Glioblastoma is an aggressive central nervous system tumor characterized by diffuse infiltration. Despite substantial advances in oncology, survival outcomes have shown little improvement over the past three decades. Radiotherapy remains a cornerstone of treatment; however, it faces several challenges, including considerable inter-observer variability in clinical target volume delineation, dose constraints associated with adjacent organs at risk, and the persistently poor prognosis of affected patients. Recent advances in artificial intelligence, particularly deep learning, have shown promise in automating radiation therapy mapping to improve consistency, accuracy, and efficiency. This narrative review explores current auto segmentation frameworks, dose mapping, and biologically informed radiotherapy planning guided by multimodal imaging and mathematical modeling. Studies have demonstrated reproducible tumor segmentations with DSCs exceeding 0.90, reduced planning within minutes, and emerging predictive capabilities for treatment response. Radiogenomic integration has enabled imaging-based classification of critical biomarkers with high accuracy, reinforcing the potential of deep learning models in personalized radiotherapy. Despite these innovations, deployment into clinical practice remains limited, primarily due to insufficient external validation and single-institution training datasets. This review emphasizes the importance of large, annotated imaging datasets, multi-institutional collaboration, and biologically explainable modeling to successfully translate deep learning into glioblastoma radiation planning and longitudinal monitoring.

Purpose: Trastuzumab deruxtecan (T-DXd) has improved outcomes in metastatic breast cancer; however, a substantial subset of patients experience early lack of clinical benefit that is not reliably predicted by routine clinicopathologic variables. In an institutional cohort of 109 T-DXd–treated metastatic lesions, HER2 expression level, estrogen receptor (ER)/ progesterone receptor (PR) status, Ki-67 index, and metastatic site were not significantly associated with response, highlighting the need to define mechanistic determinants of intrinsic resistance. Experimental Design: We performed spatial transcriptomic and proteomic profiling using NanoString GeoMx Digital Spatial Profiling on pretreatment bone, brain, and soft-tissue metastases from patients with clinical benefit (response) versus early progression (resistance) on T-DXd. Tumor (pan-cytokeratin+) and immune (CD45+) compartments were analyzed to link tumor architecture and region-resolved signaling states with therapeutic response. Candidate resistance pathways were functionally evaluated in HER2-positive breast cancer cell lines and in vivo metastasis models treated with T-DXd alone or combined with the RAGE inhibitor TTP488. Results: Spatial proteogenomics revealed recurrent upregulation of S100 family alarmins in resistant tumor regions, associated with activation of a RAGE-centered prosurvival signaling program characterized by ERK, AKT, and STAT3 phosphorylation. Pharmacologic RAGE inhibition enhanced T-DXd–induced apoptosis, restored drug sensitivity in vitro, and significantly reduced metastatic burden in lung and brain metastasis models. Conclusions: Spatial proteogenomics identifies a conserved S100-RAGE–driven survival state coupled to immune-excluded tumor architecture as a mechanism of intrinsic T-DXd resistance across metastatic niches. Targeting this pathway with an orally available RAGE antagonist restores T-DXd responsiveness and offers an immediately translatable strategy to overcome resistance in metastatic breast cancer.

Glioblastoma is an aggressive central nervous system tumor characterized by diffuse infiltration. Despite substantial advances in oncology, survival outcomes have shown little improvement over the past three decades. Radiotherapy remains a cornerstone of treatment; however, it faces several challenges, including considerable inter-observer variability in clinical target volume delineation, dose constraints associated with adjacent organs at risk, and the persistently poor prognosis of affected patients. Recent advances in artificial intelligence, particularly deep learning, have shown promise in automating radiation therapy mapping to improve consistency, accuracy, and efficiency. This narrative review explores current auto segmentation frameworks, dose mapping, and biologically informed radiotherapy planning guided by multimodal imaging and mathematical modeling. Studies have demonstrated reproducible tumor segmentations with DSCs exceeding 0.90, reduced planning within minutes, and emerging predictive capabilities for treatment response. Radiogenomic integration has enabled imaging-based classification of critical biomarkers with high accuracy, reinforcing the potential of deep learning models in personalized radiotherapy. Despite these innovations, deployment into clinical practice remains limited, primarily due to insufficient external validation and single-institution training datasets. This review emphasizes the importance of large, annotated imaging datasets, multi-institutional collaboration, and biologically explainable modeling to successfully translate deep learning into glioblastoma radiation planning and longitudinal monitoring.

Purpose:Trastuzumab deruxtecan (T-DXd) has improved outcomes in metastatic breast cancer; however, a substantial subset of patients experience early lack of clinical benefit that is not reliably predicted by routine clinicopathologic variables. In an institutional cohort of 109 T-DXd–treated metastatic lesions, HER2 expression level, estrogen receptor (ER)/ progesterone receptor (PR) status, Ki-67 index, and metastatic site were not significantly associated with response, highlighting the need to define mechanistic determinants of intrinsic resistance. Experimental Design:We performed spatial transcriptomic and proteomic profiling using NanoString GeoMx Digital Spatial Profiling on pretreatment bone, brain, and soft-tissue metastases from patients with clinical benefit (response) versus early progression (resistance) on T-DXd. Tumor (pan-cytokeratin+) and immune (CD45+) compartments were analyzed to link tumor architecture and region-resolved signaling states with therapeutic response. Candidate resistance pathways were functionally evaluated in HER2-positive breast cancer cell lines and in vivo metastasis models treated with T-DXd alone or combined with the RAGE inhibitor TTP488. Results:Spatial proteogenomics revealed recurrent upregulation of S100 family alarmins in resistant tumor regions, associated with activation of a RAGE-centered prosurvival signaling program characterized by ERK, AKT, and STAT3 phosphorylation. Pharmacologic RAGE inhibition enhanced T-DXd–induced apoptosis, restored drug sensitivity in vitro, and significantly reduced metastatic burden in lung and brain metastasis models. Conclusions:Spatial proteogenomics identifies a conserved S100-RAGE–driven survival state coupled to immune-excluded tumor architecture as a mechanism of intrinsic T-DXd resistance across metastatic niches. Targeting this pathway with an orally available RAGE antagonist restores T-DXd responsiveness and offers an immediately translatable strategy to overcome resistance in metastatic breast cancer.