Published online Sep 20, 2026. doi: 10.5662/wjm.121456
Revised: April 28, 2026
Accepted: May 20, 2026
Published online: September 20, 2026
Processing time: 107 Days and 16.4 Hours
Gastrointestinal tumor-associated osteoporosis (GTO) is defined as secondary osteoporosis in patients with gastrointestinal tumors caused by tumorderived factors, chemotherapy, surgery, or malabsorption; it is an underrecognized but clinically significant complication that adversely affects skeletal health, treatment adherence, quality of life, and longterm prognosis. Its pathogenesis is multifactorial and involves a metabolic imbalance in the tumor microenvironment, antitumor therapy-related bone toxicity, and nutrient malabsorption caused by gastrointestinal dysfunction. These factors collectively increase the risk of skeletal-related events and worsen clinical outcomes. The aim of this review is to systematically evaluate the methodological quality, clinical validity, and translational evidence of cutting-edge technologies in GTO precision management and to clarify key methodological gaps and standardized directions for future research. In recent years, emerging technologies such as artificial intelligence (AI), nanotargeted drug delivery systems, and multiomics approaches have provided new opportunities for the precision management of GTO. This review summarizes their current applications in AI-assisted early screening and risk prediction, nanoenabled targeted bone protection, and multiomics-based mechanistic exploration of the tumor-bone-gut axis. It also discusses major barriers to clinical translation, including limited AI generalizability, nanomedicine safety and manufacturing challenges, difficulties in multidimensional data integration and standardization, imperfect multidisciplinary collaboration, and ethical concerns. Overall, these technologies are expected to drive the transition of GTO management from empirical practice to precision medicine and ultimately improve long-term patient outcomes.
Core Tip: Gastrointestinal tumor-associated osteoporosis (GTO) is an underrecognized complication that compromises quality of life, treatment adherence, and long-term prognosis. This review highlights how artificial intelligence (AI), bone-targeted nanodelivery, and multiomics are reshaping GTO management from empirical intervention to precision medicine. AI enables opportunistic screening and risk stratification, nanotechnology improves targeted bone protection, and multiomics reveals mechanisms across the tumor-bone-gut axis. The article also addresses key translational barriers, including limited model generalizability, nanomedicine safety, data standardization, and multidisciplinary coordination, and outlines future directions for integrated precision management.
- Citation: Wang P, Sun JK, Li D, Shi Z, Lu PY, Jin DF, Huang SF, Ruan YH, Li WT, Shi MD, Ma ZH, Wang ZH, Hu LY, Xue ME, Zhang CJ, Li ZP. Precision management of gastrointestinal tumor-associated osteoporosis driven by cutting-edge technologies: Current status, challenges, and future prospects. World J Methodol 2026; 16(3): 121456
- URL: https://www.wjgnet.com/2222-0682/full/v16/i3/121456.htm
- DOI: https://dx.doi.org/10.5662/wjm.121456
Gastrointestinal tumors (including gastric cancer, colorectal cancer, esophageal cancer, gastrointestinal neuroendocrine tumors, etc.) constitute a category of malignant tumors with persistently high incidence rates and mortality rates worldwide, posing a severe threat to human health[1]. According to GLOBOCAN 2022, there are more than 4.3 million new cases and more than 2.2 million deaths from gastrointestinal tumors (including gastric cancer, colorectal cancer, and esophageal cancer), accounting for 21.6% of new cancer cases and 22.6% of cancer deaths worldwide. As highly prevalent and highly lethal malignant tumors worldwide, gastrointestinal tumors show obvious disparities among regions and levels of economic development[2]. With the rapid development of precise antitumor therapies such as targeted therapy, immunotherapy, and laparoscopic minimally invasive surgery, the diagnosis and treatment outcomes of patients with gastrointestinal tumors have significantly improved, and their survival has greatly increased[3-5]. However, during long-term survival, the harm caused by the tumor itself and treatment-related complications has become increasingly prominent, representing an important factor affecting patients’ quality of life and prognosis[6]. Gastrointestinal tumor-associated osteoporosis (GTO) is defined as secondary osteoporosis in patients with gastrointestinal tumors caused by tumorderived factors, chemotherapy, surgery, or malabsorption; it is an underrecognized but clinically significant complication that adversely affects skeletal health, treatment adherence, quality of life, and longterm prognosis. Its pathogenesis is multifactorial and involves a metabolic imbalance in the tumor microenvironment (TME), antitumor therapy-related bone toxicity, and nutrient malabsorption caused by gastrointestinal dysfunction. These factors collectively increase the risk of skeletal-related events (SRE) and worsen clinical outcomes. Nevertheless, its incidence increases significantly with increasing patient survival, and it has become one of the key factors restricting the long-term quality of life of patients with gastrointestinal tumors[7,8]. High-risk patients include gastrectomy patients, patients receiving oxaliplatin, postmenopausal women, elderly patients, and vitamin D-deficient patients. Early intervention refers to bone risk assessment at the start of tumor treatment and preventive therapy before severe bone loss occurs. The incidence of GTO varies significantly according to the tumor type and treatment stage. Specifically, patients with gastric cancer undergo partial or total gastrectomy, leading to insufficient gastric acid secretion and impaired absorption of vitamin B12 and calcium. Consequently, the incidence of postoperative osteoporosis increases markedly, with 1-2 years after surgery being the period of rapid bone loss[9,10]. In patients with colorectal cancer, chemotherapy negatively affects bone metabolism, including direct inhibition of osteoblasts, resulting in a significantly increased incidence of osteoporosis; in particular, patients treated with oxaliplatin face an especially high risk of skeletal health impairment[11,12]. The harm of this complication is multidimensional: It not only causes persistent bone pain and reduced mobility but also significantly increases the risk of SREs. A prospective cohort study revealed that the incidence of SREs in gastrointestinal cancer patients with osteoporosis was 2.3 times greater than that in those without osteoporosis. Once severe SREs such as pathological fracture and spinal cord compression occur, the median survival of patients is shortened by 15-20 months, while medical costs increase by nearly 40%[13,14]. Emerging evidence indicates that the tumor itself can directly induce bone loss at an early stage. For example, Xu et al[15] provided key evidence that significant bone mineral density reduction can be observed in gastric cancer patients without bone metastasis. Further studies in animal models confirmed that gastric cancer cells might directly impair osteogenic function before bone metastasis by regulating the primary cilia of bone marrow mesenchymal stem cells (BMSCs) and dysregulating the Wnt/β-catenin signaling pathway. In addition, the chronic pain and limited mobility caused by GTO significantly reduce patients’ compliance with subsequent antitumor treatments. Some patients even interrupt critical treatments such as chemotherapy and targeted therapy due to intolerable pain, further accelerating disease progression[16,17]. Therefore, strengthening the early identification and precise management of GTO has important clinical significance and social value for improving long-term prognosis and quality of life and reducing the medical burden on patients with gastrointestinal tumors.
