at a large tertiary referral center.

RESULTS

The development cohort included 134,809 patients, and the internal validation cohort included 38,518 patients. ELMO (AUC 0.85) resulted in a 29.54% SLNB reduction rate and greater sensitivity in predicting SLNB status for T1b, T2a, and T2b tumors than previous models. In external validation, ELMO had an accuracy of 0.7586 and AUC of 0.7218. Limitations of this study are potential miscoding, unaccounted confounders, and effect modification.

CONCLUSIONS

ELMO ( https://melanoma-sentinel.herokuapp.com/ ) has been developed and validated (internally and externally) by using the largest publicly available dataset of melanoma patients and was found to have high accuracy compared with other published models and gene expression tests. Individualized risk estimates for SLNB positivity are critical in facilitating thorough decision-making for healthcare providers and patients with melanoma.

Nomogram predicts the prognosis of patients with thymic carcinoma: A population-based study using SEER data

BACKGROUND

Thymic carcinoma (TC) is a rare malignant tumor that can have a poor prognosis, and accurate prognostication prediction remains difficult. We aimed to develop a nomogram to predict overall survival (OS) and cancer-specific survival (CSS) based on a large cohort of patients.

METHODS

The Surveillance Epidemiology and End Results (SEER) database was searched to identify TC patients (1975-2016). Univariate and multivariable Cox regression analyses were used to identify predictors of OS and CSS, which were used to construct nomograms. The nomograms were evaluated using the concordance index (C-index), calibration curve, receiver operating characteristic curve, and decision curve analysis (DCA). Subgroup analysis was performed to identify high-risk patients.

RESULTS

The analysis identified six predictors of OS (Masaoka stage, surgical method, lymph node metastasis, liver metastasis, bone metastasis, and radiotherapy) and five predictors of CSS (Masaoka stage, surgical method, lymph node metastasis, tumor size, and brain metastasis), which were used to create nomograms for predicting three-year and five-year OS and CSS. The nomograms had reasonable C-index values (OS: 0.687 [training] and 0.674 [validation], CSS: 0.712 [training] and 0.739 [validation]). The DCA curve revealed that the nomograms were better for predicting OS and CSS, relative to the Masaoka staging system.

CONCLUSION

We developed nomograms using eight clinicopathological factors that predicted OS and CSS among TC patients. The nomograms performed better than the traditional Masaoka staging system and could identify high-risk patients. Based on the nomograms' performance, we believe they will be useful prognostication tools for TC patients.

Regression in cutaneous melanoma: histological assessment, immune mechanisms and clinical implications

Tumour regression is an immunologically driven process that results in complete or partial disappearance of tumour cells. This can be observed in histological sections as replacement of tumour cells with fibrosis, angiogenesis, and a variable inflammatory infiltrate. In primary cutaneous melanoma, the prognostic significance of regression has been debated for decades, in part because inconsistent histological criteria are used in prognostication studies. It is broadly accepted that CD8+ T lymphocytes are the primary effectors of the anti-tumour response, but the interplay between melanoma and the immune system is complex, dynamic, and incompletely understood. Sustained progress in unravelling the pathogenesis of melanoma regression has led to the identification of therapeutic targets, culminating in the development of immune checkpoint inhibitors for the management of advanced disease. Modern techniques allow for high-resolution spatial analyses of the tumour microenvironment. Such studies may lead to better understanding of the immune drivers of melanoma regression, thereby facilitating the search for new prognostic and predictive biomarkers to assist clinical decision-making.

Current Controversies in Melanoma Treatment

LEARNING OBJECTIVES

After reading this article and viewing the videos, the participant should be able to: 1. Discuss margins for in situ and invasive disease and describe reconstructive options for wide excision defects, including the keystone flap. 2. Describe a digit-sparing alternative for subungual melanoma. 3. Calculate personalized risk estimates for sentinel node biopsy using predictive nomograms. 4. Describe the indications for lymphadenectomy and describe a technique intended to reduce the risk of lymphedema following lymphadenectomy. 5. Offer options for in-transit melanoma management.

SUMMARY

Melanoma management continues to evolve, and plastic surgeons need to stay at the forefront of advances and controversies. Appropriate margins for in situ and invasive disease require consideration of the trials on which they are based. A workhorse reconstruction option for wide excision defects, particularly in extremities, is the keystone flap. There are alternative surgical approaches to subungual tumors besides amputation. It is now possible to personalize a risk estimate for sentinel node positivity beyond what is available for groups of patients with a given stage of disease. Sentinel node biopsy can be made more accurate and less morbid with novel adjuncts. Positive sentinel node biopsies are now rarely managed with completion lymphadenectomy. Should a patient require lymphadenectomy, immediate lymphatic reconstruction may mitigate the lymphedema risk. Finally, there are minimally invasive modalities for effective control of in-transit recurrences.

A Clinical Prediction Model Based on Post Large Artery Atherosclerosis Infarction Pneumonia

BACKGROUND AND PURPOSE

Stroke-associated pneumonia (SAP) has been found as a common complication in acute ischemic stroke (AIS) patients. Large artery atherosclerosis (LAA) infarct is a major subtype of AIS. This study aimed to build a clinical prediction model for SAP of LAA type AIS patients.

