A nomogram that predicts the presence of sentinel node metastasis in melanoma with better discrimination than the American Joint Committee on Cancer staging system

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.

Sentinel lymph node positivity in melanoma: Which risk prediction tool is most accurate?

BACKGROUND

Sentinel lymph node biopsy for melanoma determines treatment and prognostic factors and improves disease-specific survival. To risk-stratify patients for sentinel lymph node biopsy consideration, Memorial Sloan Kettering Cancer Center and Melanoma Institute Australia developed nomograms to predict sentinel lymph node positivity. We aimed to compare the accuracy of these 2 nomograms.

METHODS

A multi-institutional study of patients with melanoma receiving sentinel lymph node biopsy between September 2018 and December 2022 was performed. The accuracy of the 2 risk prediction tools in determining a positive sentinel lymph node biopsy was analyzed using receiver operating characteristic curves and area under the curve.

RESULTS

In total, 532 patients underwent sentinel lymph node biopsy for melanoma; 98 (18.4%) had positive sentinel lymph node. Increasing age was inversely related to sentinel lymph node positivity (P < .01); 35.7% of patients ≤30 years had positive sentinel lymph node compared with 9.7% of patients ≥75 years. When we analyzed the entire study population, accuracy of the 2 risk prediction tools was equal (area under the curveMemorial Sloan Kettering Cancer Center: 0.693; area under the curveMIA: 0.699). However, Memorial Sloan Kettering Cancer Center tool was a better predictor in patients aged ≥75 years (area under the curveMemorial Sloan Kettering Cancer Center: 0.801; area under the curveMelanoma Institute Australia: 0.712, P < .01) but Melanoma Institute Australia tool performed better in patients with a higher mitotic index (mitoses/mm2 ≥2; area under the curveMemorial Sloan Kettering Cancer Center: 0.659; area under the curveMelanoma Institute Australia: 0.717, P = .027). Both models were poor predictors of sentinel lymph node positivity in young patients (age ≤30 years; area under the curveMemorial Sloan Kettering Cancer Center: 0.456; area under the curveMelanoma Institute Australia: 0.589, P = .283).

CONCLUSION

The current study suggests that the 2 risk stratification tools differ in their abilities to predict sentinel lymph node positivity in specific populations: Memorial Sloan Kettering Cancer Center tool is a better predictor for older patients, whereas Melanoma Institute Australia tool is more accurate in patients with a higher mitotic index. Both nomograms performed poorly in predicting sentinel lymph node positivity in young patients.

Sentinel lymph node risk prognostication in primary cutaneous melanoma through tissue-based profiling, potentially redefining the need for sentinel lymph node biopsy

PURPOSE OF REVIEW

The role of Sentinel Lymph Node Biopsy (SLNB) is pivotal in the contemporary staging of cutaneous melanoma. In this review, we examine advanced molecular testing platforms like gene expression profiling (GEP) and immunohistochemistry (IHC) as tools for predicting the prognosis of sentinel lymph nodes. We compare these innovative approaches with traditional staging assessments. Additionally, we delve into the shared genetic and protein markers between GEP and IHC tests and their relevance to melanoma biology, exploring their prognostic and predictive characteristics. Finally, we assess alternative methods to potentially obviate the need for SLNB altogether.

RECENT FINDINGS

Progress in adjuvant melanoma therapy has diminished the necessity of Sentinel Lymph Node Biopsy (SLNB) while underscoring the importance of accurately identifying high-risk stage I and II melanoma patients who may benefit from additional anti-tumor interventions. The clinical application of testing through gene expression profiling (GEP) or immunohistochemistry (IHC) is gaining traction, with platforms such as DecisionDx, Merlin Assay (CP-GEP), MelaGenix GEP, and Immunoprint coming into play. Currently, extensive validation studies are in progress to incorporate routine molecular testing into clinical practice. However, due to significant methodological limitations, widespread clinical adoption of tissue-based molecular testing remains elusive at present.

SUMMARY

While various tissue-based molecular testing platforms have the potential to stratify the risk of sentinel lymph node positivity (SLNP), most suffer from significant methodological deficiencies, including limited sample size, lack of prospective validation, and limited correlation with established clinicopathological variables. Furthermore, the genes and proteins identified by individual gene expression profiling (GEP) or immunohistochemistry (IHC) tests exhibit minimal overlap, even when considering the most well-established melanoma mutations. However, there is hope that the ongoing prospective trial for the Merlin Assay may safely reduce the necessity for SLNB procedures if successful. Additionally, the MelaGenix GEP and Immunoprint tests could prove valuable in identifying high-risk stage I-II melanoma patients and potentially guiding their selection for adjuvant therapy, thus potentially reducing the need for SLNB. Due to the diverse study designs employed, effective comparisons between GEP or IHC tests are challenging, and to date, there is no study directly comparing the clinical utility of these respective GEP or IHC tests.

A predictive nomogram developed and validated for gastric cancer patients with triple-negative tumor markers

Aim: To predict the prognosis of gastric cancer patients with triple-negative tumor markers. Materials & methods: Prognostic factors of the nomogram were identified through univariate and multivariate Cox regression analyses. Calibration and receiver operating characteristic curves were used to assess accuracy. Decision curve analysis and concordance indexes were utilized to compare the nomogram with the pathological tumor, node, metastasis stage. Results: A nomogram incorporating log odds of positive lymph nodes, tumor size and lymphocyte-to-monocyte ratio was constructed. The calibration and receiver operating characteristic curves (area under the curve >0.85) showed high accuracy in predicting overall survival. The concordance indexes (0.832 vs 0.760; p < 0.001) and decision curve analysis demonstrated that the nomogram was superior to the pathological tumor, node, metastasis stage. Conclusion: A prediction and risk stratification nomogram has been developed and validated for gastric cancer patients with triple-negative tumor markers.