The traditional management model of GTO is centered on empirical intervention and lacks individualized and precise consideration. Its core procedures mainly include bone mineral density examination, screening of bone metabolic markers, and the application of conventional bone-protective agents, but many insurmountable limitations exist in clinical practice. In the early screening stage, the traditional method relies mainly on dual-energy X-ray absorptiometry for the quantitative detection of bone mineral density. Although this method is regarded as the gold standard for osteoporosis diagnosis, it also has obvious shortcomings: First, it involves low-dose radiation exposure and is not suitable for repeated testing in a short period[18]; second, patient compliance is low. Most patients focus on tumor follow-up and ignore bone health screening, resulting in approximately 60% of GTO patients not being identified early[19]. In the intervention stage, the application of traditional bone-protective agents (such as bisphosphonates and denosumab) is mostly based on unified clinical guideline standards, without fully considering individual differences such as tumor stage, pathological type, treatment regimen, genetic background, and nutritional status, leading to unsatisfactory therapeutic effects in some patients. For example, a large retrospective study of postmenopausal women with osteoporotic vertebral fractures revealed that the failure rate of bisphosphonate treatment was as high as 61.3%, which is much higher than previously recognized, revealing the serious limitations of the one-size-fits-all strategy[20]. Moreover, long-term use of traditional bone-protective agents is prone to adverse reactions. The long-term use of bisphosphonates increases the risk of atypical femoral fractures and osteonecrosis of the jaw, whereas denosumab may cause hypocalcemia[21,22]. In terms of mechanistic understanding, traditional studies have focused mostly on a single bone metabolism regulatory pathway (such as the RANKL-RANK-OPG system) and have failed to comprehensively elucidate the complex multisystem interaction network of “tumor-gastrointestinal tract-bone” involved in GTO. Key mechanisms, such as the regulatory effect of intestinal flora imbalance on bone metabolism and the paracrine effect of tumor-related inflammatory factors, have not been fully explored, limiting the development of precise intervention targets[23-25].
The proposal and practice of precision medicine have provided a new direction for innovation in GTO management. Its core goal is to achieve whole-process closed-loop management of “precision screening–precision evaluation–precision intervention-precision monitoring” on the basis of individual patient characteristics (including tumor biological characteristics, genetic background, bone metabolic status, lifestyle, etc.)[26]. In recent years, advanced technologies such as artificial intelligence (AI), nanotargeted drug delivery systems, and multiomics technologies (genomics, transcriptomics, metabolomics, microbiomics) have developed rapidly and have been increasingly applied in the medical field, providing key technical support for overcoming the limitations of the traditional GTO management model. Specifically, with its powerful data mining and analysis capabilities, AI can realize opportunistic screening of GTO based on routine images and greatly improve the efficiency of early diagnosis[27,28]. Nanotargeted drug delivery systems can achieve targeted delivery of bone-protective agents through precise targeting modification and intelligent response design, improving therapeutic effects while reducing systemic adverse reactions[29,30]. Multiomics technologies can be used to analyze the pathogenesis of GTO in detail and explore novel precise intervention targets at the molecular, cellular, microbial and other levels[31,32]. The deep integration of these advanced technologies with GTO management is expected to promote the transformation of GTO management from empirical to precise. Therefore, systematically reviewing the application status of advanced technologies in the precise management of GTO, deeply analyzing the challenges in technology application and clinical translation, and scientifically prospecting future development directions have important guiding significance for promoting clinical translation and scientific innovation in this field (Table 1). The overall framework of technology-enabled precision management for GTO is summarized in Figure 1.
| Items (evaluation indicators) | Traditional management mode | Precision management mode (empowered by cutting-edge technologies) |
| Core concept | Empirical intervention, one-size-fits-all strategy | Precision medicine, based on individual characteristics (tumor biology, genetic background, bone metabolism status, etc.) |
| Screening method | Mainly dual-energy X-ray absorptiometry | AI-based opportunistic screening (based on routine imaging) + bone metabolism marker detection |
| Screening efficiency | Low, about 60% of GTO patients not identified early | High, significantly improved early recognition rate |
| Therapeutic strategy | Routine bone protectors (bisphosphonates, denosumab) applied uniformly based on guidelines | Nanoparticle-targeted drug delivery system + personalized scheme (combined with multiomics results) |
| Consideration of individual differences | Ignored (tumor stage, pathological type, treatment plan, genetic background, nutritional status not fully considered) | Fully considered, tailored to individual patient characteristics |
| Therapeutic effect | Poor in some patients (61.3% failure rate of bisphosphonates in postmenopausal osteoporotic vertebral fracture patients) | Significantly improved, targeted delivery enhances efficacy |
| Adverse reactions | High risk (atypical femoral fractures, osteonecrosis of the jaw from bisphosphonates; hypocalcemia from denosumab) | Reduced systemic adverse reactions (targeted delivery reduces nontarget organ exposure) |
| Mechanism understanding | Focus on single bone metabolism pathway (e.g., RANKL-RANK-OPG system) | In-depth analysis via multiomics technologies, clarifying “tumor-gastrointestinal-bone” multisystem interaction network |
| Management process | Discontinuous (screening → intervention, no closed-loop monitoring) | Whole-process closed-loop management (precision screening → precision assessment → precision intervention → precision monitoring) |
| Impact on patient compliance | Low (due to poor therapeutic effect and adverse reactions) | High (improved efficacy, reduced side effects, and personalized intervention) |
| Impact on prognosis | Limited improvement, failure to effectively reduce SREs and prolong survival | Significantly improves long-term prognosis, reduces SREs incidence, and enhances quality of life |
| Supporting technologies | Conventional detection and drug delivery technologies | AI, nanoparticle-targeted drug delivery system, multiomics (genomics, transcriptomics, metabolomics, microbiomics), MDT collaboration |
To achieve this goal, literature searches were performed in PubMed, EMBASE, and Web of Science from January 2012 to December 2025 via keywords such as gastrointestinal cancer, osteoporosis, AI, nanotechnology, and multiomics; a total of 123 articles were included after screening. The present review aims to systematically summarize the technical methodologies, application effects, evidence levels, and clinical validation status of emerging tools in precision screening, targeted intervention, and mechanistic research of GTO; clarify methodological bottlenecks; unify key concepts; standardize evaluation frameworks; and provide methodologically rigorous guidance for clinical translation and standardized research, thereby offering actionable technical standards and directions for the multidisciplinary management of GTO.