METHODS

This study included 295 patients with LAA type AIS. Univariate analyses and logistic regression analyses were conducted to determine the independent predictors for the modeling purpose. Nomogram used receiver operating characteristics to assess the accuracy of the model, and the calibration plots were employed to assess the fitting degree between the model and the practical scenario. One hundred and five patients were employed for the external validation to test the stability of the model.

RESULTS

From the univariate analysis, patients' ages, neutrophil-to-lymphocyte ratios, National Institute of Health Stroke scale (NIHSS) scores, red blood cell, sex, history of coronary artery disease, stroke location and volume-viscosity swallow test showed statistical difference in the development group for the occurrence of SAP. By incorporating the factors above into a multivariate logistic regression analysis, patients' ages, neutrophil-to-lymphocyte ratios, NIHSS, and volume-viscosity swallow test emerged as the independent risk factors of the development of SAP. The nomogram based on the mentioned 4 variables above achieved a receiver operating characteristic of 0.951 and a validation group of 0.946.

CONCLUSIONS

The proposed nomogram is capable of predicting predict the occurrence of SAP in LAA type AIS patients, and it may identify high-risk patients in time and present information for in-depth treatment.

Sentinel Lymph Node Biopsy in Cutaneous Melanoma, a Clinical Point of View

Sentinel lymph node biopsy (SLNB) is a surgical procedure that has been used in patients with cutaneous melanoma for nearly 30 years. It is used for both staging and regional disease control with minimum morbidity, as proven by numerous worldwide prospective studies. It has been incorporated in the recommendations of national and professional guidelines. In this article, we provide a summary of the general information on SLNB in the clinical guidelines for the management of cutaneous malignant melanoma (American Association of Dermatology, European Society of Medical Oncology, National Comprehensive Cancer Network, and Cancer Council Australia) and review the most relevant literature to provide an update on the existing recommendations for SLNB.

Early Diagnosis of Bipolar Disorder Coming Soon: Application of an Oxidative Stress Injury Biomarker (BIOS) Model

Early distinction of bipolar disorder (BD) from major depressive disorder (MDD) is difficult since no tools are available to estimate the risk of BD. In this study, we aimed to develop and validate a model of oxidative stress injury for predicting BD. Data were collected from 1252 BD and 1359 MDD patients, including 64 MDD patients identified as converting to BD from 2009 through 2018. 30 variables from a randomly-selected subsample of 1827 (70%) patients were used to develop the model, including age, sex, oxidative stress markers (uric acid, bilirubin, albumin, and prealbumin), sex hormones, cytokines, thyroid and liver function, and glycolipid metabolism. Univariate analyses and the Least Absolute Shrinkage and Selection Operator were applied for data dimension reduction and variable selection. Multivariable logistic regression was used to construct a model for predicting bipolar disorder by oxidative stress biomarkers (BIOS) on a nomogram. Internal validation was assessed in the remaining 784 patients (30%), and independent external validation was done with data from 3797 matched patients from five other hospitals in China. 10 predictors, mainly oxidative stress markers, were shown on the nomogram. The BIOS model showed good discrimination in the training sample, with an AUC of 75.1% (95% CI: 72.9%-77.3%), sensitivity of 0.66, and specificity of 0.73. The discrimination was good both in internal validation (AUC 72.1%, 68.6%-75.6%) and external validation (AUC 65.7%, 63.9%-67.5%). In this study, we developed a nomogram centered on oxidative stress injury, which could help in the individualized prediction of BD. For better real-world practice, a set of measurements, especially on oxidative stress markers, should be emphasized using big data in psychiatry.

Prognostic nomogram and novel risk-scoring system for small cell lung cancer with different patterns of metastases

OBJECTIVE

This research is aimed to develop the prognostic nomogram and novel risk-scoring system for small cell lung cancer (SCLC) with different patterns of metastases.

METHODS

Data on SCLC patients were extracted from the 2010-2015 Surveillance, Epidemiology, and End Results (SEER) database. This nomogram prognostic model was confirmed in the validation cohort. C-index and calibration curve were used to evaluate the accuracy of nomogram model. The predictive capability and net benefit of nomogram was estimated by decision curve analysis (DCA). The cut-off point of the risk stratification system based on nomogram was assessed by X-tile analysis.

RESULTS

Our Cox model indicated that age, gender, American Joint Committee on Cancer (AJCC) stage, metastases, chemotherapy, radiation and surgery were independent predictors for OS in SCLC patients. The C-index value of nomogram integrating significant variables for predicting OS in SCLC patients was 0.752 in SEER training set and 0.748 in SEER validation set, respectively. However, the TNM stage only had C-indexes of 0.464 and 0.472 for predicting OS, respectively. The nomogram prognostic model in this study showed higher C-indexes than those in the TNM stage. The C-index value and high quality of calibration plots indicate that the predictive ability of our nomogram model was of great superiority. DCA showed the nomogram had good clinical value. SCLC patients were further divided into low-risk and high-risk group according to nomogram predicted scores.

CONCLUSION

Our nomogram model that integrated significant factors can aid as an individualized clinical predictive tool in SCLC patients.

