Quantifying Nocturnal Scratch in Atopic Dermatitis: A Machine Learning Approach Using Digital Wrist Actigraphy.

Nocturnal scratching substantially impairs the quality of life in individuals with skin conditions such as atopic dermatitis (AD). Current clinical measurements of scratch rely on patient-reported outcomes (PROs) on itch over the last 24 h. Such measurements lack objectivity and sensitivity. Digital health technologies (DHTs), such as wearable sensors, have been widely used to capture behaviors in clinical and real-world settings. In this work, we develop and validate a machine learning algorithm using wrist-wearing actigraphy that could objectively quantify nocturnal scratching events, therefore facilitating accurate assessment of disease progression, treatment effectiveness, and overall quality of life in AD patients. A total of seven subjects were enrolled in a study to generate data overnight in an inpatient setting. Several machine learning models were developed, and their performance was compared. Results demonstrated that the best-performing model achieved the F1 score of 0.45 on the test set, accompanied by a precision of 0.44 and a recall of 0.46. Upon satisfactory performance with an expanded subject pool, our automatic scratch detection algorithm holds the potential for objectively assessing sleep quality and disease state in AD patients. This advancement promises to inform and refine therapeutic strategies for individuals with AD.

Machine learning driven index of tumor multinucleation correlates with survival and suppressed anti-tumor immunity in head and neck squamous cell carcinoma patients.

OBJECTIVES: Matching treatment intensity to tumor biology is critical to precision oncology for head and neck squamous cell carcinoma (HNSCC) patients. We sought to identify biological features of tumor cell multinucleation, previously shown by us to correlate with survival in oropharyngeal (OP) SCC using a machine learning approach. MATERIALS AND METHODS: Hematoxylin and eosin images from an institutional OPSCC cohort formed the training set (DTr). TCGA HNSCC patients (oral cavity, oropharynx and larynx/hypopharynx) formed the validation set (DV). Deep learning models were trained in DTr to calculate a multinucleation index (MuNI) score. Gene set enrichment analysis (GSEA) was then used to explore correlations between MuNI and tumor biology. RESULTS: MuNI correlated with overall survival. A multivariable nomogram that included MuNI, age, race, sex, T/N stage, and smoking status yielded a C-index of 0.65, and MuNI was prognostic of overall survival (2.25, 1.07-4.71, 0.03), independent of the other variables. High MuNI scores correlated with depletion of effector immunocyte subsets across all HNSCC sites independent of HPV and TP53 mutational status although the correlations were strongest in wild-type TP53 tumors potentially due to aberrant mitotic events and activation of DNA-repair mechanisms. CONCLUSION: MuNI is associated with survival in HNSCC across subsites. This may be driven by an association between high levels of multinucleation and a suppressive (potentially exhausted) tumor immune microenvironment. Mechanistic studies examining the link between multinucleation and tumor immunity will be required to characterize biological drivers of multinucleation and their impact on treatment response and outcomes.

CT radiomic signature predicts survival and chemotherapy benefit in stage I and II HPV-associated oropharyngeal carcinoma.

Chemoradiation is a common therapeutic regimen for human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC). However, not all patients benefit from chemotherapy, especially patients with low-risk characteristics. We aim to develop and validate a prognostic and predictive radiomic image signature (pRiS) to inform survival and chemotherapy benefit using computed tomography (CT) scans from 491 stage I and II HPV-associated OPSCC, which were divided into three cohorts D1-D3. The prognostic performance of pRiS was evaluated on two test sets (D2, n = 162; D3, n = 269) using concordance index. Patients from D2 and D3 who received either radiotherapy alone or chemoradiation were used to validate pRiS as predictive of added benefit of chemotherapy. Seven features were selected to construct pRiS, which was found to be prognostic of overall survival (OS) on univariate analysis in D2 (hazard ratio [HR] = 2.14, 95% confidence interval [CI], 1.1-4.16, p = 0.02) and D3 (HR = 2.74, 95% CI, 1.34-5.62, p = 0.006). Chemotherapy was associated with improved OS for high-pRiS patients in D2 (radiation vs chemoradiation, HR = 4.47, 95% CI, 1.73-11.6, p = 0.002) and D3 (radiation vs chemoradiation, HR = 2.99, 95% CI, 1.04-8.63, p = 0.04). In contrast, chemotherapy did not improve OS for low-pRiS patients, which indicates these patients did not derive additional benefit from chemotherapy and could be considered for treatment de-escalation. The proposed radiomic signature was prognostic of patient survival and informed benefit from chemotherapy for stage I and II HPV-associated OPSCC patients.

Radiomic Features Associated With HPV Status on Pretreatment Computed Tomography in Oropharyngeal Squamous Cell Carcinoma Inform Clinical Prognosis.

PURPOSE: There is a lack of biomarkers for accurately prognosticating outcome in both human papillomavirus-related (HPV+) and tobacco- and alcohol-related (HPV-) oropharyngeal squamous cell carcinoma (OPSCC). The aims of this study were to i) develop and evaluate radiomic features within (intratumoral) and around tumor (peritumoral) on CT scans to predict HPV status; ii) investigate the prognostic value of the radiomic features for both HPV- and HPV+ patients, including within individual AJCC eighth edition-defined stage groups; and iii) develop and evaluate a clinicopathologic imaging nomogram involving radiomic, clinical, and pathologic factors for disease-free survival (DFS) prediction for HPV+ patients. EXPERIMENTAL DESIGN: This retrospective study included 582 OPSCC patients, of which 462 were obtained from The Cancer Imaging Archive (TCIA) with available tumor segmentation and 120 were from Cleveland Clinic Foundation (CCF, denoted as SCCF) with HPV+ OPSCC. We subdivided the TCIA cohort into training (ST, 180 patients) and validation (SV, 282 patients) based on an approximately 3:5 ratio for HPV status prediction. The top 15 radiomic features that were associated with HPV status were selected by the minimum redundancy-maximum relevance (MRMR) using ST and evaluated on SV. Using 3 of these 15 top HPV status-associated features, we created radiomic risk scores for both HPV+ (RRSHPV+) and HPV- patients (RRSHPV-) through a Cox regression model to predict DFS. RRSHPV+ was further externally validated on SCCF. Nomograms for the HPV+ population (Mp+RRS) were constructed. Both RRSHPV+ and Mp+RRS were used to prognosticate DFS for the AJCC eighth edition-defined stage I, stage II, and stage III patients separately. RESULTS: RRSHPV+ was prognostic for DFS for i) the whole HPV+ population [hazard ratio (HR) = 1.97, 95% confidence interval (CI): 1.35-2.88, p < 0.001], ii) the AJCC eighth stage I population (HR = 1.99, 95% CI: 1.04-3.83, p = 0.039), and iii) the AJCC eighth stage II population (HR = 3.61, 95% CI: 1.71-7.62, p < 0.001). HPV+ nomogram Mp+RRS (C-index, 0.59; 95% CI: 0.54-0.65) was also prognostic of DFS (HR = 1.86, 95% CI: 1.27-2.71, p = 0.001). CONCLUSION: CT-based radiomic signatures are associated with both HPV status and DFS in OPSCC patients. With additional validation, the radiomic signature and its corresponding nomogram could potentially be used for identifying HPV+ OPSCC patients who might be candidates for therapy deintensification.