Establishment and validation of a nomogram model for predicting the specific mortality risk of melanoma in upper limbs based on the SEER database

For patients with upper limb melanoma, the significance of specific death is more important than that of all-cause death, and traditional survival analysis may overestimate the mortality rate of patients. Therefore, the nomogram model for predicting the specific mortality risk of melanoma in the upper limbs was developed. A population with melanoma in the upper limbs, diagnosed from 2010 to 2015, were selected from the National Cancer Institute database of Surveillance, Epidemiology, and End Results (SEER). The independent predictive factors of specific death were confirmed by the competing risk model of one-factor analysis and multi-factor analysis, and the nomogram was constructed according to the independent predictive factors. 17,200 patients with upper limb melanoma were enrolled in the study (training cohort: n = 12,040; validation cohort: n = 5160). Multi-factor analysis of the competing risk model showed that age, marital status, gender, tumor stage, T stage, M stage, regional lymph node surgery information, radiotherapy, chemotherapy, mitotic cell count, ulcer and whether there were multiple primary cancers, were independent factors affecting the specific death of upper limb melanoma patients (P < 0.05). The nomogram has good predictive ability regarding the specific mortality risk of melanoma in the upper limbs, and could be of great help to formulate prognostic treatment strategies and follow-up strategies that are conducive to survival.

Tumor-infiltrating lymphocytes as a prognostic and predictive factor for Melanoma

INTRODUCTION

Tumor-infiltrating lymphocytes (TILs) have been investigated as prognostic factors in melanoma. Recent advancements in assessing the tumor microenvironment in the setting of more widespread use of immune checkpoint blockade have reignited interest in identifying predictive biomarkers. This review examines the function and significance of TILs in cutaneous melanoma, evaluating their potential as prognostic and predictive markers.

AREAS COVERED

A literature search was conducted on papers covering tumor infiltrating lymphocytes in cutaneous melanoma available online in PubMed and Web of Science from inception to 1 December 2023, supplemented by citation searching. This article encompasses the assessment of TILs, the role of TILs in the immune microenvironment, TILs as a prognostic factor, TILs as a predictive factor for immunotherapy response, and clinical applications of TILs in the treatment of cutaneous melanoma.

EXPERT OPINION

Tumor-infiltrating lymphocytes play a heterogeneous role in cutaneous melanoma. While they have historically been associated with improved survival, their status as independent prognostic or predictive factors remains uncertain. Novel methods of TIL assessment, such as determination of TIL subtypes and molecular signaling, demonstrate potential for predicting therapeutic response. Further, while their clinical utility in risk-stratification in melanoma treatment shows promise, a lack of consensus data hinders standardized application.

Validation of the Melanoma Institute of Australia's Sentinel Lymph Node Biopsy Risk Prediction Tool for Cutaneous Melanoma

BACKGROUND

For patients with cutaneous melanoma, sentinel lymph node biopsy (SLNB) is used to stage regional lymph nodes pathologically and inform prognosis, treatment, and surveillance. To reduce unnecessary surgeries, predictive tools aim to identify those at lowest risk for node-positive disease. The Melanoma Institute of Australia (MIA)'s Prediction Tool for Sentinel Node Metastasis Risk estimates risk of a positive SLNB using patient age and primary melanoma Breslow depth, histologic subtype, ulceration, mitotic rate, and lymphovascular invasion.

METHODS

A single-institution validation was performed of the MIA Calculator with 982 cutaneous melanoma patients that included all relevant clinicopathologic factors and SLNB pathology outcomes. The study evaluated discrimination via receiver operating characteristic (ROC) curves, calibration via calibration plots, and clinical utility via decision curve analysis of the MIA model in various subgroups. The data were fit to MIA model parameters via a generalized linear model to assess the odds ratio of parameters in our dataset.

RESULTS

The Calculator demonstrated limited discrimination based on ROC curves (C-statistic, 0.709) and consistently underestimated risk of SLN positivity. It did not provide a net benefit over SLNB performed on all patients or reduce unnecessary procedures in the risk domain of 0% to 16%. Compared with the original development and validation cohorts, the current study cohort had thinner tumors and a larger proportion of acral melanomas.

CONCLUSIONS

The Calculator generally underestimated SLN positivity risk, including assessment in patients who would be counseled to forego SLNB based on a predicted risk lower than 5%. Recognition of the tool's current limitations emphasizes the need to refine it further for use in medical decision-making.

Population-Based Validation of the MIA and MSKCC Tools for Predicting Sentinel Lymph Node Status

Importance

Patients with melanoma are selected for sentinel lymph node biopsy (SLNB) based on their risk of a positive SLN. To improve selection, the Memorial Sloan Kettering Cancer Center (MSKCC) and Melanoma Institute Australia (MIA) developed predictive models, but the utility of these models remains to be tested.

Objective

To determine the clinical utility of the MIA and MSKCC models.

Design, Setting, and Participants

This was a population-based comparative effectiveness research study including 10 089 consecutive patients with cutaneous melanoma undergoing SLNB from the Swedish Melanoma Registry from January 2007 to December 2021. Data were analyzed from May to August 2023.

Main Outcomes and Measures,

The predicted probability of SLN positivity was calculated using the MSKCC model and a limited MIA model (using mitotic rate as absent/present instead of count/mm2 and excluding the optional variable lymphovascular invasion) for each patient. The operating characteristics of the models were assessed and compared. The clinical utility of each model was assessed using decision curve analysis and compared with a strategy of performing SLNB on all patients.

Results

Among 10 089 included patients, the median (IQR) age was 64.0 (52.0-73.0) years, and 5340 (52.9%) were male. The median Breslow thickness was 1.8 mm, and 1802 patients (17.9%) had a positive SLN. Both models were well calibrated across the full range of predicted probabilities and had similar external area under the receiver operating characteristic curves (AUC; MSKCC: 70.8%; 95% CI, 69.5-72.1 and limited MIA: 69.7%; 95% CI, 68.4-71.1). At a risk threshold of 5%, decision curve analysis indicated no added net benefit for either model compared to performing SLNB for all patients. At risk thresholds of 10% or higher, both models added net benefit compared to SLNB for all patients. The greatest benefit was observed in patients with T2 melanomas using a threshold of 10%; in that setting, the use of the nomograms led to a net reduction of 8 avoidable SLNBs per 100 patients for the MSKCC nomogram and 7 per 100 patients for the limited MIA nomogram compared to a strategy of SLNB for all.

Conclusions and Relevance

This study confirmed the statistical performance of both the MSKCC and limited MIA models in a large, nationally representative data set. However, decision curve analysis demonstrated that using the models only improved selection for SLNB compared to biopsy in all patients when a risk threshold of at least 7% was used, with the greatest benefit seen for T2 melanomas at a threshold of 10%. Care should be taken when using these nomograms to guide selection for SLNB at the lowest thresholds.

Re-exploration of prognosis in type B thymomas: establishment of a predictive nomogram model

OBJECTIVE

To explore the risk factors for disease progression after initial treatment of type B thymomas using a predictive nomogram model.