AI imaging analysis and opportunistic screening: Opportunistic screening is an effective strategy to overcome the limitations of traditional GTO screening and improve the early detection rate. Its core design concept is to use routine computed tomography (CT) images obtained from tumor follow-up, physical examination, or diagnosis and treatment of other diseases to simultaneously complete bone mineral density assessment without additional examinations, radiation exposure, or medical costs, thus greatly improving screening accessibility and patient compliance[33,34]. With the powerful feature extraction capability of deep learning algorithms, AI imaging analysis technology can accurately mine subtle information in bone tissue from CT images, such as texture features, density distribution, and trabecular morphology, realizing quantitative evaluation of bone mineral density and graded diagnosis of osteoporosis[35]. AI-driven opportunistic screening may become an effective way to overcome the limitations of conventional GTO screening. Its core idea is to use routine CT images from tumor followup and other indications to perform simultaneous bone mineral density estimation via deep learning algorithms, with no extra examinations or costs. A multicenter validation study published in 2025 provided strong evidence. This study trained and validated an AI model for bone mineral density prediction via routine chest and abdominal CT data from 702 patients. The model achieved an area under the curve (AUC) of 0.822 for osteoporosis diagnosis, and its measurements were highly consistent with the quantitative CT gold standard (R² 0.88-0.96)[36]. The researchers clearly stated that this AI technique enables efficient and costeffective osteoporosis screening via routine CT, especially in resourcelimited settings. These results are from general osteoporosis populations and have not been specifically validated in GTO patients. However, extrapolation to gastrointestinal cancer patients needs further validation. In addition, multimodal AI image fusion technology further improves the accuracy of GTO screening and risk assessment by integrating the advantages of different imaging modalities. Traditional single CT images might only evaluate the density characteristics of bone tissue, whereas MRI might clearly display subtle changes in the bone marrow microenvironment and trabecular structure. The fusion of the two enables multidimensional evaluation of bone health status[37]. Multimodal AI image fusion further enhances the precision of GTO screening and risk stratification by combining complementary imaging information. Whereas conventional singlemodality CT mainly assesses bone mineral density, MRI noninvasively reveals subtle alterations in the bone marrow microenvironment and trabecular network. The combination of these data allows a more comprehensive characterization of bone health. A study by Küçükçiloğlu et al[38] provided direct evidence. The team developed a convolutional neural networkbased multimodal deep learning model that combined lumbar spine MRI and CT for osteoporosis prediction. The multimodal fusion model achieved a balanced accuracy of 98.90%, outperforming either modality alone, confirming the value of integrated multimodal information. These findings indicate that future AI models that combine CT density information and MRI structural information may not only accurately diagnose osteoporosis but also enable earlier and more precise risk prediction for fragility fractures and other SREs by comprehensively evaluating the cortex, trabecular network, and bone marrow status, greatly shortening the clinical workflow and improving efficiency.
Multidimensional data-driven risk prediction models: The occurrence of GTO is not caused by a single factor but rather results from the combined effects of tumor characteristics, treatment regimens, patient baseline status, nutritional status, and other factors. Traditional single-factor risk assessment models (such as those based only on age or therapeutic drugs) have difficulty accurately identifying high-risk patients. AI-based risk prediction models using multidimensional data might achieve accurate quantitative prediction of GTO risk through comprehensive analysis of integrated multisource information[39].
Researchers usually collect multidimensional data of gastrointestinal tumor patients from electronic medical records, laboratory information systems, picture archiving and communication systems, etc. These data include clinical baseline data (age, sex, body mass index, smoking, drinking, previous bone diseases), tumor characteristics (stage, pathological type, differentiation, metastasis), treatment-related data (chemotherapy drugs, dosage, course, targeted/immune therapy, surgical approach), laboratory data [bone turnover markers such as PINP and β-CTX, serum calcium, phosphorus, vitamin D, and inflammatory factors such as tumor necrosis factor-alpha (TNF-α) and interleukin (IL)-6], and imaging data (baseline bone mineral density and trabecular parameters). Machine learning algorithms such as random forests, gradient boosting decision trees, and support vector machines are then used to construct risk prediction models[40]. A study by Lin et al[41] provided methodological support for this approach. This study integrated multidimensional clinical data and systematically compared the performance of artificial neural networks, random forests, support vector machines, and logistic regression in predicting osteoporosis risk. The best model achieved an AUC of 0.731, confirming the good discriminative ability of machine learning models in osteoporosis-related risk stratification. This lays a solid methodological foundation for applying similar frameworks to GTO risk prediction.
Drawing on these successful experiences, the development of dedicated multidimensional GTO risk prediction models for gastrointestinal tumor patients has great potential. Through algorithmic analysis, the model can quantify the individualized risk of each patient at the initial stage of tumor treatment, thereby achieving two major clinical goals: First, timely initiation of primary prevention (such as enhanced nutritional supplementation and lifestyle intervention) for subclinical high-risk patients without significant bone loss; second, early warning and consideration of secondary prevention with bone-protective agents (such as bisphosphonates and denosumab) for patients with existing osteopenia and high treatment risk. Most current models are retrospective with small sample sizes. The evidence remains preliminary and requires prospective validation. Clinical practical guidance: For gastrointestinal tumor patients, baseline bone health screening should be performed at the initial diagnosis and before chemotherapy. High-risk groups (gastrectomy, oxaliplatin-based chemotherapy, postmenopausal women, elderly patients, vitamin D deficiency) should receive early intervention, including calcium and vitamin D supplementation and regular bone metabolism monitoring. Once osteopenia is identified, bone-protective agents should be considered to prevent further bone loss and SRE.
Although traditional bone-protective agents (such as bisphosphonates and denosumab) exert certain antiosteoporotic effects in GTO treatment, their lack of targeting leads to widespread distribution in vivo. This results in insufficient local drug concentrations in bone tissue, compromising drug efficacy, and drug accumulation in nontarget organs (liver, kidney, gastrointestinal tract), causing systemic adverse reactions. For example, oral bisphosphonates easily irritate the gastrointestinal mucosa, leading to nausea, vomiting, and gastric ulcers, while intravenous infusion may impair renal function[42]; long-term use of denosumab may increase the risk of hypocalcemia and infection[43]. Owing to their unique nanoscale effects and surface-modifiable properties, bone-targeted nanoparticulate delivery systems can achieve active targeted delivery by modifying bone-targeting ligands (such as alendronate, polyaspartic acid, osteocalcin antibodies, and arginine-glycine-aspartic acid peptides) on the nanoparticle surface. Drugs can be enriched in sites with abnormal bone metabolism (such as areas with active osteoclasts and around bone metastatic lesions), significantly increasing the local drug concentration while reducing nontarget organ exposure and adverse reactions[29,44]. In addition to the physicochemical modification of nanoparticles, cell-based drug delivery has emerged as a disruptive strategy. Chen et al[45] reported an innovative “bone lesion-homing” osteoprogenitor delivery system. Researchers have anchored drug-loaded nanoliposomes onto the surface of osteoprogenitor cells, which actively migrate to bone lesions guided by the CXCL12/CXCR4 axis, achieving ultrahigh-precision targeting of osteoporotic and osteosarcoma sites. In osteoporosis models, the system successfully delivers drugs and restores bone structure. This study demonstrated that cell-mediated delivery can overcome the physical barriers of traditional nanomedicines in terms of bone targeting and penetration, providing an inspiring new paradigm for next-generation GTO-targeted therapies. In interventions for gastrointestinal tumor-related bone toxicity (such as bone loss induced by chemotherapy and targeted therapy), the codelivery function of nanoparticulate systems has unique advantages, enabling simultaneous “tumor treatment and bone protection”. During chemotherapy and targeted therapy, antitumor drugs kill cancer cells but also inhibit osteoblasts and promote osteoclast proliferation, leading to rapid bone loss. Therefore, both tumor control and bone protection are needed. Accordingly, researchers have constructed codelivery nanosystems loaded with both bone-protective agents and antitumor drugs, achieving precise targeting of tumor cells and osteoclasts via dual-targeting ligand modification[46]. For example, Vanderburgh et al[46] developed bone-targeted nanoparticles composed of amphiphilic diblock copolymers, with alendronate as the bone-targeting ligand covalently modified on the hydrophilic block, loaded with GANT58, a small-molecule inhibitor of the Gli2 transcription factor. The system achieves active bone targeting through high-affinity binding between alendronate and bone hydroxyapatite, while optimized ligand density (10 mol%) balances systemic circulation and bone accumulation for efficient delivery to bone metastases.
