Multiharmonic imaging-based automated recognition of cutaneous T-cell lymphoma

Cutaneous T-cell lymphomas (CTCLs) are a heterogeneous group of non-Hodgkin lymphomas, with mycosis fungoides (MF) being the most common type, accounting for approximately 60% of all lymphomas arising primarily in the skin. Diagnosis of MF is challenging, especially in its early stages when the number of atypical T lymphocytes is small, and clinical and histopathological changes are often nonspecific. This leads to significant delays of 3-5 years in diagnosis and treatment. Thus, novel diagnostic methods are needed to adjust the diagnostic and therapeutic strategies of CTCL. Nonlinear optical microscopy (NLOM) is promising for its sensitivity to specific tissue structures through harmonic generation and its ability to image in three dimensions.

To image haematoxylin and eosin-stained skin samples with NLOM and detect atypical epidermotropism and dermal cells in MF skin samples using an artificial intelligence (AI) model.

We used brightfield microscopy and NLOM to analyse haematoxylin and eosin-stained biopsy samples from MF skin lesions. Expert clinicians labelled the images, which were used to train a convolutional neural network to recognize skin lymphocytes. The model was applied to independent testing datasets obtained from both imaging modalities to assess its performance in detecting characteristic features of skin T lymphocytes. Additionally, NLOM was performed on fresh, unstained biopsy samples to highlight its potential for in vivo skin imaging.

NLOM successfully imaged epidermal and dermal structures in haematoxylin and eosin-stained MF tissue sections with subcellular resolution. The trained AI model detected lymphocyte epidermotropism and dermal infiltration in the images. Moreover, NLOM imaged fresh, unstained biopsies up to 400 µm deep through the epidermis to the dermis.

We demonstrate that NLOM, combined with AI, can detect lymphocyte epidermotropism and dermal infiltration in haematoxylin and eosin-stained MF skin tissue. This approach offers dermatologists a powerful tool to improve the diagnosis and prognosis of MF, paving the way for more timely and precise therapeutic strategies. An author video to accompany this article is available online.