METHODS

A single-center retrospective study of patients with type B thymoma was performed. The Cox proportional hazard model was used for univariate and multivariate analyses. Variables with statistical and clinical significance in the multivariate Cox regression were integrated into a nomogram to establish a predictive model for disease progression.

RESULTS

A total of 353 cases with type B thymoma were retrieved between January 2012 and December 2021. The median follow-up was 58 months (range: 1-128 months). The 10-year progression-free survival (PFS) was 91.8%. The final nomogram model included R0 resection status and Masaoka stage, with a concordance index of 0.880. Non-R0 resection and advanced Masaoka stage were negative prognostic factors for disease progression (p < 0.001). No benefits of postoperative radiotherapy (PORT) were observed in patients with advanced stage and non-R0 resection (p = 0.114 and 0.284, respectively).

CONCLUSION

The best treatment strategy for type B thymoma is the detection and achievement of R0 resection as early as possible. Long-term follow-up is necessary, especially for patients with advanced Masaoka stage and who have not achieved R0 resection. No prognostic benefits were observed for PORT.

The 31-Gene Expression Profile Test Outperforms AJCC in Stratifying Risk of Recurrence in Patients with Stage I Cutaneous Melanoma

BACKGROUND

Patients with stage I cutaneous melanoma (CM) are considered at low risk for metastasis or melanoma specific death; however, because the majority of patients are diagnosed with stage I disease, they represent the largest number of melanoma deaths annually. The 31-gene expression profile (31-GEP) test has been prospectively validated to provide prognostic information independent of staging, classifying patients as low (Class 1A), intermediate (Class 1B/2A), or high (Class 2B) risk of poor outcomes.

METHODS

Patients enrolled in previous studies of the 31-GEP were combined and evaluated for recurrence-free (RFS) and melanoma-specific survival (MSS) (n = 1261, "combined"). A second large, unselected real-world cohort (n = 5651) comprising clinically tested patients diagnosed 2013-2018 who were linked to outcomes data from the NCI Surveillance, Epidemiology, and End Results (SEER) Program registries was evaluated for MSS.

RESULTS

Combined cohort Class 1A patients had significantly higher RFS than Class 1B/2A or Class 2B patients (97.3%, 88.6%, 77.3%, p < 0.001)-better risk stratification than AJCC8 stage IA (97.5%) versus IB (89.3%). The SEER cohort showed better MSS stratification by the 31-GEP (Class 1A = 98.0%, Class 1B/2A = 97.5%, Class 2B = 92.3%; p < 0.001) than by AJCC8 staging (stage IA = 97.6%, stage IB = 97.9%; p < 0.001).

CONCLUSIONS

The 31-GEP test significantly improved patient risk stratification, independent of AJCC8 staging in patients with stage I CM. The 31-GEP provided greater separation between high- (Class 2B) and low-risk (Class 1A) groups than seen between AJCC stage IA and IB. These data support integrating the 31-GEP into clinical decision making for more risk-aligned management plans.

Clinical Utility of Melanoma Sentinel Lymph Node Biopsy Nomograms

BACKGROUND

For patients with melanoma, the decision to perform sentinel lymph node biopsy (SLNB) is based on the estimated risk of lymph node metastasis. We assessed 3 melanoma SLNB risk-prediction models' statistical performance and their ability to improve clinical decision making (clinical utility) on a cohort of melanoma SLNB cases.

STUDY DESIGN

Melanoma patients undergoing SLNB at a single center from 2003 to 2021 were identified. The predicted probabilities of sentinel lymph node positivity using the Melanoma Institute of Australia, Memorial Sloan Kettering Cancer Center (MSK), and Friedman nomograms were calculated. Receiver operating characteristic and calibration curves were generated. Clinical utility was assessed via decision curve analysis, calculating the net SLNBs that could have been avoided had a given model guided selection at different risk thresholds.

RESULTS

Of 2,464 melanoma cases that underwent SLNB, 567 (23.0%) had a positive sentinel lymph node. The areas under the receiver operating characteristic curves for the Melanoma Institute of Australia, MSK, and Friedman models were 0.726 (95% CI, 0.702 to 0.750), 0.720 (95% CI, 0.697 to 0.744), and 0.721 (95% CI, 0.699 to 0.744), respectively. For all models, calibration was best at predicted positivity rates below 30%. The MSK model underpredicted risk. At a 10% risk threshold, only the Friedman model would correctly avoid a net of 6.2 SLNBs per 100 patients. The other models did not reduce net avoidable SLNBs at risk thresholds of ≤10%.

CONCLUSIONS

The tested nomograms had comparable performance in our cohort. The only model that achieved clinical utility at risk thresholds of ≤10% was the Friedman model.

Prediction of nonsentinel lymph node metastasis in acral melanoma with positive sentinel lymph nodes

BACKGROUND

Metastasis in a nonsentinel lymph node (non-SLN) is an unfavorable independent prognostic factor in cutaneous melanoma (CM). Recent data did suggest potential value of completion lymph node dissection (CLND) in CM patients with non-SLN metastasis. Prediction of non-SLN metastasis assists clinicians in deciding on adjuvant therapy without CLND. We analyzed risk factors and developed a prediction model for non-SLN status in acral melanoma (AM).

METHODS

This retrospective study enrolled 656 cases of melanoma who underwent sentinel lymph node biopsy at Fudan University Shanghai Cancer Center from 2009 to 2017. We identified 81 SLN + AM patients who underwent CLND. Clinicopathologic data, including SLN tumor burden and non-SLN status were examined with Cox and Logistics regression models.

RESULTS

Ulceration, Clark level, number of deposits in the SLN (NumDep) and maximum size of deposits (MaxSize) are independent risk factors associated with non-SLN metastases. We developed a scoring system that combines ulceration, the cutoff values of Clark level V, MaxSize of 2 mm, and NumDep of 5 to predict non-SLN metastasis with an efficiency of 85.2% and 100% positive predictive value in the high-rank group (scores of 17-24).

CONCLUSIONS

A scoring system that included ulceration, Clark level, MaxSize, and NumDep is reliable and effective for predicting non-SLN metastasis in SLN-positive AM.