In a mouse model of breast cancer bone metastasis, the nanoparticles exerted dual protective effects: On the one hand, GANT58 effectively inhibited parathyroid hormone-related protein (PTHrP) secretion from tumor cells, reducing bone-resorbing signals at the source; on the other hand, alendronate itself directly suppressed osteoclast activity. Compared with free drugs or nontargeted nanoparticles, the targeted system reduced the tumor-related bone lesion area by 3-fold and increased the tibial bone volume fraction by 2.5-fold while significantly reducing systemic exposure and potential toxicity due to precise delivery. These findings are based on breast cancer bone metastasis models and have not been directly validated in GTO patients. Further clinical verification in GTO populations is warranted before application in gastrointestinal cancer patients.
Smart responsive nanomaterials represent an advanced direction of bone-targeted delivery systems. The core purpose of these methods is to use pathophysiological differences between the bone microenvironment and normal tissues (such as pH, enzyme activity, oxidative stress, and temperature) as triggers to achieve “on-demand release”, further improving intervention precision and avoiding nonspecific release in normal tissues[47]. In GTO, the bone microenvironment exhibits significant pathological changes, such as local acidosis caused by active osteoclasts, high expression of matrix metalloproteinases induced by tumor-related inflammation, and elevated oxidative stress. These features provide specific targets for smart responsive nanomaterial design[48]. For example, the immune microenvironment at bone repair sites shows characteristic changes after injury, including local acidosis, elevated reactive oxygen species, and macrophage polarization toward a proinflammatory phenotype. On the basis of these features, researchers constructed a smart hydrogel system integrating near-infrared photothermal and pH responses. The system uses a gelatin methacryloyl/sodium alginate methacryloyl composite hydrogel as the carrier, internally integrating dopamine-coated deferoxamine-loaded black phosphorus nanosheets. As a photothermal agent, black phosphorus nanosheets endow the system with excellent photothermal conversion ability, while the dopamine coating and gel network jointly provide pH sensitivity, enabling the system to respond to both external near-infrared irradiation and the local acidic microenvironment for the on-demand release of therapeutic molecules (deferoxamine and phosphate ions). The composite hydrogel remains stable in vivo and produces mild photothermal effects under near-infrared irradiation at bone defects. This effect not only directly scavenges excessive reactive oxygen species but also drives local macrophage polarization from the proinflammatory M1 phenotype to the prorepair M2 phenotype, remodeling the inflammatory injury microenvironment into a regenerative immune microenvironment. Animal experiments confirmed that the system effectively promoted angiogenesis and osteogenesis, accelerating skull defect healing. This strategy goes beyond simple drug delivery and actively regulates the bone immune microenvironment through a combination of physical stimulation and a smart material response, providing an innovative approach for intervening in bone metabolic diseases by modulating immune homeostasis[49].
Genomics and transcriptomics provide powerful tools for dissecting genetic susceptibility, molecular regulatory mechanisms, and individual differences in GTO, promoting the transformation from “population-based intervention” to “individual precision intervention”. Genome-wide association studies (GWASs) can accurately identify genetic variants associated with GTO risk by scanning the genomes of large numbers of cases and controls[50]. Multiple GWASs and mechanistic studies have confirmed that functional polymorphisms of genes such as LRP5, WNT16, ESR1, and SOST are closely related to the risk of GTO in gastrointestinal tumor patients. Among them, the rs3736228 variant of the LRP5 gene is a widely validated risk marker[51]. The LRP5 protein, a key co-receptor of the Wnt/β-catenin pathway, forms a complex with Frizzled receptors to regulate osteoblast proliferation and differentiation. Activation of this pathway inhibits the β-catenin destruction complex, promotes β-catenin nuclear translocation, and initiates osteogenic target gene transcription. Studies have shown that the rs3736228 mutation significantly downregulates LRP5 expression, directly weakens Wnt/β-catenin activity, reduces osteoblast proliferation and mineralization, and increases GTO risk by 1.8-fold. This effect is amplified in the gastrointestinal TME, where tumor-related inflammatory factors inhibit the Wnt pathway and synergize with variations in LRP5 to accelerate bone loss[52]. In addition, ESR1 polymorphisms are specifically associated with risk in female gastrointestinal tumor patients. Estrogen receptor α inhibits osteoclast activity by regulating the RANKL-RANK-OPG axis. ESR1 variants reduce receptor-estrogen binding efficiency, impair bone protection, and significantly increase osteoporosis susceptibility, as supported by meta-analyses of osteoporosis genetics[53,54]. Transcriptomic studies using RNA-seq systematically compare gene expression profiles in bone, tumor, and bone marrow microenvironments between gastrointestinal cancer patients with and without osteoporosis, successfully identifying core differentially expressed genes and revealing multipathway cross-regulatory molecular networks[55]. For example, a transcriptomic analysis of bone tissue from colorectal cancer patients with osteoporosis revealed significant upregulation of proinflammatory and bone resorption-related genes such as TNF-α, IL-6, PTHrP, and RANKL and downregulation of key osteogenic genes such as Osterix, Runx2, and COL1A1. Further functional enrichment confirmed that these genes are enriched mainly in the NF-κB, TNF-α, and Wnt signaling pathways. Abnormal activation of NF-κB directly promotes osteoclast precursor differentiation and maturation, aggravating bone resorption, whereas inhibition of Wnt/β-catenin further weakens osteogenesis, resulting in an imbalance of “increased resorption-decreased formation”. These findings clarify the molecular mechanisms of GTO and provide clear targets for precision intervention. Targeted inhibitors against TNF-α and IL-6 can simultaneously block tumor-related inflammation and osteoclast activation, representing promising novel candidates for GTO treatment.
Gastrointestinal tumor-induced intestinal dysbiosis is an important pathological factor leading to GTO, with reciprocal regulation through the “gut-bone axis”. Metabolomics can accurately identify changes in gut microbial metabolites, providing key clues for dissecting the regulatory mechanism of the gut–bone axis[56]. Gastrointestinal tumors and treatments such as surgery and chemotherapy damage intestinal mucosal barrier integrity and cause dysbiosis, which is characterized by decreased beneficial bacteria and increased harmful bacteria[10,57]. Multiple studies have reported significantly reduced Bifidobacterium, Lactobacillus, and Akkermansia and increased Escherichia coli, Proteus, and Clostridium in gastric and colorectal cancer patients. This dysbiosis regulates bone metabolism by altering microbial metabolite secretion[58,59]. Short-chain fatty acids (SCFAs) regulate bone metabolism through multiple pathways: First, they activate G protein-coupled receptors (GPR43, GPR41) on intestinal epithelial cells to inhibit osteoclast differentiation; second, they enter the circulation to directly promote osteoblast proliferation and mineralization; and third, they regulate the immune system to inhibit TNF-α, IL-6, and other inflammatory factors, reducing chronic inflammation-induced bone damage[60]. Reduced SCFA production in gastrointestinal cancer patients weakens these bone-protective effects, exacerbating metabolic imbalance and increasing osteoporosis risk[61,62]. Fecal microbiota transplantation (FMT) experiments further confirmed the regulatory role of the gut flora in GTO[63]. One study established an ovariectomized (OVX) postmenopausal osteoporosis mouse model and administered FMT for 8 weeks. FMT effectively optimized the composition and abundance of the gut microbiota, significantly increasing the levels of fecal SCFAs (mainly acetate and propionate). Micro-CT confirmed that FMT prevented OVX-induced bone loss and improved bone microarchitecture. Mechanistically, FMT increased the expression of intestinal tight junction proteins (such as ZO-1), improved intestinal permeability, and suppressed the expression of serum osteoclastogenic factors (TNF-α and IL-1β), thereby inhibiting excessive osteoclastogenesis. This study demonstrated that FMT, as a direct microbiota remodeling intervention, exerts clear bone-protective effects in osteoporosis models by correcting dysbiosis, increasing beneficial SCFA levels, repairing the intestinal barrier, and inhibiting systemic inflammation. This study provides a proof-of-concept for microbiota-based interventions targeting the gut–bone axis. Moreover, metabolomic monitoring of SCFAs suggests that metabolite analysis can serve as a key biomarker for evaluating microbiota intervention efficacy, supporting the development of precise microbial therapies[63,64].
