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Mature, germinal centre-bearing tertiary lymphoid structures (TLS) and mature lymph node follicles (secondary lymphoid structures, SLS) sustain the plasma cell response that shapes outcome and immunotherapy benefit in lung cancer. How this loco-regional axis differs between neoadjuvant-treated and therapy-naive tumors is poorly defined. We analyzed H&E whole-slide images of resected tumors and paired proximal and distal lymph nodes from matched neoadjuvant-treated (neoLUSC, n=20) and therapy-naive (naivLUSC, n=20) lung squamous cell carcinoma (LUSC) patients. Aignostics Atlas H&E Tissue Analysis (HETA) models quantified cell densities per tissue compartment and cell-cell co-localization; a dedicated deep-learning model validated against pathologist annotations detected TLS and SLS and graded their maturity. Prognostic value was tested in an independent cohort (n=458; 46 treated). In neoLUSC tumors, stromal plasma cell density was 72% (p=0.004) and stromal lymphocyte density 48% (p=0.015) lower than in naivLUSC; macrophages and granulocytes did not differ significantly. Total and immature TLS did not differ, whereas mature TLS in the tumor (p=0.034) and mature SLS in the tumor-distal lymph node (p=0.003) were lower in neoLUSC patients. Peri-TLS plasma cell density correlated with tumor lymphocyte density in naivLUSC (rho=0.54, p=0.017) but not in neoLUSC tumors (rho=0.07). In the validation cohort, stromal plasma cell density (HR 0.69, p=0.011) and lymphocyte-plasma cell co-localization (HR 0.66, p=0.004) were associated with longer survival, and neoadjuvant status with shorter survival (HR 1.50, p=0.026). Neoadjuvant therapy is thus associated with fewer mature lymphoid structures and weaker coupling of the humoral compartment to the tumor rather than uniformly lower immune readouts. The stromal plasma cell/co-localization axis is a candidate prognostic biomarker.

Multimodal large language models (MLLMs) excel at visual interpretation but fail on spatial reasoning tasks that humans solve reliably. Existing benchmarks evaluate these models as black boxes, limiting their ability to identify the underlying causes of lower performance: when a model fails a spatial reasoning task, it remains difficult to ascertain whether the hurdle is perceptual, such as recognizing object boundaries, or cognitive, such as reasoning about occlusion to infer hidden geometry. We introduce Spatial-IQ, a hierarchical diagnostic framework that decomposes object counting in stacked 3D structures into 9 perceptual and cognitive sub-tasks organized by the developmental stages of human spatial cognition, with mental rotation as an additional target probe. Using NVIDIA Isaac Sim, we procedurally generated a diverse dataset of roughly 80,000 stacked 3D structures with per-task ground truth. We evaluate models across three output formats (free-response text, multiple-choice images, and image editing) alongside a human baseline. The Spatial-IQ framework shows that top-performing models often succeed at the target task (object counting) without succeeding on the lower-level sub-tasks intended to support it, and that models differ in how much of these hierarchical chains they preserve, often revealing shortcut behavior that raw target-task accuracy alone would obscure. Finally, we demonstrate that training models with chain-of-thought (CoT) supervision over our hierarchical sub-tasks, combined with reinforcement learning with verifiable rewards, significantly improves both spatial consistency across sub-tasks and target-task accuracy, supporting the value of the proposed decomposition as both a diagnostic tool and a training signal.