Robotic-Assisted Pelvic Lymphadenectomy for Metastatic Melanoma Results in Durable Oncologic Outcomes

Robot-Assisted Pelvic Dissection for Enlarged Lymph Nodes in Melanoma Improves Recovery with Equivalent Oncological Outcomes to Open Pelvic Dissection

BACKGROUND

Robot-assisted pelvic lymph node dissection (rPLND) has been reported in heterogenous groups of patients with melanoma, including macroscopic or at-high-risk-for microscopic metastasis. With changing indications for surgery in melanoma, and availability of effective systemic therapies, pelvic dissection is now performed for clinically detected bulky lymph node metastasis followed by adjuvant drug therapy. rPLND has not been compared with open pelvic lymph node dissection (oPLND) for modern practice.

METHODS

All patients undergoing pelvic node dissection for macroscopic melanoma at a single institution were reviewed as a cohort, observational study.

RESULTS

Twenty-two pelvic lymph node dissections were identified (8 oPLND; 14 rPLND). The number of pelvic lymph nodes removed was similar (median oPLND 6.5 (interquartile range [IQR] 6.0-12.5] versus rPLND 6.0 [3.75-9.0]), with frequent matted nodes (11/22, 50.0%). Operative time (median oPLND 130 min [IQR 95.5-182] versus rPLND 126 min [IQR 97.8-160]) and complications (Clavien-Dindo scale) were similar. Length of hospital stay (median 5.34 days (IQR 3.77-6.94) versus 1.98 days (IQR 1.39-3.50) and time to postoperative adjuvant therapy (median 11.6 weeks [IQR 10.6-18.5] versus 7.71 weeks [IQR 6.29-10.4]) were shorter in the rPLND group. No differences in pelvic lymph node recurrence (p = 0.984), distant metastatic recurrence (p = 0.678), or melanoma-specific survival (p = 0.655) were seen (median follow-up 21.1 months [rPLND] and 25.7 months [oPLND]).

CONCLUSIONS

rPLND is an effective way to remove bulky pelvic lymph nodes in melanoma, with a shorter recovery and reduced interval to initiating adjuvant therapy compared with oPLND. This group of patients may especially benefit from neoadjuvant systemic approaches to management.

Selection of patients with malignant melanoma for pelvic lymph node dissection (PLND) using CT-PET

OBJECTIVES

In melanoma patients with positive pelvic nodes, pelvic lymph node dissection (PLND) to achieve fully resected disease status facilitates adjuvant-dose systemic therapy and avoids higher treatment doses with greater toxicity. This study aimed to test the accuracy of prediction of nodal involvement of the 2010 joint BAPRAS/BAD guidance compared with Positron Emission Tomography (CT-PET).

METHODS

A retrospective review was undertaken of 26 melanoma patients undergoing PLND by a single surgeon between July 2012 and July 2020. The indications for performing PLND were in accordance with the 2010 guidance, but this was supplemented by CT-PET in 16/26 patients.

RESULTS

Of the 26 patients undergoing PLND, 10 underwent surgery based upon the 2010 criteria alone and 16 underwent supplementary CT-PET. 17 patients had positive nodes on histology; of these, 13 had a positive CT-PET. Amongst node-negative patients, only one had a false positive CT-PET. CT-PET was 100% sensitive for pelvic nodal disease and 75% specific, with a positive predictive value for nodal involvement of 92%. Of the 10 patients who underwent PLND without CT-PET, only 4 had positive nodes while 6 patients had negative nodes.

CONCLUSIONS

The 2010 guidelines remain broad and contributed to negative PLND in a third of our patients (9/26). Hence, the indications for performing PLND need to be revisited. Our series supports PET-CT as being 100% sensitive in the identification of pelvic nodal disease and 75% specific. We recommend that a positive PET-CT should be considered as the primary indication for PLND in melanoma patients.

Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends

BACKGROUND

Thanks to the rapid development of computer-based systems and deep-learning-based algorithms, artificial intelligence (AI) has long been integrated into the healthcare field. AI is also particularly helpful in image recognition, surgical assistance and basic research. Due to the unique nature of dermatology, AI-aided dermatological diagnosis based on image recognition has become a modern focus and future trend. Key scientific concepts of review: The use of 3D imaging systems allows clinicians to screen and label skin pigmented lesions and distributed disorders, which can provide an objective assessment and image documentation of lesion sites. Dermatoscopes combined with intelligent software help the dermatologist to easily correlate each close-up image with the corresponding marked lesion in the 3D body map. In addition, AI in the field of prosthetics can assist in the rehabilitation of patients and help to restore limb function after amputation in patients with skin tumors.

THE AIM OF THE STUDY

For the benefit of patients, dermatologists have an obligation to explore the opportunities, risks and limitations of AI applications. This study focuses on the application of emerging AI in dermatology to aid clinical diagnosis and treatment, analyzes the current state of the field and summarizes its future trends and prospects so as to help dermatologists realize the impact of new technological innovations on traditional practices so that they can embrace and use AI-based medical approaches more quickly.