Single-cell sequencing can be used to analyze gene expression at the single-cell level, accurately identify cell subpopulations in the gastrointestinal TME and bone microenvironment, reveal crosstalk between cell subsets, and provide an unprecedented perspective for understanding “tumor-bone” interactions in the GTO[65]. The gastrointestinal TME is a complex ecosystem containing tumor cells, immune cells, stromal cells, etc. Among them, immune cells, especially tumor-associated macrophages (TAMs), play critical regulatory roles in GTO[66,67]. Using single-cell RNA sequencing, researchers have classified immune cells in gastrointestinal tumor tissues and identified two major TAM subsets: The proinflammatory M1 subset and the anti-inflammatory M2 subset. M1 TAMs are key drivers of GTO[68]. M1 TAMs are major sources of proinflammatory factors in the TME. Secreted TNF-α, IL-6, and IL-1β enter the circulation, directly acting on osteoclast precursors to accelerate maturation and stimulating osteolineage cells to upregulate RANKL, disrupting the RANKL/OPG balance and systemically enhancing bone resorption[69]. BMSCs are core seed cells used for bone repair and regeneration. The differentiation fate of these cells (osteoblasts vs adipocytes) is critical for maintaining bone mineral density[70]. Single-cell sequencing revealed the abnormal differentiation of BMSCs in gastrointestinal tumor patients, as indicated by decreased osteogenic capacity and increased adipogenic capacity, which is an important cause of osteopenia[71,72]. Cutting-edge single-cell transcriptomic analysis indicates that this imbalance stems from a reversal of key osteogenic-adipogenic transcriptional programs: Suppressed expression of the osteogenic master regulator RUNX2 and aberrantly activated expression of the adipogenic regulator peroxisome proliferator-activated receptor gamma (PPARγ)[73]. At the molecular level, RUNX2 directly initiates osteogenic gene programs such as Osterix and COL1A1, while activated PPARγ not only drives adipogenic genes but also antagonizes classical osteogenic signaling, dual-suppressing bone formation at the transcriptional level[74]. Gastrointestinal tumors reshape the local and systemic microenvironments by secreting factors such as PTHrP and TNF-α. These factors interfere with intracellular signaling (NF-κB, Wnt/β-catenin, MAPK), potentially reducing RUNX2 expression/activity and increasing PPARγ expression/activity in BMSCs, shifting differentiation toward adipogenesis and inhibiting osteogenesis, ultimately impairing bone formation and contributing to tumor-associated osteoporosis[75,76]. This mechanism remains a testable hypothesis requiring validation through in vitro BMSC treatment with tumor-conditioned medium or single factors and functional interventions in tumor animal models (e.g., evaluating the effects of RUNX2 activators or PPARγ antagonists on bone mass and osteogenic markers). If confirmed, RUNX2 activation or PPARγ inhibition may represent potential molecular intervention strategies, but specificity, systemic side effects, and safety must be evaluated before clinical translation. The multifactorial mechanisms and downstream biological consequences of GTO are summarized in Figure 2.
Insufficient generalization and clinical validation of AI models: Although AI shows great potential in the screening and prediction of GTO, its clinical translation still faces multiple substantial challenges[77]. At present, most models are developed and validated on the basis of single-center datasets with limited samples, carrying a significant risk of overfitting, which results in insufficient generalizability and external applicability[78]. Overfitting is characterized by excellent model performance on training data but a marked drop in independent and heterogeneous external datasets, mainly due to the inherent selection bias of single-center data, such as homogeneous patient populations and high standardization of imaging acquisition protocols. When such models are deployed at different medical institutions, the heterogeneity of imaging equipment (from manufacturers such as GE, Siemens, Philips, etc.) and differences in scanning parameters and reconstruction algorithms (including tube voltage, tube current, slice thickness, and postprocessing methods) introduce significant domain shifts, leading to systematic differences in imaging features of the same patient under different scenarios, thereby weakening the stability of model judgment[79]. A convincing example comes from the field of spinal imaging analysis: A study developed a deep learning model for automatic vertebral fracture detection via Xrays. The model performed excellently in the elderly patient cohort (≥ 60 years) used for training (sensitivity 88.97%), but when it was directly applied to young patients (18-59 years) at the same institution with different age distributions, its detection sensitivity decreased sharply to 65.00%[80].
This empirical evidence indicates that even within the same medical institution, changes in patient cohort characteristics (such as age and comorbidities) alone can lead to clinically meaningful performance degradation in AI models. Extending to CT imaging screening for GTO, differences in imaging equipment, scanning protocols, tumor stage, and treatment history across medical centers may inevitably pose more severe challenges to model generalization. Another critical limitation is the lack of model interpretability. Most deep learning-based models are “black boxes” with opaque decision-making logic, making it difficult for clinicians to understand the diagnostic basis, thereby affecting trust and adoption[81,82]. The lack of interpretability not only hinders clinical validation but also restricts application in regulatory review and medical liability determination[83]. In summary, despite the promising role of AI in GTO-assisted diagnosis, its actual clinical application is limited by three core issues: Insufficient data generalization, low-level clinical validation, and poor interpretability. Future efforts are needed to build multicenter standardized datasets, conduct prospective intervention studies, and develop explainable AI methods to promote translation into reliable, usable, and trustworthy clinical tools[84].