Predictors of Sentinel Lymph Node Metastasis in Patients with Thin Melanoma: An International Multi-institutional Collaboration

BACKGROUND

Consideration of sentinel lymph node biopsy (SLNB) is recommended for patients with T1b melanomas and T1a melanomas with high-risk features; however, the proportion of patients with actionable results is low. We aimed to identify factors predicting SLNB positivity in T1 melanomas by examining a multi-institutional international population.

METHODS

Data were extracted on patients with T1 cutaneous melanoma who underwent SLNB between 2005 and 2018 at five tertiary centers in Europe and Canada. Univariable and multivariable logistic regression analyses were performed to identify predictors of SLNB positivity.

RESULTS

Overall, 676 patients were analyzed. Most patients had one or more high-risk features: Breslow thickness 0.8-1 mm in 78.1% of patients, ulceration in 8.3%, mitotic rate > 1/mm² in 42.5%, Clark's level ≥ 4 in 34.3%, lymphovascular invasion in 1.4%, nodular histology in 2.9%, and absence of tumor-infiltrating lymphocytes in 14.4%. Fifty-three patients (7.8%) had a positive SLNB. Breslow thickness and mitotic rate independently predicted SLNB positivity. The odds of positive SLNB increased by 50% for each 0.1 mm increase in thickness past 0.7 mm (95% confidence interval [CI] 1.05-2.13) and by 22% for each mitosis per mm² (95% CI 1.06-1.41). Patients who had one excised node (vs. two or more) were three times less likely to have a positive SLNB (3.6% vs. 9.6%; odds ratio 2.9 [1.3-7.7]).

CONCLUSIONS

Our international multi-institutional data confirm that Breslow thickness and mitotic rate independently predict SLNB positivity in patients with T1 melanoma. Even within this highly selected population, the number needed to diagnose is 13:1 (7.8%), indicating that more work is required to identify additional predictors of sentinel node positivity.

Utility of a Model for Predicting the Risk of Sentinel Lymph Node Metastasis in Patients With Cutaneous Melanoma

Importance

A neural network-based model (i31-GEP-SLNB) that uses clinicopathologic factors (thickness, mitoses, ulceration, patient age) plus molecular analysis (31-gene expression profiling) has become commercially available to guide selection for sentinel lymph node (SLN) biopsy in cutaneous melanoma, but its clinical utility is not well characterized.

Objective

To determine if use of the i31-GEP-SLNB model is associated with clinical benefit when used to select patients for SLN biopsy.

Design, Setting, and Participants

This decision-analytic study used data derived from a published external validation study of the i31-GEP-SLNB prediction model. Participants included patients with primary cutaneous melanoma.

Main Outcomes and Measures

The primary outcome was the net benefit associated with using the i31-GEP-SLNB model for SLN biopsy selection compared with other selection strategies (SLN biopsy for all patients and SLN biopsy for no patients) at a 5% risk threshold. Analyses were stratified by American Joint Committee on Cancer (AJCC) T category. The reduction in the number of avoidable SLN biopsies and relative utility were also calculated.

Results

Compared with other SLN biopsy selection strategies, use of the i31-GEP-SLNB model had greater net benefit for patients with T1b (+0.012), T2a (+0.002), and T2b melanoma (+0.002) but not for those with high-risk T1a (-0.003) disease. The improvement in relative utility was +22% in patients with T1b, +1% in T2a, and +2% in T2b melanoma. Compared with SLN biopsy for all patients, use of the model would equate to a 23% decrease in SLN biopsies among patients with T1b disease without an SLN metastasis with no increase in the number of patients with an SLN metastasis left untreated; among patients with T2a and T2b melanoma, the net decrease in avoidable biopsies compared with SLN biopsy for all was 3% and 4%, respectively.

Conclusions and Relevance

The findings of this decision-analytic study suggest that i31-GEP SLNB has significant potential for risk-stratifying patients with T1b melanoma if using a 5% risk threshold; its role among patients with T1a and T2 melanoma or using other risk thresholds requires further study. A prospective validation study confirming the added clinical benefit and cost-effectiveness of i31-GEP-SLNB compared with free clinicopathologic-based prediction models is needed in patients with T1b melanoma.

Tumor Response and Nomogram-Based Prognostic Stratification for Hepatocellular Carcinoma After Drug-Eluting Beads Transarterial Chemoembolization

Purpose

To explore the tumor response and propose a nomogram-based prognostic stratification for hepatocellular carcinoma (HCC) after drug-eluting beads transarterial chemoembolization (DEB-TACE).

Patients and Methods

From the database of two centers, patients who received DEB-TACE as an initial treatment were enrolled and divided into the training and validation sets. The tumor response after DEB-TACE was estimated according to the Modified Response Evaluation Criteria in Solid Tumors. Using the independent survival predictors in the training set, a nomogram was constructed and validated internally and externally by measuring concordance index (C-index) and calibration. A prognostic stratification based on the nomogram was established.