The limited value of sentinel lymph node biopsy in lentigo maligna melanoma: A nomogram based on the results of 29 years of the nationwide dutch pathology registry (PALGA)

BACKGROUND

Lentigo maligna melanoma (LMM) predominantly presents in the head and neck of the elderly. The value of sentinel lymph node biopsy (SLNB) for LMM patients remains to be determined, as the reported average yield of positive lymph nodes is less than 10%. In this nationwide cohort study, we wanted to identify LMM patients with an increased risk of SLNB-positivity.

METHODS

LMM with an SLNB indication according to the 8th AJCC melanoma guidelines were retrospectively identified from the nationwide network and registry of histo- and cytopathology in the Netherlands (PALGA). A penalized (LASSO) logistic regression analysis was performed to determine the optimal combination of clinicopathological factors to predict a positive SLNB.

RESULTS

Between 1991 and 2020, 1989 LMM patients met our inclusion criteria. SLNB was performed in 16.7% (n = 333) and was positive in 7.5% (25/333). The false-negative rate was 21.9%. Clinically detectable regional lymph node (LN) metastases were found in 1.3% (n = 25). Clinicopathological characteristics best predictive for SLNB-positivity (Odds ratio; 95% CI) were age (0.95; 0.91-0.99), ulceration 1.59 (0.44-4.83), T4-stage (1.81; 0.43-6.2), male sex (1.97; 0.79-5.27), (lymph)angioinvasion (5.07; 0.94-23.31), and microsatellites (7.23; 1.56-32.7) (C-statistic 0.75). During follow-up, regional LN recurrences were detected in 4.2% (83/1989) of patients, of which the majority (74/83) had no evidence of regional LN metastases at baseline.

CONCLUSION

Our findings confirm the limited SLNB-positivity in LMM patients. Based on the identified high-risk clinicopathological features, a nomogram was developed to predict the risk of a positive SLNB.

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.

External validation of the Melanoma Institute Australia Sentinel Node Metastasis Risk Prediction Tool using the National Cancer Database

BACKGROUND

To improve patient selection for sentinel node (SN) biopsy, the Melanoma Institute of Australia (MIA) created a predictive model based on readily available clinicopathologic factors.

OBJECTIVES

Validation of the MIA nomogram using the National Cancer Database (NCDB), a nationwide oncology outcomes database for >1500 Commission-accredited cancer programs in the United States.

METHODS

A total of 60,165 patients were included in the validation. The probability of SN positivity was calculated for each patient. Using calculated probabilities, a receiver operating characteristic curve was generated to assess the model's discrimination ability.

RESULTS

At baseline, the NCDB cohort had different clinicopathologic characteristics compared with the original MIA data set. Despite these differences, the MIA nomogram retained high-predictive accuracy within the NCDB dataset (C-statistic, 0.733 [95% CI, 0.726-0.739]), although calibration weakened for the highest risk decile.

LIMITATIONS

The NCDB collects data from hospital registries accredited by the Commission on Cancer.

CONCLUSIONS

In conclusion, this study validated the use of the MIA nomogram in a nationwide oncology outcomes database collected from >1500 Commission-accredited cancer programs in the United States, demonstrating the potential for this nomogram to predict SN positivity and reduce the number of negative SN biopsies.

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.

Are the MIA and MSKCC nomograms useful in selecting patients with melanoma for sentinel lymph node biopsy?

BACKGROUND AND METHODS

The Melanoma Institute of Australia (MIA) and Memorial Sloan Kettering Cancer Center (MSKCC) nomograms were developed to help guide sentinel lymph node biopsy (SLNB) decisions. Although statistically validated, whether these prediction models provide clinical benefit at National Comprehensive Cancer Network guideline-endorsed thresholds is unknown. We conducted a net benefit analysis to quantify the clinical utility of these nomograms at risk thresholds of 5%-10% compared to the alternative strategy of biopsying all patients. External validation data for MIA and MSKCC nomograms were extracted from respective published studies.

RESULTS

The MIA nomogram provided added net benefit at a risk threshold of 9% but net harm at 5%-8% and 10%. The MSKCC nomogram provided added net benefit at risk thresholds of 5% and 9%-10% but net harm at 6%-8%. When present, the magnitude of net benefit was small (1-3 net avoidable biopsies per 100 patients).

CONCLUSION

Neither model consistently provided added net benefit compared to performing SLNB for all patients.

DISCUSSION

Based on published data, use of the MIA or MSKCC nomograms as decision-making tools for SLNB at risk thresholds of 5%-10% does not clearly provide clinical benefit to patients.

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.

Machine learning to predict overall short-term mortality in cutaneous melanoma

BACKGROUND

Cutaneous malignant melanoma (CMM) ranks among the ten most frequent malignancies, clinicopathological staging being of key importance to predict prognosis. Artificial intelligence (AI) has been recently applied to develop prognostically reliable staging systems for CMM. This study aims to provide a useful machine learning based tool to predict the overall CMM short-term survival.

METHODS

CMM records as collected at the Veneto Cancer Registry (RTV) and at the Veneto regional health service were considered. A univariate Cox regression validated the strength and direction of each independent variable with overall mortality. A range of machine learning models (Logistic Regression classifier, Support-Vector Machine, Random Forest, Gradient Boosting, and k-Nearest Neighbors) and a Deep Neural Network were then trained to predict the 3-years mortality probability. Five-fold cross-validation and Grid Search were performed to test the best data preprocessing procedures, features selection, and to optimize models hyperparameters. A final evaluation was carried out on a separate test set in terms of balanced accuracy, precision, recall and F1 score. The best model was deployed as online tool.

RESULTS

The univariate analysis confirmed the significant prognostic value of TNM staging. Adjunctive clinicopathological variables not included in the AJCC 8th melanoma staging system, i.e., sex, tumor site, histotype, growth phase, and age, were significantly linked to overall survival. Among the models, the Neural Network and the Random Forest models featured the best prognostic performance, achieving a balanced accuracy of 91% and 88%, respectively. According to the Gini importance score, age, T and M stages, mitotic count, and ulceration appeared to be the variables with the greatest impact on survival prediction.

CONCLUSIONS

Using data from patients with CMM, we developed an AI algorithm with high staging reliability, on top of which a web tool was implemented ( unipd.link/melanomaprediction ). Being essentially based on routinely recorded clinicopathological variables, it can already be implemented with minimal effort and further tested in the current clinical practice, an essential phase for validating the model's accuracy beyond the original research context.