Long cycle and high cost of the clinical translation of nanoscale targeted drugs: Nanodrugs have high research and development and production costs due to their complex materials, strict quality control, and long-term safety evaluation. Although nanomedicines show great potential and broad application prospects in disease treatment, diagnosis, and monitoring, their clinical translation and large-scale application still face multiple obstacles. The complexity of in vivo behavior and insufficient technical adaptability are key challenges, especially in special disease scenarios such as GTO, where the dual demands of tumor treatment and bone metabolism regulation must be balanced, further increasing adaptation difficulty[85]. First, the in vivo fate and mechanism of action of nanomedicines have not been fully elucidated, making it difficult to meet the requirements of precise therapy in special disease settings[86]. Although nanocarriers such as lipid nanoparticles, extracellular vesicles, and metal–organic frameworks exhibit outstanding advantages in targeted delivery and controlled release in basic research, after they enter the human body, core issues such as their interaction with biological barriers, clearance by the mononuclear phagocyte system, and specific enrichment efficiency in diseased tissues still need in-depth exploration[87]. Some nanomedicines are susceptible to protein corona formation in vivo, leading to failure of ligand targeting, reduced cellular uptake efficiency, and even off-target effects, making it difficult to act precisely on tumor lesions and sites with abnormal bone metabolism simultaneously. Moreover, the metabolic pathways, excretion routes, and long-term accumulation risks of nanomaterials remain unclear. Inorganic nanomaterials such as carbon nanotubes and graphene oxide may damage biofilms and DNA through direct physical injury or reactive oxygen species production. GTO patients often have impaired liver and kidney function and immune dysfunction related to gastrointestinal tumors, and safety data in this special population are severely lacking, increasing the degree of clinical application risk[88-90]. At present, most in vivo studies of nanomedicines still rely on small animal models, which have significant species differences from the human physiological environment. There is also a lack of dedicated models for the pathological characteristics of GTO, and long-term pharmacokinetic and toxicological data in large animals are insufficient to provide adequate safety evidence for clinical translation[91]. Second, the research and development, production, and quality control of nanomedicines are difficult, making adapting to the individualized treatment needs of special diseases difficult. The preparation of nanocarriers has strict requirements for material selection, structural design, and process parameters. For example, optimization of the membrane composition of lipid nanoparticles and precision of cell membrane modification for biomimetic nanocarriers require high-precision equipment and complex processes, prolonging the research and development cycle and increasing industrialization costs. Moreover, the quality control system of nanomedicines is not fully mature. Detection methods for key quality indicators, such as uniformity of the particle size distribution, stability of the drug loading rate, and sensitivity of the trigger response, lack unified standards, and batch differences are prone to occur during large-scale production, directly affecting the consistency of efficacy and safety[92-94]. For GTO patients, who require both tumor-targeted therapeutic drugs and bone metabolism regulators, technologies such as codelivery and synergistic release of nanomedicines still need breakthroughs. Individual differences in tumor progression and bone loss also impose greater requirements on dose regulation and response sensitivity, further increasing research and development and quality control difficulties. In addition, the adaptability of nanomedicines needs to be improved to meet the comprehensive treatment needs of GTO patients. Currently, exogenous triggers (such as light and ultrasound) of responsive nanomedicines suffer from poor tissue penetration and limited applicable sites, whereas specific regulation of endogenous triggers (such as pH and enzymes) is difficult, making it difficult to precisely match the dual pathological features of the TME and abnormal bone metabolism regions in GTO patients[95]. Some nanomedicines have obvious limitations in terms of their administration routes. Intravenous injection may cause adverse reactions such as immune reactions and vascular irritation, and GTO patients often have reduced physical tolerance due to tumor consumption, requiring increased safety[96]. Oral formulations face bottlenecks such as gastrointestinal degradation and low absorption efficiency and cannot adapt to gastrointestinal dysfunction in GTO patients. Local administration routes, such as skin and eye delivery, have difficulty covering the systemic treatment of tumors and bones, limiting their use in clinical scenarios[91,97]. In conclusion, the challenges of unclear in vivo mechanisms, complex research and development and quality control, and insufficient application adaptability in the clinical translation of nanomedicines are more prominent in special diseases such as GTO, which requires multitarget and multipathway therapy. In the future, targeted design, safety evaluation, and administration routes of nanomedicines should be optimized according to the pathological characteristics of GTO to better promote clinical translation and application.
The precision management of tumor-related bone health (GTO) fundamentally relies on the systematic integration and analysis of multidimensional and multisource heterogeneous data. This requires the integration of tumor diagnosis and treatment records, bone mineral density and bone metabolic marker monitoring results, laboratory biochemical indicators, medical imaging data, and patient lifestyle data to construct a comprehensive profile of the individual pathophysiological state and guide precise intervention. However, the widespread “data silos” (i.e., data isolated among different departments and hospitals cannot be shared or analyzed uniformly) and research environments seriously hinder the achievement of this goal[98]. Cross-disciplinary and cross-institutional data integration and sharing face multiple structural bottlenecks. The primary challenge stems from the lack of technical interoperability[99]. Data management systems used by various clinical departments and medical institutions differ significantly in architecture and standards[100]. For example, electronic medical record systems in surgical oncology often record treatment plans and staging in unstructured and semistructured text; orthopedic databases mostly store bone density scan results and bone metabolic markers (such as PINP and β-CTX) in structured tables; imaging departments store raw image sequences following the digital imaging and communications in medicine standard; and laboratory information systems output highly structured test reports[101]. These heterogeneous data formats cannot be directly connected and require complex and error-prone processes of data extraction, cleaning, and conversion, greatly increasing the technical complexity and economic cost of data fusion. Second, the detection of key biomarkers lacks a standardized and consistent reference system, leading to insufficient data quality and comparability. Different laboratories use different detection platforms, reagent brands, and operating procedures, so test results and reference intervals of the same indicator (such as β-CTX) may not be directly comparable across institutions[102]. For example, β-CTX measured via different commercial kits may present significant shifts in reported normal reference ranges. This variability complicates longitudinal follow-up and multicenter data analysis and weakens the reliability of clinical decisions and research conclusions. Finally, increasingly stringent regulations on personal data privacy protection and ethical review requirements constitute institutional barriers to data sharing. Cross-institutional data sharing requires not only overcoming technical difficulties but also completing cumbersome ethical review, data security assessment, and patient informed consent procedures. Concerns about compliance risks and potential legal disputes make many medical institutions conservative about data openness, further solidifying data silos and limiting the construction of large-scale, high-quality datasets for advanced analysis[103].
Practical clinical management pathway: The standard clinical workflow for GTO includes baseline bone mineral density examination, bone turnover marker detection, regular follow-up every 6-12 months, and timely bone protection intervention for high-risk patients. Multidisciplinary cooperation involving oncologists, orthopedists, endocrinologists, and nutritionists is needed to formulate individualized strategies. It ideal model requires in-depth cooperation among surgical oncology, orthopedics, radiology, clinical nutrition, pharmacy, and rehabilitation medicine to jointly build a full-cycle diagnosis and treatment closed loop covering “comprehensive tumor treatment-systematic bone health monitoring-targeted bone protection intervention-personalized functional rehabilitation”[104]. However, the current multidisciplinary team (MDT) model has significant defects in practice and has difficulty supporting the above precision management goals. The primary problem is the lack of institutionalization and proactivity in multidisciplinary collaboration[105]. In many medical institutions, MDT consultations are often reduced to a “firefighting” emergency response mechanism, which is temporarily initiated only after patients develop severe SRE such as pathological fractures or spinal cord compression. At this stage, bone structural damage is mostly irreversible, and the window for early prevention and intervention has been missed. For high-risk GTO patients identified at the initial stage of tumor treatment or during systemic therapy, there is no routine and proactive multidisciplinary evaluation process to promptly initiate individualized bone health management plans. Second, significant knowledge barriers and goal differences among participating disciplines lead to low collaboration efficiency and difficulty in implementing “integrated therapy”. Oncologists and gastroenterologists focus on identifying high-risk patients but often pay insufficient attention to treatment-related bone toxicity and long-term bone health, whereas rheumatologists and endocrinologists are primarily responsible for the formal diagnosis and standardized treatment of osteoporosis. Orthopedists, who excel in osteoporosis management, lack sufficient knowledge of gastrointestinal tumor biology and systemic therapies, limiting the development of coordinated bone-protective strategies[106].