Results

A total of 335 patients met the inclusion criteria for the study. Alkaline phosphatase level, tumor maximum diameter, tumor capsule and portal vein invasion were interrelated with the achievement of complete release after DEB-TACE. Alpha-fetoprotein level, Child-Pugh class, tumor maximum diameter, tumor number, tumor extent and portal vein invasion were integrated into the nomogram. The nomogram demonstrated good calibration and discrimination, with C-indexes of 0.735 and 0.854 and higher area under the curve (AUC) than BCLC and CNLC staging systems in the internal and external validation sets. The prognostic stratification classified patients into three different risk groups, which had significant differences in survival, complete release and objective response rate between any two groups (P < 0.05).

Conclusion

The nomogram-based prognostic stratification has a good distinction and may help to identify the patients benefiting from DEB-TACE.

The Use and Technique of Sentinel Node Biopsy for Skin Cancer

LEARNING OBJECTIVES

After studying this article, the participant should be able to: 1. Understand the indications for and prognostic value of sentinel lymph node biopsy in skin cancer. 2. Learn the advantages and disadvantages of various modalities used alone or in combination when performing sentinel lymph node biopsy. 3. Understand how to perform sentinel lymph node biopsy in skin cancer patients.

SUMMARY

Advances in technique used to perform sentinel lymph node biopsy to assess lymph node status have led to increased accuracy of the procedure and improved patient outcomes.

Nomogram for Predicting the Probability of Permanent Stoma after Laparoscopic Intersphincteric Resection

PURPOSE

The purpose of this study was to determine the risk factors for the development of a permanent stoma in laparoscopic intersphincteric resection (LS-ISR) for ultralow rectal adenocarcinoma and to develop and validate a prediction model to predict the probability of permanent stoma after surgery.

METHODS

A primary cohort consisting of 301 consecutive patients who underwent LS-ISR was enrolled in this study. Multivariable logistic regression analysis was used to identify risk factors and develop the nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. An independent validation cohort contained 91 consecutive patients from January 2012 to January 2019.

RESULTS

The permanent stoma rate was 11.3% (34/301) in the primary cohort and 18.7% (17/91) in the validation cohort. Multivariable analysis revealed that nCRT (OR, 3.195; 95% CI, 1.169-8.733; P=0.024), ASA score of 3 (OR, 5.062; 95% CI, 1.877-13.646; P=0.001), distant metastasis (OR, 14.645; 95% CI, 3.186-67.315; P=0.001), and anastomotic leakage (OR, 11.308; 95% CI, 3.650-35.035; P<0.001) were independent risk factors for permanent stoma, and a nomogram was established. The AUCs of the nomogram were 0.842 and 0.858 in the primary and validation cohorts, respectively. The calibration curves showed good calibration in both cohorts. Decision curve analysis demonstrated that the nomogram was clinically useful.

CONCLUSION

We developed and validated a nomogram for ultralow rectal adenocarcinoma patients who underwent LS-ISR, and the nomogram could help surgeons identify which patients are at a higher risk of a permanent stoma after surgery.

Histological regression in melanoma: impact on sentinel lymph node status and survival

Regression in melanoma is an immunological phenomenon that results in partial or complete replacement of the tumor with variably vascular fibrous tissue, often accompanied by pigment-laden macrophages and chronic inflammation. In some cases, tumor-infiltrating lymphocytes (TILs) may represent the earliest phase of this process. The prognostic significance of regression has long been a matter of debate, with inconsistent findings reported in the literature to date. This study sought to determine whether regression in primary cutaneous melanomas predicted sentinel lymph node (SLN) status and survival outcomes in a large cohort of patients managed at a single centre. Clinical and pathological parameters for 8,693 consecutive cases were retrieved. Associations between regression and SLN status, overall survival (OS), melanoma-specific survival (MSS) and recurrence-free survival (RFS) were investigated using logistic and Cox regression. Histological evidence of regression was present in 1958 cases (22.5%). Regression was significantly associated with lower Breslow thickness, lower mitotic rate, and absence of ulceration (p < 0.0001). Multivariable analysis showed that regression in combination with TILs independently predicted a negative SLN biopsy (OR 0.33; 95% C.I. 0.20-0.52; p < 0.0001). Patients whose tumors showed both regression and TILs had the highest 10-year OS (65%, 95% C.I. 59-71%), MSS (85%, 95% C.I. 81-89%), and RFS (60%, 95% C.I. 54-66%). On multivariable analyses, the concurrent presence of regression and TILs independently predicted the lowest risk of death from melanoma (HR 0.69; 95% C.I. 0.51-0.94; p = 0.0003) as well as the lowest rate of disease recurrence (HR 0.71; 95% C.I. 0.58-0.85; p < 0.0001). However, in contrast, in the subgroup analysis of Stage III patients, the presence of regression predicted the lowest OS and RFS, with MSS showing a similar trend. Overall, these findings indicate a prognostically favorable role of regression in primary cutaneous melanoma. However, in Stage III melanoma patients, regression may be a marker of more aggressive disease.

More sentinel lymph node biopsies for thin melanomas after transition to AJCC 8th edition do not increase positivity rate: A Danish population-based study of 7148 patients

BACKGROUND

We evaluated the outcome of sentinel lymph node biopsies (SLNB) in patients with thin melanoma before and after the implementation of AJCC 8th edition (AJCC8) and identified predictors of positive sentinel lymph nodes (+SLN).