Influencing factors associated with lymph node status in patients with cutaneous melanoma: An Asian population study

BACKGROUND

Sentinel lymph node (SLN) status is the predominant prognostic factor in patients diagnosed with clinically localized melanoma. The significance of completion lymph node dissection in patients with SLN metastasis is debatable. Not many studies have been conducted on acrallentiginous melanoma (ALM). This study aimed to characterize the prognostic factors of nodal positive ALM and confirm whether ALM patients can undergo the same treatment strategy as non-ALM patients in the Asian population.

METHODS

This is a retrospective review of patients who underwent surgery for cutaneous melanoma (CM) at Taipei Veterans General Hospital between January 1993 and December 2019. We investigated the risk factors for lymph node status. The association between clinicopathological factors and lymph node status of ALM and non-ALM patients was analyzed. Outcomes of completion lymph node dissection (CLND) performed following sentinel lymph node biopsy (SLNB) in the CM and ALM groups were compared.

RESULTS

A total of 197 patients were included in this study. ALM was the most common histological subtype, accounting for 66.5% of all the cases. Patients in the CM and ALM subgroups with metastatic SLN ( p = 0.012) or lymph nodes ( p < 0.001 and p = 0.001) exhibited higher mortality rate. Multivariate analysis showed that patients with clinical presentation of T4 category tumor ( p = 0.012) and lymphovascular invasion ( p = 0.012) had a significantly higher risk of positive lymph nodes. The overall survival of patients with lymph nodes metastasis was not associated with the performance of CLND.

CONCLUSION

Patients in the CM or ALM subgroups with metastatic SLNs or lymph nodes exhibited significantly poorer overall survival. Advanced Breslow thickness and lymphovascular invasion were independent predictive factors for CM and ALM patients with positive lymph node status. There was no significant difference in survival between CM and ALM patients following SLNB, regardless of CLND being performed.

Predicting Regional Lymph Node Recurrence in the Modern Age of Tumor-Positive Sentinel Node Melanoma: The Role of the First Postoperative Ultrasound

BACKGROUND

The Multicenter Selective Lymphadenectomy Trial II (MSLT-II) led to a change in the management of tumor-positive sentinel lymph nodes (SLNs) from completion node dissection (CLND) to nodal observation. This study aimed to evaluate prognostic factors for predicting sentinel node basin recurrence (SNBR) using data from MSLT-II trial participants.

METHODS

In MSLT-II, 1076 patients were treated with observation. Patients were included in the current study if they had undergone a post-sentinel node basin ultrasound (PSNB-US) within 4 months after surgery. The study excluded patients with positive SLN by reverse transcription-polymerase chain reaction (RT-PCR) or incomplete SLN pathologic data. Primary tumor, patient, PSNB-US, and SLN characteristics were evaluated. Multivariable regression analyses were performed to determine independent prognostic factors associated with SNBR.

RESULTS

The study enrolled 737 patients: 193 (26.2%) patients with SNBR and 73 (9.9%) patients with first abnormal US. The patients with an abnormal first US were more likely to experience SNBR (23.8 vs. 5.0%). In the multivariable analyses, increased risk of SNBR was associated with male gender (adjusted hazard ratio [aHR], 1.38; 95% confidence interval [CI], 1.00-1.9; p = 0.049), increasing Breslow thickness (aHR, 1.10; 95% CI, 1.01-1.2; p = 0.038), presence of ulceration (aHR, 1.93; 95% CI, 1.42-2.6; p < 0.001), sentinel node tumor burden greater than 1 mm (aHR, 1.91; 95% CI, 1.10-3.3; p = 0.022), lymphovascular invasion (aHR, 1.53; 95% CI, 1.00-2.3; p = 0.048), and presence of abnormal PSNB-US (aHR, 4.29; 95% CI, 3.02-6.1; p < 0.001).

CONCLUSIONS

The first postoperative US together with clinical and pathologic factors may play an important role in predicting SNBR.

Nomogram for prediction of peritoneal metastasis risk in colorectal cancer

OBJECTIVE

Peritoneal metastasis is difficult to diagnose using traditional imaging techniques. The main aim of the current study was to develop and validate a nomogram for effectively predicting the risk of peritoneal metastasis in colorectal cancer (PMCC).

METHODS

A retrospective case-control study was conducted using clinical data from 1284 patients with colorectal cancer who underwent surgery at the First Affiliated Hospital of Guangxi Medical University from January 2010 to December 2015. Least absolute shrinkage and selection operator (LASSO) regression was applied to optimize feature selection of the PMCC risk prediction model and multivariate logistic regression analysis conducted to determine independent risk factors. Using the combined features selected in the LASSO regression model, we constructed a nomogram model and evaluated its predictive value via receiver operating characteristic (ROC) curve analysis. The bootstrap method was employed for repeated sampling for internal verification and the discrimination ability of the prediction models evaluated based on the C-index. The consistency between the predicted and actual results was assessed with the aid of calibration curves.

RESULTS

Overall, 96 cases of PMCC were confirmed via postoperative pathological diagnosis. Logistic regression analysis showed that age, tumor location, perimeter ratio, tumor size, pathological type, tumor invasion depth, CEA level, and gross tumor type were independent risk factors for PMCC. A nomogram composed of these eight factors was subsequently constructed. The calibration curve revealed good consistency between the predicted and actual probability, with a C-index of 0.882. The area under the curve (AUC) of the nomogram prediction model was 0.882 and its 95% confidence interval (CI) was 0.845-0.919. Internal validation yielded a C-index of 0.868.

CONCLUSION

We have successfully constructed a highly sensitive nomogram that should facilitate early diagnosis of PMCC, providing a robust platform for further optimization of clinical management strategies.

Factors influencing acceptance, adoption and adherence to sentinel node biopsy recommendations in the Australian Melanoma Management Guidelines: a qualitative study using an implementation science framework

BACKGROUND

Sentinel node biopsy (SN biopsy) is a surgical procedure used to accurately stage patients with primary melanoma at high risk of recurrence. Although Australian Melanoma Management Guidelines recommend SN biopsy be considered in patients with melanomas > 1 mm thick, SN biopsy rates in Australia are reportedly low. Our objective was to identify factors impacting the acceptance, adoption and adherence to the Australian SN biopsy guideline recommendations.

METHODS

Opinions of Australian key informants including clinicians, representatives from melanoma education and training providers, professional associations and colleges, and melanoma advocacy organisations were collected through semi-structured interviews (n = 29) and from publicly released statements (n = 14 news articles). Data analysis involved inductive and deductive thematic analysis using Flottorp's determinants framework.

