Psychosocial Status and Self-Perception in Patients with Cleft Lip and/or Palate.

OBJECTIVE: To describe how the psychosocial status of patients with cleft lip and/or palate (CL/P) relates to patient-reported outcomes (PROs). DESIGN: Cross-sectional retrospective chart review. SETTING: Tertiary care pediatric hospital. PATIENTS/PARTICIPANTS: Patients aged 8 to 29 years attending cleft team evaluations during a 1-year period. MAIN OUTCOME MEASURES: CLEFT-Q. RESULTS: Patients (N = 158) with isolated or syndromic CL/P and mean age 13.4 ± 3.0 years were included. Fifteen (9%) patients had siblings who also had CL/P. Of 104 patients who met with the team psychologist, psychosocial concerns were identified in 49 (47%) patients, including 25 (24%) with Attention-Deficit/Hyperactivity Disorder or behavior concerns, 28 (27%) with anxiety, and 14 (13%) with depression or mood concerns. Younger age and having siblings with cleft were associated with better PROs, while psychosocial concerns were associated with worse PROs on Speech, Psychosocial, and Face Appearance scales. CONCLUSIONS: Patient perception of cleft outcomes is linked to psychosocial factors.

Long-Term Patient-Reported Outcomes in Internationally Adopted Children with Cleft Lip and Palate.

OBJECTIVE: To compare patient-reported outcomes (PROs) in internationally adopted patients with cleft lip and palate to those in non-adopted peers. DESIGN: Cross-sectional study. SETTING: Multidisciplinary cleft team at tertiary care hospital. PATIENTS: Patients aged ≥ 8 with cleft lip and palate attending routine cleft team evaluations September 2021 - September 2022. MAIN OUTCOME MEASURE: CLEFT-Q PRO scores. RESULTS: Sixty-four internationally adopted patients and 113 non-adopted patients with a mean age of 13 years were included. Compared to non-adopted peers, adopted patients demonstrated worse satisfaction with face appearance (mean 59 vs. 66, p = .044), speech function (mean 69 vs. 78, p = .005), and speech distress (mean 80 vs. 84, p = .032). No significant differences were observed on the nose, nostrils, teeth, lips, lip scar, jaws, psychological function, or social function scales (p > .05). Objective clinical evaluation corroborated these findings, with adopted patients demonstrating worse Pittsburgh Weighted Speech scores (mean 3.0 vs 1.9, p = .027) and greater incidence of articulation errors (64% vs 46%, p = .021). No significant differences were observed in rates of mood, anxiety, or behavior concerns identified on psychosocial assessment (p = .764). Among adopted patients, undergoing palatoplasty prior to adoption was associated with worse satisfaction with speech, appearance, school, and social function (p < .05). CONCLUSIONS: Patient-reported outcomes among internationally adopted adolescents and young adults with cleft lip and palate show slightly lower satisfaction with facial appearance and speech but otherwise demonstrate similar results to non-adopted peers on most appearance and psychosocial measures. PRO data correlated well with objective speech assessment and did not portend worse psychosocial function.

Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review.

BACKGROUND: Improved breast cancer risk assessment models are needed to enable personalized screening strategies that achieve better harm-to-benefit ratio based on earlier detection and better breast cancer outcomes than existing screening guidelines. Computational mammographic phenotypes have demonstrated a promising role in breast cancer risk prediction. With the recent exponential growth of computational efficiency, the artificial intelligence (AI) revolution, driven by the introduction of deep learning, has expanded the utility of imaging in predictive models. Consequently, AI-based imaging-derived data has led to some of the most promising tools for precision breast cancer screening. MAIN BODY: This review aims to synthesize the current state-of-the-art applications of AI in mammographic phenotyping of breast cancer risk. We discuss the fundamentals of AI and explore the computing advancements that have made AI-based image analysis essential in refining breast cancer risk assessment. Specifically, we discuss the use of data derived from digital mammography as well as digital breast tomosynthesis. Different aspects of breast cancer risk assessment are targeted including (a) robust and reproducible evaluations of breast density, a well-established breast cancer risk factor, (b) assessment of a woman's inherent breast cancer risk, and (c) identification of women who are likely to be diagnosed with breast cancers after a negative or routine screen due to masking or the rapid and aggressive growth of a tumor. Lastly, we discuss AI challenges unique to the computational analysis of mammographic imaging as well as future directions for this promising research field. CONCLUSIONS: We provide a useful reference for AI researchers investigating image-based breast cancer risk assessment while indicating key priorities and challenges that, if properly addressed, could accelerate the implementation of AI-assisted risk stratification to future refine and individualize breast cancer screening strategies.