METHODS

Patients diagnosed with T1 melanomas (Breslow thickness ≤1 mm) during 2016-2017 as per AJCC 7th edition (AJCC7) (n = 3414) and 2018-2019 as per AJCC8 (n = 3734) were identified in the Danish Melanoma Database.

RESULTS

More SLNBs were performed in the AJCC8 cohort compared to the AJCC7 (22.2% vs. 16.2%, p < 0.001), with no significant difference in +SLN rates (4.7% vs. 6.7%, p = 0.118). In the AJCC7 + SLN subgroup, no melanomas were ulcerated, 94.6% had mitotic rate (MR) ≥ 1, 67.6% were ≥0.8 mm and 32.4% would be T1a according to AJCC8. In the AJCC8 + SLN subgroup, 10.3% were ulcerated, 74.4% had MR≥ 1, 97.4% were ≥0.8 mm and 23.1% would be T1a according to AJCC7. On multivariable analysis younger age and MR ≥ 1 were significant predictors of +SLN.

CONCLUSION

More SLNBs were performed in T1 melanomas after transition to AJCC8 without an increase in +SLN rate. None of the AJCC8 T1b criteria were significant predictors of +SLN. We suggest that mitosis and younger age should be considered as indications for SLNB in thin melanoma.

Regression in melanoma is significantly associated with a lower regional recurrence rate and better recurrence-free survival

BACKGROUND AND OBJECTIVES

The prognostic significance of regression in predicting melanoma recurrences is unknown. We present a large multicenter study correlating regression with recurrence.

METHODS

The Sentinel Lymph Node Working Group database was queried from 1993 to 2018 for cases with regression data. Clinicopathologic factors were correlated with overall and first-site of recurrence and with recurrence-free survival (RFS).

RESULTS

There were 4790 patients and the median follow-up was 39.6 months. Regression and recurrences were seen in 1081 (22.6%) and 773 (16.1%) cases, respectively. First-site locoregional and distant recurrences were seen in 412 (8.6%) and 352 (7.3%) patients, respectively. Regression was seen in 15.8% and 24.7% of all cases with and without recurrences (p < 0.0001), respectively, while regression was seen in 14.3% and 17.9% of first-site locoregional and distant recurrent cases, respectively, compared with 23.3% and 22.9% of patients with regression and without first-site locoregional and distant recurrences, respectively (p = 0.29). On multivariable analysis, after controlling for age, gender, thickness, ulceration, lymphovascular invasion, and sentinel lymph node status, regression significantly predicted improved RFS (p = 0.004) and fewer first-site regional recurrences (p = 0.017).

CONCLUSION

Our data suggest that regression is a favorable prognostic marker in melanoma and predicts significantly better RFS and decreased first-site regional recurrences.

Risk-score model to predict prognosis of malignant airway obstruction after interventional bronchoscopy

Background

Interventional bronchoscopy exhibits substantial effects for patients with malignant airway obstruction (MAO), while little information is available regarding the potential prognostic factors for these patients.

Methods

Between October 31, 2016, and July 31, 2019, a total of 150 patients undergoing interventional bronchoscopy and histologically-confirmed MAO were collected, in which 112 eligible participants formed the cohort for survival study. External validation cohort from another independent institution comprised 33 MAO patients with therapeutic bronchoscopy. The least absolute shrinkage and selection operator regression (LASSO) was applied to the model development dataset for selecting features correlated with MAO survival for inclusion in the Cox regression from which we elaborated the risk score system. A nomogram algorithm was also utilized.

Results

In our study, we observed a significant decline of stenosis rate after interventional bronchoscopy from 71.7%±2.1% to 36.6%±2.7% (P<0.001) and interventional bronchoscopy dilated airway effectively. Patients in our study undergoing interventional bronchoscopy had a median survival time of 614.000 days (95% CI: 269.876–958.124). Patients receiving distinct therapeutic methods of interventional bronchoscopy had different prognosis (P=0.022), and patients receiving treatment of electrocoagulation in combination with stenting and electrosurgical snare had worse survival than those receiving other options. Multivariate Cox analysis revealed that nonsmoking status, adenoid cystic carcinoma, and low preoperative stenosis length, as independent predictive factors for better overall survival (OS) of MAO patients. Then, the nomogram based on Cox regression and risk score system based on results from LASSO regression were elaborated respectively. Importantly, this risk score system was proved to have better performance than the nomogram and other single biomarkers such as traditional staging system (area under the curve 0.855 vs. 0.392–0.739). Survival curves showed that patients with the higher risk-score had poorer prognosis than those with lower risk-score (third quantile of OS: 126.000 days, 95% CI: 73.588–178.412 vs. 532.000 days, 95% CI: 0.000–1,110.372; P<0.001).

Conclusions

Nonsmoking status, adenoid cystic carcinoma, and low preoperative stenosis length, were independent predictive factors for better OS of MAO patients. We proposed a nomogram and risk score system for survival prediction of MAO patients undergoing interventional bronchoscopy with good performance.

