Symptom burden and symptom clusters in ovarian cancer patients during first-line maintenance therapy: a cross-sectional survey.

Primary progressive aphasia (PPA) is a clinical syndrome in which speech and language decline is the earliest and most prominent symptom, with relative preservation of other cognitive domains in the early course. PPA comprises 3 consensus-defined variants with distinct linguistic and neuroanatomical profiles as follows: nonfluent or agrammatic PPA, characterized by effortful, agrammatic speech and left frontal-insular atrophy; semantic variant PPA, marked by fluent but semantically impoverished speech and left-predominant anterior temporal degeneration; and logopenic variant PPA, defined by impaired word retrieval and repetition with left temporoparietal atrophy. Accurate variant classification informs prognosis and underlying pathology and is increasingly relevant for emerging disease-modifying treatments. However, diagnosis remains challenging in routine practice, particularly in early or mixed presentations, and access to expert speech-language assessment is limited in many clinical settings. Connected speech offers a rich window into the multidimensional speech and language deficits in PPA. Compared with domain-specific tasks (eg, naming, repetition), brief narrative samples capture lexical retrieval, semantic specificity, syntactic complexity, fluency, and prosody within minutes. Manual analyses have shown that connected speech can reliably differentiate PPA variants, and automated transcription and speech-analysis tools now enable scalable extraction of linguistic and acoustic features. Beyond reproducing established markers, automated approaches can quantify a broader feature space and detect subtle patterns difficult to assess reliably by hand or have been underemphasized in traditional batteries. Prior semiautomated and automated studies—often in modest samples—demonstrate that combinations of lexical, syntactic, semantic, and acoustic measures can distinguish PPA variants and controls. Yet individual features overlap across variants, and highly multivariate or end-to-end models may lack clinical interpretability. There remains a need for approaches that treat speech as an integrated multidomain behavior while yielding concise, transparent speech profiles that align with established clinical constructs.