RESULTS

A complex interplay of contemporary and historical factors was identified as influencing acceptance, adoption and adherence to the SN biopsy guideline recommendations at the individual, guideline, patient, organisational and social levels. Expert and peer opinion leaders have played an important role in facilitating or inhibiting adoption of guideline recommendations, as have financial incentives driven by healthcare-funding policies and non-financial incentives including professional identity and standing. Of critical importance have been the social and knowledge boundaries that exist between different professional groups to whom the guidelines apply (surgeons, dermatologists and primary care practitioners) with adherence to the guideline recommendations having the potential to shift work across professional boundaries, altering a clinician's workflow and revenue. More recently, the emergence of effective immunotherapies and targeted therapies for patients at high risk of recurrence, the emergence of new opinion leaders on the topic (in medical oncology), and patient demands for accurate staging are playing crucial roles in overcoming the resistance to change created by these social and knowledge boundaries.

CONCLUSIONS

Acceptance and adherence to SN biopsy guideline recommendations in Australia over the past 20 years has involved a process of renegotiation and reframing of the evidence for SN biopsy in melanoma by clinicians from different professional groups and networks. This process has helped to refine the evidence for SN biopsy and our understanding of appropriate adoption. New effective systemic therapies have changed the balance towards accepting guideline recommendations.

Prognostic biomarkers of cutaneous melanoma

BACKGROUND/PURPOSE

Melanomas account for only approximately 4% of diagnosed skin cancers in the United States but are responsible for the majority of deaths caused by skin cancer. Both genetic factors and ultraviolet (UV) radiation exposure play a role in the development of melanoma. Although melanomas have a strong propensity to metastasize when diagnosed late, melanomas that are diagnosed and treated early pose a low mortality risk. In particular, the identification of patients with increased metastatic risk, who may benefit from early adjuvant therapies, is crucial, especially given the advent of new melanoma treatments. However, the accuracy of classic clinical and histological variables, including the Breslow thickness, presence of ulceration, and lymph node status, might not be sufficient to identify such individuals. Thus, there is a need for the development of additional prognostic melanoma biomarkers that can improve early attempts to stratify melanoma patients and reliably identify high-risk subgroups with the aim of providing effective personalized therapies.

METHODS

In our current work, we discuss and assess emerging primary melanoma tumor biomarkers and prognostic circulating biomarkers.

RESULTS

Several promising biomarkers show prognostic value (eg, exosomal MIA (ie, melanoma inhibitory activity), serum S100B, AMLo signatures, and mRNA signatures); however, the scarcity of reliable data precludes the use of these biomarkers in current clinical applications.

CONCLUSION

Further research is needed on several promising biomarkers for melanoma. Large-scale studies are warranted to facilitate the clinical translation of prognostic biomarker applications for melanoma in personalized medicine.

Prognostic models to predict survival in patients with pancreatic cancer: a systematic review

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) has poor survival. Current treatments offer little likelihood of cure or long-term survival. This systematic review evaluates prognostic models predicting overall survival in patients diagnosed with PDAC.

METHODS

We conducted a comprehensive search of eight electronic databases from their date of inception through to December 2019. Studies that published models predicting survival in patients with PDAC were identified.

RESULTS

3297 studies were identified; 187 full-text articles were retrieved and 54 studies of 49 unique prognostic models were included. Of these, 28 (57.1%) were conducted in patients with advanced disease, 17 (34.7%) with resectable disease, and four (8.2%) in all patients. 34 (69.4%) models were validated, and 35 (71.4%) reported model discrimination, with only five models reporting values >0.70 in both derivation and validation cohorts. Many (n = 27) had a moderate to high risk of bias and most (n = 33) were developed using retrospective data. No variables were unanimously found to be predictive of survival when included in more than one study.

CONCLUSION

Most prognostic models were developed using retrospective data and performed poorly. Future research should validate instruments performing well locally in international cohorts and investigate other potential predictors of survival.

A new prognostic nomogram for heterotopic ossification formation after elbow trauma : the Shanghai post-Traumatic Elbow Heterotopic Ossification Prediction (STEHOP) model

AIMS

Heterotopic ossification (HO) is a common complication after elbow trauma and can cause severe upper limb disability. Although multiple prognostic factors have been reported to be associated with the development of post-traumatic HO, no model has yet been able to combine these predictors more succinctly to convey prognostic information and medical measures to patients. Therefore, this study aimed to identify prognostic factors leading to the formation of HO after surgery for elbow trauma, and to establish and validate a nomogram to predict the probability of HO formation in such particular injuries.

METHODS

This multicentre case-control study comprised 200 patients with post-traumatic elbow HO and 229 patients who had elbow trauma but without HO formation between July 2019 and December 2020. Features possibly associated with HO formation were obtained. The least absolute shrinkage and selection operator regression model was used to optimize feature selection. Multivariable logistic regression analysis was applied to build the new nomogram: the Shanghai post-Traumatic Elbow Heterotopic Ossification Prediction model (STEHOP). STEHOP was validated by concordance index (C-index) and calibration plot. Internal validation was conducted using bootstrapping validation.

RESULTS

Male sex, obesity, open wound, dislocations, late definitive surgical treatment, and lack of use of non-steroidal anti-inflammatory drugs were identified as adverse predictors and incorporated to construct the STEHOP model. It displayed good discrimination with a C-index of 0.80 (95% confidence interval 0.75 to 0.84). A high C-index value of 0.77 could still be reached in the internal validation. The calibration plot showed good agreement between nomogram prediction and observed outcomes.

CONCLUSION

The newly developed STEHOP model is a valid and convenient instrument to predict HO formation after surgery for elbow trauma. It could assist clinicians in counselling patients regarding treatment expectations and therapeutic choices. Cite this article: Bone Joint J 2022;104-B(8):963-971.

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.

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.

MelRisk: Using neutrophil-to-lymphocyte ratio to improve risk prediction models for metastatic cutaneous melanoma in the sentinel lymph node

BACKGROUND

Identifying metastatic melanoma in the sentinel lymph node (SLN) is important because 80% of SLN biopsies are negative and 11% of patients develop complications. The neutrophil-to-lymphocyte ratio (NLR), a biomarker of micrometastatic disease, could improve prediction models for SLN status. We externally validated existing models and developed 'MelRisk' prognostic score to better predict SLN metastasis.