Compliance with sentinel lymph node biopsy guidelines for invasive melanomas treated with Mohs micrographic surgery

BACKGROUND

Sentinel lymph node biopsy (SLNB) has not been studied for invasive melanomas treated with Mohs micrographic surgery using frozen-section MART-1 immunohistochemical stains (MMS-IHC). The primary objective of this study was to assess the accuracy and compliance with National Comprehensive Cancer Network (NCCN) guidelines for SLNB in a cohort of patients who had invasive melanoma treated with MMS-IHC.

METHODS

This retrospective cohort study included all patients who had primary, invasive, cutaneous melanomas treated with MMS-IHC at a single academic center between March 2006 and April 2018. The primary outcomes were the rates of documenting discussion and performing SLNB in patients who were eligible based on NCCN guidelines. Secondary outcomes were the rate of identifying the sentinel lymph node and the percentage of positive lymph nodes.

RESULTS

In total, 667 primary, invasive, cutaneous melanomas (American Joint Committee on Cancer T1a-T4b) were treated with MMS-IHC. The median patient age was 69 years (range, 25-101 years). Ninety-two percent of tumors were located on specialty sites (head and/or neck, hands and/or feet, pretibial leg). Discussion of SLNB was documented for 162 of 176 (92%) SLNB-eligible patients, including 127 of 127 (100%) who had melanomas with a Breslow depth >1 mm. SLNB was performed in 109 of 176 (62%) SLNB-eligible patients, including 102 of 158 melanomas (65%) that met NCCN criteria to discuss and offer SLNB and 7 of 18 melanomas (39%) that met criteria to discuss and consider SLNB. The sentinel lymph node was successfully identified in 98 of 109 patients (90%) and was positive in 6 of those 98 patients (6%).

CONCLUSIONS

Combining SLNB and MMS-IHC allows full pathologic staging and confirmation of clear microscopic margins before reconstruction of specialty site invasive melanomas. SLNB can be performed accurately and in compliance with consensus guidelines in patients with melanoma using MMS-IHC.

Nomogram predict relapse-free survival of patients with thymic epithelial tumors after surgery

BACKGROUND

Hematological indicators and clinical characteristics play an important role in the evaluation of the progression and prognosis of thymic epithelial tumors. Therefore, we aimed to combine these potential indicators to establish a prognostic nomogram to determine the relapse-free survival (RFS) of patients with thymic epithelial tumors undergoing thymectomy.

METHODS

This retrospective study was conducted on 156 patients who underwent thymectomy between May 2004 and August 2015. Cox regression analysis were performed to determine the potential indicators related to prognosis and combine these indicators to create a nomogram for visual prediction. The prognostic predictive ability of the nomogram was evaluated using the consistency index (C-index), receiver operating characteristic (ROC) curve, and risk stratification. Decision curve analysis was used to evaluate the net benefits of the model.

RESULTS

Preoperative albumin levels, neutrophil-to-lymphocyte ratio (NLR), T stage, and WHO histologic types were included in the nomogram. In the training cohort, the nomogram showed well prognostic ability (C index: 0.902). Calibration curves for the relapse-free survival (RFS) were in good agreement with the standard lines in training and validation cohorts.

CONCLUSIONS

Combining clinical and hematologic factors, the nomogram performed well in predicting the prognosis and the relapse-free survival of this patient population. And it has potential to identify high-risk patients at an early stage. This is a relatively novel approach for the prediction of RFS in this patient population.

Identification and Validation of Autophagy-Related Gene Nomograms to Predict the Prognostic Value of Patients with Cervical Cancer

Autophagy is a process of engulfing one's own cytoplasmic proteins or organelles and coating them into vesicles, fusing with lysosomes to form autophagic lysosomes, and degrading the contents it encapsulates. Increasing studies have shown that autophagy disorders are closely related to the occurrence of tumors. However, the prognostic role of autophagy genes in cervical cancer is still unclear. In this study, we constructed risk signatures of autophagy-related genes (ARGs) to predict the prognosis of cervical cancer. The expression profiles and clinical information of autophagy gene sets were downloaded from TCGA and GSE52903 queues as training and validation sets. The normal cervical tissue expression profile data from the UCSC XENA website (obtained from GTEx) were used as a supplement to the TCGA normal cervical tissue. Univariate COX regression analysis of 17 different autophagy genes was performed with the consensus approach. Tumor samples from TCGA were divided into six subtypes, and the clinical traits of the six subtypes had different distributions. Further absolute shrinkage and selection operator (LASSO) and multivariable COX regression yielded an autophagy genetic risk model consisting of eight genes. In the training set, the survival rate of the high-risk group was lower than that of the low-risk group (p < 0.0001). In the validation set, the AUC area of the receiver operating characteristic (ROC) curve was 0.772 for the training set and 0.889 for the verification set. We found that high and low risk scores were closely related to TNM stage (p < 0.05). The nomogram shows that the risk score combined with other indicators, such as G, T, M, and N, better predicts 1-, 3-, and 5-year survival rates. Decline curve analysis (DCA) shows that the risk model combined with other indicators produces better clinical efficacy. Immune cells with an enrichment score of 28 showed statistically significant differences related to high and low risk. GSEA enrichment analysis showed the main enrichment being in KRAS activation, genes defining epithelial and mesenchymal transition (EMT), raised in response to the low oxygen level (hypoxia) gene and NF-kB in response to TNF. These pathways are closely related to the occurrence of tumors. Our constructed autophagy risk signature may be a prognostic tool for cervical cancer.