Clinical nutritionists may lack precise knowledge of abnormal tumor metabolism and bone nutrient loss caused by specific drugs, resulting in a failure to provide optimized nutritional support for GTO patients[107]. In addition, the lack of authoritative and unified clinical practice guidelines and standardized pathways in this field is the root cause of diagnostic and therapeutic arbitrariness and outcome heterogeneity. At present, there are no internationally issued consensus or clinical pathway guidelines specifically for precision management of GTO. This lack of standards directly leads to enormous differences in practice among different institutions and even physicians; for example, there is no evidence-based basis for key decisions such as when baseline bone density screening should be performed after gastrointestinal tumor surgery (1 month vs 6 months) and how to choose first-line bone-modifying agents (bisphosphonates vs RANKL inhibitors such as denosumab). This inconsistency in management strategies not only results in uneven patient outcomes but also seriously hinders the standardization and evaluation of treatment quality[108].
The application of cutting-edge technologies such as AI and nanomedicine in the precision management of GTO is driving the innovation of diagnosis and treatment paradigms, but it also introduces nonnegligible ethical and biosafety challenges. AI-driven decision support systems face two main core risks. First, at the level of data privacy and security, the construction of high-performance AI models heavily relies on massive, multidimensional sensitive patient information, including medical images and detailed clinical records. Inadequate security measures (such as unencrypted transmission, uncontrolled third-party access) during data collection, storage, sharing, and processing may lead to serious privacy breaches, violating data protection regulations such as the GDPR and eroding patient trust[109]. Second, regarding the reliability of diagnostic decisions, most current complex AI models are “black boxes” with unexplainable internal decision logic[110]. Clinicians struggle to trace the reasoning path leading to specific conclusions (such as bone metastasis risk grading). Excessive reliance on such opaque AI outputs in clinical practice, while weakening necessary critical thinking, may lead to misjudgement[111]. For example, in GTO patients with rare bone metabolic diseases (such as tumor-induced osteomalacia), AI models may produce incorrect classifications owing to the scarcity of such cases in training data. If not recognized and reviewed by physicians, this may directly lead to inappropriate intervention, resulting in adverse outcomes and ethical disputes. As targeted delivery tools, nanomedicines are also accompanied by unique uncertainties in safety and efficacy[112]. Long-term biosafety is the primary concern. Although preclinical studies have shown good tolerance, there are essential differences in physiological and pathological states between humans and animal models. The long-term pharmacokinetic behavior, potential immunogenicity, and chronic toxicity or inflammatory reactions caused by organ-selective accumulation of nanoparticles in the reticuloendothelial system (such as the liver, spleen, and kidney) have not been fully elucidated[113,114]. This risk is particularly prominent in GTO patients with underlying liver and kidney impairment, which may further aggravate the organ functional burden. On the other hand, interindividual heterogeneity in efficacy constitutes another major challenge. The targeting efficiency and final efficacy of nanomedicines are complexly affected by multiple factors, such as individual tumor biological characteristics (vascular permeability, receptor expression heterogeneity), gene polymorphisms, and systemic immune status, which may lead to unsatisfactory efficacy in some patients. Therefore, how to carefully balance potential benefits and unknown long-term risks on the basis of an in-depth understanding of patient-specific biomarkers and develop truly individualized nanomedicine regimens is a key scientific issue that must be addressed before clinical translation (Table 2). The major translational barriers and the proposed closed-loop implementation pathway for GTO precision management are summarized in Figure 3.
| Category | Core challenges |
| Clinical translation barriers | AI models: Insufficient generalization, overfitting, poor interpretability, lack of multicenter validation. Nanodrugs: Unclear in vivo mechanism, long translation cycle, high cost, difficult quality control, biosafety risks, poor scenario adaptability |
| Data integration and standardization | Data islands among departments and institutions. Multisource heterogeneous data with poor interoperability. Nonuniform detection standards for bone metabolic markers. Ethical and privacy barriers restricting data sharing |
| Imperfect MDT cooperation | MDT is passive and delayed rather than proactive. Knowledge barriers between oncology, orthopedics, nutrition, etc. Lack of standardized GTO-specific guidelines and workflows |
| Ethical and safety risks | Data privacy and security issues in AI applications. “Black box” problem of AI leading to low clinical trust. Unknown long-term biosafety of nanomaterials. Individual heterogeneity and potential toxicity of nanodrugs |
In the future, breakthroughs in building an integrated precision management system for bone health in gastrointestinal tumors will depend strongly on the deep integration of AI with multiomics analysis, nanomedicine, wearable sensing, and other cutting-edge technologies[115,116]. Such integration is expected to drive the diagnosis and treatment model toward a full-cycle, closed-loop management paradigm: Early risk warning–precise molecular typing-individualized dynamic intervention-continuous physiological monitoring.
To achieve this goal, the primary task is to construct a general AI platform based on large-scale, multicenter real-world datasets to overcome the fundamental limitation of insufficient generalizability in current models developed from single-center data[117]. Specifically, privacy-preserving computing paradigms such as federated learning should be adopted to collaboratively integrate heterogeneous data resources from multiple institutions worldwide without transferring raw data. These data should include multimodal imaging data, multidimensional omics data, longitudinal clinical records, and systematic bone health monitoring indicators[118]. Ensemble learning models trained on such high-quality data should possess multitask decision-making capabilities: Interpreting multimodal images for early identification and accurate diagnosis of GTO; integrating genomic, transcriptomic, metabolomic, and microbiomic data for refined molecular subtyping of patients; and generating comprehensive intervention plans, including bone-modifying agent selection, nanotargeted delivery strategies, personalized nutritional support on the basis of subtype results, tumor pathological features, and established treatment regimens. Moreover, noninvasive or minimally invasive wearable sensing technologies can enable continuous dynamic monitoring of key bone metabolic biomarkers and patient functional status, forming a real-time feedback loop of “assessment-intervention-reassessment”. Another key to implementing this system is developing portable, low-cost intelligent detection devices suitable for primary care settings to solve the current low screening coverage caused by shortages of professional manpower and expensive equipment. For example, developing smartphone attachments integrated with AI image recognition algorithms or simplified portable quantitative ultrasound bone densitometers can significantly reduce the technical and economic barriers of screening. This may equip primary healthcare institutions with the ability to conduct routine bone health risk assessments for gastrointestinal tumor patients, fundamentally promoting the universal application of GTO precision management strategies.
To address the bottlenecks in the clinical translation of nanoscale targeted drugs, future efforts should focus on three dimensions, namely, technical optimization, clinical validation, and cost control, to accelerate translation and improve clinical accessibility. First, the biocompatibility and administration routes of nanomaterials should be optimized to increase patient compliance and safety. Clinically approved biocompatible materials (e.g., polyethylene glycol, liposomes, and gelatin) should be prioritized to reduce immune reactions and organ accumulation risks. Moreover, research on oral nanomedicines should be strengthened. Through surface modification with enteric materials (e.g., polymethyl methacrylate), microspheres or nanomicelles can improve stability in the gastrointestinal tract, avoid degradation by gastric acid and enzymes, and enhance intestinal absorption efficiency. For example, loading denosumab into enteric-coated nanospheres allows intestinal release, absorption through the mucosa into circulation, and precise targeting to bone tissue, greatly improving patient compliance[119,120]. Second, multicenter, large-sample clinical trials should be conducted to accumulate sufficient clinical evidence. For nanoscale targeted drugs that have completed preclinical studies, prospective, multicenter, randomized controlled clinical trials should be launched, enrolling GTO patients with different tumor types and treatment stages to evaluate their safety and efficacy and clarify key clinical parameters such as the optimal dosage, administration interval, and target population. Moreover, long-term safety monitoring should be strengthened, and adverse reaction reporting and early warning mechanisms should be established to provide reliable support for clinical application[121]. Third, research and development and production costs should be reduced through technological innovation and large-scale manufacturing. At the research and development stage, high-throughput screening and microfluidic technologies can improve efficiency and shorten cycles. At the production stage, process optimization enables large-scale, standardized production, lowering unit costs[122]. In addition, cooperation between enterprises and research institutions via technology transfer and joint development is encouraged to promote industrialization, reduce market prices, and improve patient accessibility. Furthermore, on the basis of specific targets of the “tumor–bone axis”, multitarget synergistic nanodrug delivery systems should be developed to simultaneously regulate tumor progression and abnormal bone metabolism and enhance therapeutic effects. For example, constructing multitarget nanosystems coloaded with antitumor drugs, bone-protective agents, and anti-inflammatory drugs can inhibit tumor growth, protect bone health, and alleviate inflammatory responses simultaneously.