METHODS

The models were externally validated using data from a multicenter cohort study of 1,251 adults. Additionally, we developed and internally validated a new prognostic score `MelRisk', using candidate predictors derived from the extant literature.

RESULTS

The Karakousis model had a C-statistic of 0.58 (95% CI, 0.54-0.62). The Sondak model had a C-statistic of 0.57 (95% CI 0.53-0.61). The MIA model had a C-statistic of 0.60 (95% CI. 0.56-0.64). Our 'MelRisk' model (which used Breslow thickness, ulceration, age, anatomical site, and the NLR) showed an adjusted C-statistic of 0.63 (95% CI, 0.56-0.64).

CONCLUSION

Our prediction tool is freely available in the Google Play Store and Apple App Store, and we invite colleagues to externally validate its performance .

Machine learning directed sentinel lymph node biopsy in cutaneous head and neck melanoma

BACKGROUND

The specificity of sentinel lymph node biopsy (SLNB) for detecting lymph node metastasis in head and neck melanoma (HNM) is low under current National Comprehensive Cancer Network (NCCN) treatment guidelines.

METHODS

Multiple machine learning (ML) algorithms were developed to identify HNM patients at very low risk of occult nodal metastasis using National Cancer Database (NCDB) data from 8466 clinically node negative HNM patients who underwent SLNB. SLNB performance under NCCN guidelines and ML algorithm recommendations was compared on independent test data from the NCDB (n = 2117) and an academic medical center (n = 96).

RESULTS

The top-performing ML algorithm (AUC = 0.734) recommendations obtained significantly higher specificity compared to the NCCN guidelines in both internal (25.8% vs. 11.3%, p < 0.001) and external test populations (30.1% vs. 7.1%, p < 0.001), while achieving sensitivity >97%.

CONCLUSION

Machine learning can identify clinically node negative HNM patients at very low risk of nodal metastasis, who may not benefit from SLNB.

Breslow thickness 2.0: Why gene expression profiling is a step toward better patient selection for sentinel lymph node biopsies

Risk-stratification of cutaneous melanoma is important. Patients want to know what to expect after diagnosis, and physicians need to decide on a treatment plan. Historically, melanoma that had spread beyond the skin and regional lymph nodes was largely incurable, and the only approach to preventing a bad outcome was surgery. Through the seminal work of Alexander Breslow and Donald Morton, a system was devised to carefully escalate surgery based on primary tumor thickness and sentinel lymph node status. Today, we know that prophylactic lymph node dissections do not improve survival, but we continue to appreciate the prognostic implications of a positive sentinel node and the benefits of removing nodal metastases, which facilitates locoregional disease control. However, the question arises whether we can better select patients for sentinel lymph node biopsies (SLNB) as, currently, 85% of these procedures are negative and non-therapeutic. Here, we argue that gene expression profiling (GEP) of the diagnostic biopsy is a valuable step toward better patient selection when combined with reliable clinicopathologic (CP) information such as patient age and Breslow thickness. Recently, a CP-GEP-based classifier of nodal metastasis risk, the Merlin Assay, has become commercially available. While CP-GEP is still being validated in prospective studies, preliminary data suggest that it is an independent predictor of nodal metastasis, outperforming clinicopathological variables. The hunt is on for Breslow thickness 2.0.

Validation of Four Cutaneous Squamous Cell Carcinoma Staging Systems Using Nationwide Data

Background

Cutaneous squamous cell carcinoma (cSCC) is the second most common cancer worldwide with relatively low metastatic potential (2–5%). Developments in therapeutic options have highlighted the need to better identify high‐risk patients who could benefit from closer surveillance, adjuvant therapies and baseline/follow‐up imaging, while at the same time safely omitting low‐risk patients from further follow‐up. Controversy remains regarding the predictive performance of current cSCC staging systems and which methodology to adopt.

Objectives

To validate the performance of four cSCC staging systems [American Joint Committee on Cancer 8th edition (AJCC8), Brigham and Women’s Hospital (BWH), Tübingen and Salamanca T3 refinement] in predicting metastasis using a nationwide cohort.

Methods

A nested case–control study using data from the National Disease Registration Service, England, 2013–2015 was conducted. Metastatic cSCC cases were identified using an algorithm to identify all potential cases for manual review. These were 1 : 1 matched on follow‐up time to nonmetastatic controls randomly selected from 2013. Staging systems were analysed for distinctiveness, homogeneity, monotonicity, specificity, positive predictive value (PPV), negative predictive value (NPV) and c‐index.

Results

We included 887 metastatic cSCC cases and 887 nonmetastatic cSCC controls. The BWH system showed the highest specificity [92.8%, 95% confidence interval (CI) 90.8–94.3%, PPV (13.2%, 95% CI 10.6–16.2) and c‐index (0.84, 95% CI 0.82–0.86). The AJCC8 showed superior NPV (99.2%, 95% CI 99.2–99.3), homogeneity and monotonicity compared with the BWH and Tübingen diameter and thickness classifications (P < 0.001). Salamanca refinement did not show any improvement in AJCC8 T3 cSCC staging.

Conclusions

We validated four cSCC staging systems using the largest nationwide dataset of metastatic cSCC so far. Although the BWH system showed the highest overall discriminative ability, PPV was low for all staging systems, which shows the need for further improvement and refining of current cSCC staging systems.

Bioinformatic and Machine Learning Applications in Melanoma Risk Assessment and Prognosis: A Literature Review

Over 100,000 people are diagnosed with cutaneous melanoma each year in the United States. Despite recent advancements in metastatic melanoma treatment, such as immunotherapy, there are still over 7000 melanoma-related deaths each year. Melanoma is a highly heterogenous disease, and many underlying genetic drivers have been identified since the introduction of next-generation sequencing. Despite clinical staging guidelines, the prognosis of metastatic melanoma is variable and difficult to predict. Bioinformatic and machine learning analyses relying on genetic, clinical, and histopathologic inputs have been increasingly used to risk stratify melanoma patients with high accuracy. This literature review summarizes the key genetic drivers of melanoma and recent applications of bioinformatic and machine learning models in the risk stratification of melanoma patients. A robustly validated risk stratification tool can potentially guide the physician management of melanoma patients and ultimately improve patient outcomes.

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.