Regression is significantly associated with outcomes for patients with melanoma

BACKGROUND

The prognostic significance of regression in melanoma is debated. We present a large multicenter study correlating regression with sentinel lymph node metastasis and melanoma-specific survival.

METHODS

The Sentinel Lymph Node Working Group database was reviewed from 1993 to 2018. Patients with known regression and sentinel lymph node status were included. Clinicopathologic factors were correlated with regression, sentinel lymph node status, and melanoma-specific survival.

RESULTS

There were 4,790 patients; median follow-up was 39.6 months. Regression was present in 1,081 (22.6%) cases, and 798 (16.7%) patients had sentinel lymph node metastases. On multivariable analysis, male sex, truncal tumors, and decreasing thickness were significantly associated with regression (P < .05), whereas head/neck or leg tumors had lower rates of regression (P < .05). Regression was significantly correlated with a decreased risk of sentinel lymph node disease on multivariable analysis (odds ratio 0.68, 95% confidence interval 0.54-0.85; P = .0008). Multivariable analysis also showed that increasing age, male sex, increasing thickness, ulceration, lymphovascular invasion, microsatellitosis, and sentinel lymph node metastasis were significantly (P < .05) associated with worse melanoma-specific survival, while regression was significantly associated with better melanoma-specific survival (hazard ratio 0.75, 95% confidence interval 0.57-0.99; P = .043).

CONCLUSION

This large study shows that regression is significantly associated with better outcomes in patients with melanoma and is correlated with a lower risk of sentinel lymph node metastasis and a better melanoma-specific survival.

Prognostic score model-based signature genes for predicting the prognosis of metastatic skin cutaneous melanoma

PURPOSE

Cutaneous melanoma (SKCM) is the most invasive malignancy of skin cancer. Metastasis to distant lymph nodes or other system is an indicator of poor prognosis in melanoma patients. The aim of this study was to identify reliable prognostic biomarkers for SKCMs.

METHODS

Four RNA-sequencing datasets associated with SKCMs were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database as well as corresponding clinical information. Differentially expressed genes (DEGs) were screened between primary and metastatic samples by using MetaDE tool. Weighted gene co-expression network analysis (WGCNA) was conducted to screen functional modules. A prognostic score (PS)-based predictive model and nomogram model were constructed to identify signature genes and independent clinicopathologic factors.

RESULTS

Based on MetaDE analysis and WGCNA, a total of 456 overlapped genes were identified as hub genes related to SKCMs progression. Functional enrichment analysis revealed these genes were mainly involved in the hippo signaling pathway, signaling pathways regulating pluripotency of stem cells, pathways in cancer. In addition, eight optimal DEGs (RFPL1S, CTSV, EGLN3, etc.) were identified as signature genes by using PS model. Cox regression analysis revealed that pathologic stage T, N and recurrence were independent prognostic factors. Three clinical factors and PS status were incorporated to construct a nomogram predictive model for estimating the three years and five-year survival probability of individuals.

CONCLUSIONS

The prognosis prediction model of this study may provide a promising method for decision making in clinic and prognosis predicting of SKCM patients.

Individualized model for predicting COVID-19 deterioration in patients with cancer: A multicenter retrospective study

The 2019 novel coronavirus has spread rapidly around the world. Cancer patients seem to be more susceptible to infection and disease deterioration, but the factors affecting the deterioration remain unclear. We aimed to develop an individualized model for prediction of coronavirus disease (COVID-19) deterioration in cancer patients. The clinical data of 276 cancer patients diagnosed with COVID-19 in 33 designated hospitals of Hubei, China from December 21, 2019 to March 18, 2020, were collected and randomly divided into a training and a validation cohort by a ratio of 2:1. Cox stepwise regression analysis was carried out to select prognostic factors. The prediction model was developed in the training cohort. The predictive accuracy of the model was quantified by C-index and time-dependent area under the receiver operating characteristic curve (t-AUC). Internal validation was assessed by the validation cohort. Risk stratification based on the model was carried out. Decision curve analysis (DCA) were used to evaluate the clinical usefulness of the model. We found age, cancer type, computed tomography baseline image features (ground glass opacity and consolidation), laboratory findings (lymphocyte count, serum levels of C-reactive protein, aspartate aminotransferase, direct bilirubin, urea, and d-dimer) were significantly associated with symptomatic deterioration. The C-index of the model was 0.755 in the training cohort and 0.779 in the validation cohort. The t-AUC values were above 0.7 within 8 weeks both in the training and validation cohorts. Patients were divided into two risk groups based on the nomogram: low-risk (total points ≤ 9.98) and high-risk (total points > 9.98) group. The Kaplan-Meier deterioration-free survival of COVID-19 curves presented significant discrimination between the two risk groups in both training and validation cohorts. The model indicated good clinical applicability by DCA curves. This study presents an individualized nomogram model to individually predict the possibility of symptomatic deterioration of COVID-19 in patients with cancer.

