Bradley Callas

Artificial intelligence (AI) is now applied across oncology, including imaging, pathology, therapy selection, toxicity assessment, and survival modeling, and large language models (LLMs) have been adopted particularly quickly. Reported physician use of AI roughly doubled between 2023 and 2026. The frameworks used to evaluate these tools have not advanced at the same rate. Many are available, but each was designed for a single stage of the AI lifecycle, and none directly answers the question most relevant to a treating clinician: whether a given tool is appropriate for the individual patient under care. We reviewed the frameworks in current use, including TRIPOD+AI, PROBAST+AI, CLAIM, SPIRIT-AI, CONSORT-AI, DECIDE-AI, MINIMAR, and CREMLS, together with the oncology-specific ESMO EBAI and the newer LLM-specific guidance (TRIPOD-LLM, MI-CLEAR-LLM, CHART, and ESMO ELCAP). Each addresses part of the evaluation problem, but none is sufficient on its own for oncology, where the standard of care changes rapidly, assays drift, biomarker-defined subgroups are small, and a model validated in one period may perform poorly in the next. LLMs introduce additional challenges, including sensitivity to prompting, undisclosed vendor updates, behavioral drift, and hallucination rates approaching 50% on clinical quality-assurance tasks. The predictive-model frameworks were not designed to capture these failure modes. The central argument of this review is that the frameworks the field requires already exist; what is missing is their mandatory adoption. We propose that journals and regulators move from recommending these frameworks to requiring their use, and we outline a cancer-aware evaluation pathway together with the specific responsibilities of authors, reviewers, journals, and regulators in implementing it.

Background/Objectives: Primary spinal sarcomas, encompassing both bone and soft tissue histotypes, demand individualized reconstruction due to heterogeneous tumor biology, anatomic complexity, and host environments compromised by radiation, systemic therapy, or prior surgery. This narrative review reframes post-resection spinal reconstruction through a precision-medicine lens. Methods: A structured literature review was performed using PubMed and Scopus, targeting articles published between 2000 and 2026. Searches encompassed spinal sarcoma reconstruction, radiation and fusion, biologic reconstruction, and emerging technologies. Results: Tumor grade, radiation exposure, and systemic therapy timing emerge as multiplicative determinants of reconstructive environment quality, with drug-class-specific perioperative effects warranting stratified management. Vascularized bone grafts achieve reliable fusion in compromised hosts where avascular constructs fail. A precision-oriented reconstructive ladder is proposed as a conceptual, hypothesis-generating framework to guide strategy selection. Hybrid PSI-VBG constructs may further expand reconstructive possibilities. The evidence base remains largely composed of small, retrospective series. Conclusions: Individualized strategies anchored in tumor biology and host environment are the cornerstone of durable spinal sarcoma reconstructions. The proposed framework requires prospective, multi-institutional validation. Standardized outcome definitions, prospective registries, and histotype-stratified analyses are needed to advance the field.