Improving multidisciplinary collaboration and data standardization systems is an important guarantee for GTO precision management, which requires progress in mechanism construction, data standardization, and guideline formulation. First, a standardized MDT collaboration mechanism should be established, and GTO precision management should be fully integrated into the whole-course diagnosis and treatment pathway of gastrointestinal tumor patients. Clarify the responsibilities of surgical oncology, orthopedics, radiology, nutrition, and other disciplines in GTO management: Surgical oncologists conduct GTO risk screening at the initial stage of tumor treatment and initiate timely MDT consultations; orthopedists develop personalized bone protection and SRE management plans; radiologists perform bone mineral density measurements and imaging evaluations; and nutritionists formulate precise nutritional supplementation plans. A regular MDT consultation system should be established, with monthly MDT evaluations for high-risk patients after gastrointestinal tumor surgery or during chemotherapy to adjust intervention plans promptly. Moreover, an interdisciplinary information-sharing platform should be built to realize real-time synchronization of diagnostic information and improve collaboration efficiency. Second, the standardization and sharing of medical data should be promoted, and “data silos” should be broken. Led by health administrative departments, unified medical data standardization specifications should be formulated, defining storage formats, coding standards, and transmission protocols for tumor diagnosis and treatment data, bone health data, and laboratory data to achieve interoperability across disciplines and institutions. A regional medical data sharing center should be established, using blockchain to ensure privacy and security and realizing cross-institutional data sharing with informed consent and ethical approval[123]. Moreover, reference standards for key indicators such as bone metabolic markers and bone density detection should be unified, and testing methods and reagent selection should be standardized to improve comparability. Third, expert consensus and clinical guidelines for GTO precision management should be developed to standardize practice. Experts in oncology, orthopedics, radiology, nutrition, and other fields should formulate guidelines on the basis of the latest evidence and clarify key contents, including screening processes (timing, population, methods), risk assessment criteria, intervention timing, treatment selection, and monitoring indicators. Moreover, guideline promotion and training should be strengthened to improve clinicians’ awareness and implementation capacity. In addition, interdisciplinary talent training combining oncology and orthopedics should be enhanced to cultivate professionals who are proficient in both tumor diagnosis and bone health management.
While promoting innovative applications of advanced technologies, a sound ethical supervision system must be established to standardize procedures and protect patients’ legitimate rights and medical safety. First, regulations and technical systems for medical data privacy protection should be improved. In response to data privacy issues in AI applications, the requirements of laws and regulations such as the Personal Information Protection Law and Medical Data Security Guidelines should be further refined to clarify specific norms and responsibility divisions for data collection, storage, use, and sharing. Technologies such as data encryption, anonymization, and federated learning should be adopted to achieve effective data utilization without privacy leakage. For example, federated learning allows local data retention and sharing only of model training intermediate results, avoiding raw data transmission and exposure. Second, clinical access standards and supervision mechanisms for AI diagnostic models and nanomedicines should be established. AI diagnostic equipment should be included in medical device supervision, with clear access standards requiring sufficient multicenter clinical trial evidence to prove safety and effectiveness. Moreover, a continuous monitoring and updating mechanism for AI models should be established, requiring enterprises to optimize models on the basis of real-world data and resolve issues promptly. For nanomedicines, preclinical safety evaluation and clinical trial supervision should be strengthened, with strict review of protocols to protect participants. A postmarketing surveillance system should be established to track long-term adverse reactions and update instructions in a timely manner. Third, the position of AI in clinical diagnosis should be clarified, and the responsibility boundaries between doctors and AI should be standardized. AI should serve as an auxiliary tool rather than a replacement; final clinical decisions must be made by doctors on the basis of comprehensive patient evaluation. A manual review system for AI results should be established, with positive or suspicious findings confirmed by professional physicians to avoid medical disputes caused by misdiagnosis or missed diagnosis. In addition, extensive patient and physician education should be carried out through hospital posters, lectures, and online platforms to popularize the principles, advantages, and potential risks of advanced technologies; improve rational application by clinicians and acceptance by patients; and promote standardized and reasonable use (Table 3).
| Dimension | Future strategies |
| Construction of integrated precision system | Multitechnology fusion (AI, multiomics, nanomedicine, wearable sensors). Multicenter dataset and federated learning. Closed-loop management: Early warning, precise typing, intervention, monitoring |
| Accelerated clinical translation of nanotechnology | Optimize biocompatibility and administration routes (oral, enteric-coated). Conduct multicenter, randomized, controlled clinical trials. Scale production to reduce cost; develop multitarget synergistic nanosystems |
| Improved MDT and data standardization | Establish standardized, proactive MDT workflow. Define responsibilities of oncology, orthopedics, radiology, nutrition. Unify data standards and build regional data sharing platform. Develop GTO-specific clinical guidelines |
| Ethical supervision and technical regulation | Improve data privacy protection and encryption technology. Establish clinical access and postmarketing surveillance system. Define AI as an auxiliary tool; strengthen manual review. Standardize clinical application and risk control of new technologies |
GTO is an underrecognized yet clinically important complication that significantly affects skeletal health, treatment tolerance, quality of life, and long-term prognosis in patients with gastrointestinal tumors. Its development is driven by complex interactions among tumor-related factors, antitumor therapies, and gastrointestinal dysfunction, highlighting the limitations of traditional empirical management. Emerging technologies, particularly AI, nanotargeted delivery systems, and multiomics approaches, are reshaping GTO management by improving early screening, enabling more precise interventions, and deepening mechanistic understanding. Nevertheless, major barriers remain, including limited AI generalizability, slow nanomedicine translation, inadequate data integration and standardization, insufficient multidisciplinary collaboration, and unresolved ethical and safety concerns. Future progress may depend on multitechnology integration, stronger clinical validation, standardized data and care pathways, and improved regulatory oversight. These advances may ultimately promote the transition of GTO management from empirical practice to precision medicine and improve long-term outcomes for patients with gastrointestinal tumors.
The authors sincerely thank Zhejiang Luming Biotechnology Co., Ltd. (2nd Floor, 113-1 to 113-5 Nanliu Road, Chashan Street, Ouhai District, Wenzhou, Zhejiang Province, China) for providing technical assistance with scientific figure preparation, formatting refinement, and other manuscript-related support during the development of this work. The authors alone are responsible for the scientific content, interpretations, and conclusions presented in this manuscript.
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