Re-evaluation of Sentinel Lymph Node Biopsy for Melanoma

Morrison, S.; Han, Dale

doi:10.1007/s11864-021-00819-2

Cited by 20 publications

(22 citation statements)

References 107 publications

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“…Furthermore, increasing TIL levels were correlated with a decreasing positive SLN rate, with the lowest SLN metastasis rate found in patients with B TILs. In addition, this study confirms prior studies reporting the predictive value of age, gender, thickness, ulceration, LVI, MS, and primary site in predicting SLN positivity 26‐32 . In light of the strong association between TIL status and SLN status, TIL status may potentially be considered when patients are being selected for SLNB because patients with A TILs may be at higher risk for SLN metastases, whereas patients with thinner melanomas that have B TILs may have a lower risk for SLN disease; however, this would need further evaluation.…”

Section: Discussionsupporting

confidence: 82%

Is the presence of tumor‐infiltrating lymphocytes predictive of outcomes in patients with melanoma?

Morrison

Han

Elenwa

et al. 2022

Cancer

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Background The significance of tumor‐infiltrating lymphocytes (TILs) in melanoma is debated. This article presents a multicenter, retrospective study assessing the predictive and prognostic value of TILs. Methods The Sentinel Lymph Node Working Group database was queried from 1993 to 2018 for cases with known TIL data. TILs were categorized as absent or present, which included nonbrisk (NB), brisk (B), and present but unspecified TIL levels. Clinicopathologic factors were correlated with TILs, sentinel lymph node (SLN) status, and melanoma‐specific survival (MSS). Results Overall, 3203 patients were included. The median thickness was 1.5 mm, and 469 cases had SLN metastases. TILs were present in 2458 cases (76.7%), with NB, B, and unspecified TILs seen in 1691 (68.8%), 691 (28.1%), and 76 (3.1%), respectively. Multivariable analysis showed that the presence of TILs significantly predicted a negative SLN biopsy (P < .05). The median follow‐up was 25.2 months. MSS was significantly better for cases with TILs than cases without TILs (P < .001). According to multivariable analysis, age, gender, thickness, mitotic rate, ulceration, lymphovascular invasion, and SLN status were significantly prognostic of MSS (all P values < .05). Although TILs were not prognostic of MSS, when multiple imputation was used and the SLN status was excluded, the presence of TILs was significantly prognostic of improved MSS (hazard ratio, 0.78; 95% confidence interval, 0.64‐0.95; P = .0154). Conclusions TILs are a favorable marker because their presence significantly predicts a negative SLN, and the absence of TILs may be a prognostic marker of worse survival in patients with a positive SLN but not a negative SLN. TILs may also serve as a prognostic marker of survival when the SLN status is not considered.

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Section: Discussionsupporting

confidence: 82%

Is the presence of tumor‐infiltrating lymphocytes predictive of outcomes in patients with melanoma?

Morrison

Han

Elenwa

et al. 2022

Cancer

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“…36 A test to increase the accuracy of predicting a positive SLNB that complements NCCN guidelines could help patients and physicians when considering an SLNB. 37 Our data show that integrating CP features with the continuous 31-GEP score, which provides insight into intrinsic tumor biology, accurately predicts the likelihood of having a positive SLN. Although the continuous 31-GEP score has the biggest impact on the algorithm, a major strength of the i31-GEP is that it incorporates many routinely recorded CP features, including Breslow thickness, MR, ulceration, and age into the algorithm.…”

Section: Discussionmentioning

confidence: 67%

“…Furthermore, there is no consensus about which CP variables are prognostic for SLN positivity outside of staging factors, eg tumor thickness and ulceration status. [36][37][38] Many patients with T4 melanomas may be eligible for adjuvant therapy regardless of SLN status, and it is unclear if these patients should undergo the procedure. 36 A test to increase the accuracy of predicting a positive SLNB that complements NCCN guidelines could help patients and physicians when considering an SLNB.…”

Section: Discussionmentioning

confidence: 99%

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

Whitman

Koshenkov

Gastman

et al. 2021

JCO Precision Oncology

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PURPOSE 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. METHODS An integrated 31-GEP (i31-GEP) neural network algorithm incorporating clinicopathologic features with the continuous 31-GEP score was developed using a previously reported patient cohort (n = 1,398) and validated using an independent cohort (n = 1,674). RESULTS Compared with other covariates in the i31-GEP, the continuous 31-GEP score had the largest likelihood ratio (G2 = 91.3, P < .001) for predicting SLN positivity. The i31-GEP demonstrated high concordance between predicted and observed SLN positivity rates (linear regression slope = 0.999). The i31-GEP increased the percentage of patients with T1-T4 tumors predicted to have < 5% SLN-positive likelihood from 8.5% to 27.7% with a negative predictive value of 98%. Importantly, for patients with T1 tumors originally classified with a likelihood of SLN positivity of 5%-10%, the i31-GEP reclassified 63% of cases as having < 5% or > 10% likelihood of positive SLN, for a more precise, personalized, and clinically actionable SLN-positive likelihood estimate. CONCLUSION These data suggest the i31-GEP could reduce the number of SLNBs performed by identifying patients with likelihood under the 5% threshold for performance of SLNB and improve the yield of positive SLNBs by identifying patients more likely to have a positive SLNB.

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“…В настоящее время расширенная лимфодиссекция как вариант ведения пациентов с регионарными метастазами меланомы остается одной из линий выбора, наряду с динамическим наблюдением, адьювантной и таргетной терапией. Цель биопсии СЛУ в значительной степени сместилась с терапевтической процедуры к прогностической [6], однако по-прежнему продолжает играть решающую роль в ведении пациентов с меланомой, обеспечивая стратификацию риска, потенциальный региональный контроль заболевания и дополнительные варианты лечения для пациентов с положительным СЛУ [7].…”

Section: рефератunclassified

“…В настоящее время идентификация СЛУ возможна посредством различных методов визуализации с применением флуорес цирую щих красителей, радиофармпрепаратов, ферромагенетиками (NIR/ICGфлуоресцентный метод, гамма-NIR, гамма-УЗ-гибридные методы [8], черенковское излучение, ОФЭКТ/КТ, МРТ с наночастицами оксида железа [7] и др. ).…”

Section: рефератunclassified

The Efficacy of Radionuclide Imaging Techniques at Sentinel Node Identification in Patients with Cutaneous Melanoma

Abdulova¹,

Крылов

Рыжков³

et al. 2021

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ЯдернаЯ медицинаЭффективность методов радионуклидной визуализации сторожевого лимфоузла у пациентов с меланомой кожи л.Ю. абдулова 1 , а.с. крылов 1 , а.д. рыжков 1,2 , е.а. николаева 1 , т.в. захарова 1 , м.е. Билик 1 , к.а. Барышников 1

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Re-evaluation of Sentinel Lymph Node Biopsy for Melanoma

Cited by 20 publications

References 107 publications

Is the presence of tumor‐infiltrating lymphocytes predictive of outcomes in patients with melanoma?

Is the presence of tumor‐infiltrating lymphocytes predictive of outcomes in patients with melanoma?

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

The Efficacy of Radionuclide Imaging Techniques at Sentinel Node Identification in Patients with Cutaneous Melanoma

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