Development and validation of a survival nomogram for patients with Siewert type II/III adenocarcinoma of the esophagogastric junction based on real-world data

Background

The clinical staging systems for adenocarcinoma of the esophagogastric junction (AEG) are controversial. We aimed to propose a prognostic nomogram based on real-world data for predicting survival of Siewert type II/III AEG patients after surgery.

Methods

A total of 396 patients with Siewert type II/III AEG diagnosed and treated at the Center for Gastrointestinal Surgery, the First Affiliated Hospital, Sun Yat-sen University, from June 2009 to June 2017 were enrolled. The original data of 29 variables were exported from the electronic medical records system. The nomogram was established based on multivariate Cox regression coefficients, and its performance was measured using Harrell’s concordance index (C-index), receiver operating characteristic (ROC) curve analysis and calibration curve.

Results

A nomogram was constructed based on nine variables. The C-index for overall survival (OS) prediction was 0.76 (95% CI, 0.72 to 0.80) in the training cohort, in the validation-1 cohort was 0.79 (95% CI, 0.72 to 0.86), and 0.73 (95% CI, 0.67 to 0.80) in the validation-2 cohort. Time-dependent ROC curves and calibration curves in all three cohorts showed good prognostic predictive accuracy. We further proved the superiority of the nomogram in predictive accuracy for OS to pathological TNM (pTNM) staging system and other independent prognostic factors. Kaplan-Meier survival curves demonstrated the pTNM stage, grade of differentiation, positive lymph node, log odds of positive lymph node and organ invasion were prognostic factors with good discriminative ability.

Conclusion

The established nomogram demonstrated a more precise prognostic prediction for patients with Siewert type II/III AEG.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08249-x.

How we treat locoregional melanoma

Cutaneous melanoma is the most lethal form of skin cancer and its incidence has been increasing in the past 30 years. Although this is completely resectable in most cases, thicker melanoma and those with regional lymph-node involvement are at a high risk of relapse. In recent years, the management of locoregional disease has drastically changed. In particular, in the 8th Edition of the American Joint Committee on Cancer (AJCC), subgroup classification of TNM (tumor–node–metastasis) has been modified, with the addition of the IIID stage. Furthermore, in recent randomized trials, completion lymph node dissection in case of sentinel lymph node biopsy positivity has not been shown to offer any improvement in overall survival versus observation. Consequently, radical dissection has been recommended as the standard treatment, but only in patients with palpable nodal metastases. However, the major novelty in the treatment of locally advanced melanoma has been the introduction of drugs, already used for metastatic disease, that have also shown clinical efficacy in the adjuvant setting. In fact, immunotherapies and, in the case of BRAF V600E/K-mutated melanoma, combination treatment of BRAF and MEK inhibitors have improved recurrence-free survival in these patients. In this paper, we will describe the current management of a patient with radically resectable melanoma and discuss the key points in light of the latest scientific evidence.

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Melanoma is the deadliest of skin cancers, although most cases are resectable at diagnosis.

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Use of targeted therapies and immunotherapies as adjuvant treatment revolutionized the scenario in stage III melanoma.

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In this review, we summarize all current evidence about locoregional melanoma, including open issues and future directions.

Development and Validation of Nomograms to Predict Local, Regional, and Distant Recurrence in Patients With Thin (T1) Melanomas

PURPOSE

Although the prognosis of patients with thin primary cutaneous melanomas (T1, ≤ 1.0 mm) is generally excellent, some develop recurrence. We sought to develop and validate a model predicting recurrences in patients with thin melanomas.

METHODS

A Dutch population-based cohort (n = 25,930, development set) and a cohort from an Australian melanoma treatment center (n = 2,968, validation set) were analyzed (median follow-up 6.7 and 12.0 years, respectively). Multivariable Cox models were generated for local, regional, and distant recurrence-free survival (RFS). Discrimination was assessed using Harrell's C-statistic for each outcome. Each nomogram performance was evaluated using calibration plots defining low-risk and high-risk groups as the lowest and top 5% of the nomogram risk score, respectively. The nomograms' C-statistics were compared with those of a model including the current American Joint Committee on Cancer staging parameters (T-stage and sentinel node status).

RESULTS

Local, regional, and distant recurrences were found in 209 (0.8%), 503 (1.9%), and 203 (0.8%) Dutch patients, respectively, and 23 (0.8%), 61 (2.1%), and 75 (2.5%) Australian patients, respectively. C-statistics of 0.79 (95% CI, 0.75 to 0.82) for local RFS, 0.77 (95% CI, 0.75 to 0.78) for regional RFS, and 0.80 (95% CI, 0.77 to 0.83) for distant RFS were obtained for the development model. External validation showed C-statistics of 0.80 (95% CI, 0.69 to 0.90), 0.76 (95% CI, 0.70 to 0.82), and 0.74 (95% CI, 0.69 to 0.80), respectively. Calibration plots showed a good match between predicted and observed rates. Using the nomogram, the C-statistic was increased by 9%-12% for the development cohort and by 11%-15% for the validation cohort, compared with a model including only T-stage and sentinel node status.

CONCLUSION

Most patients with thin melanomas have an excellent prognosis, but some develop recurrence. The presented nomograms can accurately identify a subgroup at high risk. An online calculator is available at www.melanomarisk.org.au.