Oral minoxidil for late alopecia in cancer survivors.

PURPOSE: Late alopecia, defined as incomplete hair regrowth > 6 months following cytotoxic chemotherapy or > 6 months from initiation of endocrine therapy, negatively impacts quality of life and may affect dose intensity of adjuvant therapy. This study investigates the effect of oral minoxidil in women with chemotherapy and/or endocrine therapy-induced late alopecia. METHODS: The rate of clinical response was assessed by standardized photography and quantitated with trichoscopy. RESULTS: Two hundred and sixteen patients (mean age 57.8 ± 13.7) were included. The most common cancer diagnosis was breast, in 170 patients (79.1%). Alopecia developed after chemotherapy in 31 (14.4%) patients, endocrine monotherapy in 65 (30.1%) patients, and chemotherapy followed by endocrine therapy in 120 (55.6%) patients. In 119 patients, standardized photography assessments were used to determine clinical change in alopecia after a median of 105 (IQR = 70) days on oral minoxidil and revealed improvement in 88 (74%) patients. Forty-two patients received quantitative trichoscopic assessments at baseline and at follow-up after a median of 91 (IQR = 126) days on oral minoxidil. Patients had clinically and statistically significant increases in frontal hair shaft density (from 124.2 hairs/cm2 at initial to 153.2 hairs/cm2 at follow-up assessment, p = 0.008) and occipital shaft density (from 100.3 hairs/cm2 at initial to 123.5 hairs/cm2 at follow-up assessment. p = 0.004). No patients discontinued oral minoxidil due to adverse events. CONCLUSIONS: Overall, oral minoxidil was well tolerated by patients and may benefit both frontal and occipital late alopecia in cancer survivors treated with cytotoxic and/or endocrine therapy by increasing hair shaft and follicle density.

Validating the Reliability and Clinical Relevance of a Nasolabial Fold Photonumeric Scale.

BACKGROUND: The use of tissue fillers to treat age-related deepening of the nasolabial fold (NLF) has increased and become the standard clinical approach, creating a need for evidence-based, objective evaluation for pre- and post-procedure assessment of the NLF. METHODS: A 5-point rating scale was developed to assess the NLF, specifically the presence of depression and shadowing. Live validation of the scale was performed with a total of 73 participants representing the full range of NLF severities. Physicians board-certified in a core aesthetic specialty (3 trained raters) performed the scale validation over 2 rounds, 2 weeks apart. Training was carried out, and test-retest reliability was quantitated through the determination of intra- and inter-rater reliability by percentage of agreement, weighted kappa statistic with 95% confidence interval (CI), and intraclass correlation coefficient with 95% CI. To evaluate the clinical relevance of a 1-grade difference, rater assessments of 90 photo pairs were compared with previous designations of clinically different or not clinically different. RESULTS: The NLF scale achieved near-perfect intra- and inter-rater reliability when utilized by trained raters to assess a diverse group of live participants. Furthermore, clinically relevant differences between grades were established, and a 1-point difference was detectable by trained evaluators using the NLF scale. CONCLUSION: The clinically relevant and highly reliable validated NLF scale provides a standardized grading system with a user-friendly design for objectively assessing NLF in clinical practice and as a research tool for clinical approval studies of new aesthetic products and technologies. J Drugs Dermatol. 2024;23(1):1284-1291.   doi:10.36849/JDD.7316.

Validating the Reliability and Clinical Relevance of an Infraorbital Hollow Photonumeric Scale.

Background: Infraorbital hollowing is a facial aesthetic issue for which a broad age range of patients seek treatment. Expanding treatment options for this region warrants the development of validated tools to objectively assess infraorbital hollow (IOH) severity before and after treatment. Objectives: To validate a 4-point rating scale to assess depression of IOH, depression relative to the mid-pupillary line, and visibility of the lateral orbital rim. Methods: The IOH scale described herein was developed and subjected to live validation with a total of 73 patients representing the full range of IOH severities. Scale validation was performed by board-certified plastic surgeons and dermatologists (3 raters) over 2 rounds, 2 weeks apart. Intrarater and interrater reliabilities were used to demonstrate test-retest reliability as quantitated with percentage of agreement, weighted kappa statistic with 95% confidence interval (CI), and intraclass correlation coefficient with 95% CI. The clinical relevance of a 1-grade difference was evaluated by comparing rater assessments of 77 photo pairs with their previously determined designation as "clinically different" or "not clinically different." Results: The IOH scale demonstrated substantial to near-perfect intrarater and interrater reliabilities when utilized by trained raters to assess a diverse group of live patients. Furthermore, clinically relevant differences between grades were established, and detection of a 1-point difference could be achieved by trained evaluators using the IOH scale. Conclusions: This highly reliable, clinically relevant, and validated IOH scale provides a user-friendly, standardized grading system to objectively evaluate and track changes in infraorbital hollowing in clinical practice and research.

Development and qualification of a machine learning algorithm for automated hair counting.

OBJECTIVE: Determining the amount of hair on the scalp has always been an important metric of patient satisfaction for hair growth and hair retention technologies. While simple in concept, this measurement is a difficult, resource intensive task for the dermatologist and the research scientist. Specifically, counting and measuring hair in phototrichogram images is very time consuming and labour intensive. Due to cost, often only a fraction of available images is manually analysed. There is a need for an automated method that can significantly increase speed and throughput while reducing the cost of counting and measuring hair in phototrichogram images. METHODS: Recent advances in machine learning and deep convolutional neural networks (deep learning) have led to a revolution in the analysis of image, video, speech, text and other sensor data. Image diagnostics have seen remarkable improvements with completely automated methods outperforming both human experts and human-engineered analysis methods. Deep learning methods can also provide speed and cost benefits. To enable use of a deep learning, we created a data set of 288 manually annotated phototrichogram images with marked location and length of each hair (the training dataset). We designed a custom neural network architecture and custom image processing algorithms to best utilize the available training data and to maximize performance for hair counting and length measurement. The performance of the algorithm was qualified by comparing hair count and length measurements to an independent ground truth method, the semi-manual Canfield's Hair Metrix method. RESULTS: Leveraging deep neural networks, we have developed capability to apply machine learning to reduce the time needed to acquire data from phototrichograms of patients' scalp from months to seconds. Our algorithm enables fast and fully automated hair counting and length measurement. The algorithm shows high agreement with human manually assisted analysis (ground truth). CONCLUSIONS: We have trained and deployed an algorithm utilizing this technology and have demonstrated the reproducibility, accuracy and speed of this algorithm that, once deployed, requires little to no recurring cost or manual intervention for its operation. The method allows fast analysis of large number of images, reducing study cost and significantly reducing study analysis time.