Integrating 31-Gene Expression Profiling With Clinicopathologic Features to Optimize Cutaneous Melanoma Sentinel Lymph Node Metastasis Prediction

National guidelines recommend sentinel lymph node biopsy (SLNB) be offered to patients with > 10% likelihood of sentinel lymph node (SLN) positivity. On the other hand, guidelines do not recommend SLNB for patients with T1a tumors without high-risk features who have < 5% likelihood of a positive SLN. However, the decision to perform SLNB is less certain for patients with higher-risk T1 melanomas in which a positive node is expected 5%-10% of the time. We hypothesized that integrating clinicopathologic features with the 31-gene expression profile (31-GEP) score using advanced artificial intelligence techniques would provide more precise SLN risk prediction.

Contemporary management of locoregionally advanced melanoma in Australia and New Zealand and the role of adjuvant systemic therapy

Australia and New Zealand have the highest incidence and mortality rates for melanoma in the world. Local surgery is still the standard treatment of primary cutaneous melanoma, and it is therefore important that surgeons understand the optimal care pathways for patients with melanoma. Accurate staging is critical to ensure a reliable assessment of prognosis and to guide treatment selection. Sentinel node biopsy (SNB) plays an important role in staging and the provision of reliable prognostic estimates for patients with cutaneous melanoma. Patients with stage III melanoma have a substantial risk of disease recurrence following surgery, leading to poor long-term outcomes. Systemic immunotherapies and targeted therapies, known to be effective for stage IV melanoma, have now also been shown to be effective as adjuvant post-surgical treatments for resected stage III melanoma. These patients should be made aware of this and preferably managed in an integrated multidisciplinary model of care, involving the surgeon, medical oncologists and radiation oncologists. This review considers the impact of a recent update to the American Joint Committee on Cancer (AJCC) staging system, the role of SNB for patients with high-risk primary melanoma and recent advances in adjuvant systemic therapies for high-risk patients.

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.

Predicting sentinel node positivity in patients with melanoma: external validation of a risk-prediction calculator (the Melanoma Institute Australia nomogram) using a large European population-based patient cohort

BACKGROUND

A nomogram to predict sentinel node (SN) positivity [the Melanoma Institute Australia (MIA) nomogram] was recently developed and externally validated using two large single-institution databases. However, there remains a need to further validate the nomogram's performance using population-based data.

OBJECTIVES

To perform further validation of the nomogram using a European national patient cohort.

METHODS

Patients with cutaneous melanoma who underwent SN biopsy in the Netherlands between 2000 and 2014 were included. Their data were obtained from the Dutch Pathology Registry. The predictive performance of the nomogram was assessed by discrimination (C-statistic) and calibration. Negative predictive values (NPVs) were calculated at various predicted probability cutoffs.

RESULTS

Of the 3049 patients who met the eligibility criteria, 23% (691) were SN positive. Validation of the MIA nomogram (including the parameters Breslow thickness, ulceration, age, melanoma subtype and lymphovascular invasion) showed a good C-statistic of 0·69 (95% confidence interval 0·66-0·71) with excellent calibration (R² = 0·985, P = 0·40). The NPV of 90·1%, found at a 10% predicted probability cutoff for having a positive SN biopsy, implied that by using the nomogram, a 16·3% reduction in the rate of performing an SN biopsy could be achieved with an error rate of 1·6%. Validation of the MIA nomogram considering mitotic rate as present or absent showed a C-statistic of 0·70 (95% confidence interval 0·68-0·74).

CONCLUSIONS

This population-based validation study in European patients with melanoma confirmed the value of the MIA nomogram in predicting SN positivity. Its use will spare low-risk patients the inconvenience, cost and potential risks of SN biopsy while ensuring that high-risk patients are still identified.

Co-expression Analysis of Genes and Tumor-Infiltrating Immune Cells in Metastatic Uterine Carcinosarcoma

Uterine carcinosarcoma (UCS) is a malignant tumor with a high tendency to invasion and metastasis. However, the underlying invasion and metastasis mechanisms of UCS remain poorly understood. Genetic alteration and tumor-infiltrating immune cells play important roles in tumorigenesis, progression, and metastasis. To better understand the underlying mechanisms of UCS, we screened tumor-infiltrating immune cells by applying CIBERSORT algorithm and constructed nomograms to predict the prognosis of UCS patients based on metastasis-specific tumor-infiltrating immune cells and genes, and demonstrated their utility by the high AUC values. Combining gene co-expression and experimental validation results, we propose a potential mechanism of AK8, MPZ, and mast cells activated might play important parts in UCS metastasis.

Molecular Biomarkers for Melanoma Screening, Diagnosis and Prognosis: Current State and Future Prospects

Despite significant progress in the development of treatment options, melanoma remains a leading cause of death due to skin cancer. Advances in our understanding of the genetic, transcriptomic, and morphologic spectrum of benign and malignant melanocytic neoplasia have enabled the field to propose biomarkers with potential diagnostic, prognostic, and predictive value. While these proposed biomarkers have the potential to improve clinical decision making at multiple critical intervention points, most remain unvalidated. Clinical validation of even the most commonly assessed biomarkers will require substantial resources, including limited clinical specimens. It is therefore important to consider the properties that constitute a relevant and clinically-useful biomarker-based test prior to engaging in large validation studies. In this review article we adapt an established framework for determining minimally-useful biomarker test characteristics, and apply this framework to a discussion of currently used and proposed biomarkers designed to aid melanoma detection, staging, prognosis, and choice of treatment.

Development and validation of a Brazilian nomogram to assess sentinel node biopsy positivity in melanoma

BACKGROUND

Although well-established, sentinel node biopsy (SNB) for melanoma is not free from controversies and sometimes it can be questionable if SNB should be considered even for patients who meet the criteria for the procedure. Mathematical tools such as nomograms can be helpful and give more precise answers for both clinicians and patients. We present a nomogram for SNB positivity that has been internally validated.

METHODS

Retrospective analysis of patients who underwent SNB from 2000 to 2015 in a single institution. Single logistic regressions were used to identify variables that were associated to SNB positivity. All variables with a p value < 0.05 were included in the final model. Overall performance, calibration, and discriminatory power of the final multiple logistic regression model were all assessed. Internal validation of the multiple logistic regression model was performed via bootstrap analysis based on 1000 replications.

RESULTS

Site of primary lesion, Breslow thickness, mitotic rate, histologic regression, lymphatic invasion, and Clark level were statistically related to SNB positivity. After internal validation, a good performance was observed as well as an adequate power of discrimination (area under the curve 0.751).

CONCLUSIONS

We have presented a nomogram that can be helpful and easily used in daily practice for assessing SNB positivity.