Lymphedema is chronic swelling from lymphatic dysfunction. The vascular delay phenomenon is used in flap reconstruction to increase flap viability through staged selective partial disruption of blood supply to enhance flap survival, followed by flap transfer 7–10 days later. A similar corollary for lymphatics remains unknown. The purpose of this study was to evaluate whether lymphatic preconditioning with staged disruption of lymphatics in the murine hindlimb lymphedema model can be protective against lymphedema. C57BL/6 mice underwent 20 Gy hindlimb irradiation. Group 1 (immediate lymphatic disruption) underwent a circumferential skin incision, complete lymphatic disruption, and popliteal lymph node excision in a single-stage operation. Group 2 (delayed lymphatic disruption) underwent a semi-circumferential inguinal skin incision and dominant lymphatic transection and, 7 days later, received a circumferential skin incision, complete lymphatic transection, and popliteal lymphadenectomy. Paw thickness was measured weekly. Near-infrared laser lymphangiography and immunohistochemistry were performed on D35. Group 2 had a lower average paw thickness (p = 0.0008) and greater lymphatic clearance signal intensity at 24 h (p = 0.0035). Immunohistochemistry indicated an abundance of the lymphatic endothelial-specific marker podoplanin in Group 2 compared to Group 1 (p = 0.03). This study demonstrates evidence for a novel concept of a lymphatic delay phenomenon parallel to the well-known vascular delay phenomenon.

BACKGROUND Breast cancer-related lymphedema (BCRL) develops in 16.6% of patients after breast cancer treatment. Translational cancer laboratories study BCRL in murine tail and hindlimb lymphedema models. Manual counting and measurement of the histologic images these models produce is tedious, observer dependent, and unreliable when image quality varies. Artificial intelligence (AI) may quantify these images more objectively, but laboratories differ in the technical resources available to adopt it. OBJECTIVE This study aimed to describe 2 complementary categories of AI integration for translational cancer laboratories and to provide an example from our BCRL laboratory. METHODS The first category was annotation-driven, no-code software (HALO AI and Imaris), in which investigator annotations train the software to batch process images without programming. The second was custom-coded machine learning and deep learning, in which models learn image features from labeled images of normal and lymphedematous tissue. As a practical example, epidermal nuclei were counted in 3 hematoxylin and eosin-stained skin specimens from a murine tail lymphedema model by a custom-coded classifier (software trained on investigator-marked nuclei), by HALO AI, and manually. Manual counts were estimated by counting 1 region of each image and multiplying by the number of equivalent regions. RESULTS Nuclear counts differed between methods: the custom classifier counted 1,477, 1,544, and 3,830 nuclei in the 3 specimens, HALO AI counted 2,406, 3,700, and 3,586, and manual counts were 2,700, 4,000, and 2,650. HALO AI counts ranged from 89.1% to 135.3% of manual counts. Custom classifier counts ranged from 38.6% to 144.5%. CONCLUSIONS Annotation-driven no-code platforms and custom-coded machine learning workflows are complementary approaches that translational cancer laboratories can adopt according to their technical resources. AI-assisted counts require validation against investigator measurements before use in definitive quantitative conclusions. AI-assisted image analysis may improve the objectivity, reproducibility, and scalability of translational BCRL research.