The diagnosis, classification, and treatment of sarcoma in this era of artificial intelligence and immunotherapy

Utility of artificial intelligence in a binary classification of soft tissue tumors

Soft tissue tumors (STTs) pose diagnostic and therapeutic challenges due to their rarity, complexity, and morphological overlap. Accurate differentiation between benign and malignant STTs is important to set treatment directions, however, this task can be difficult. The integration of machine learning and artificial intelligence (AI) models can potentially be helpful in classifying these tumors. The aim of this study was to investigate AI and machine learning tools in the classification of STT into benign and malignant categories. This study consisted of three components: (1) Evaluation of whole-slide images (WSIs) to classify STT into benign and malignant entities. Five specialized soft tissue pathologists from different medical centers independently reviewed 100 WSIs, representing 100 different cases, with limited clinical information and no additional workup. The results showed an overall concordance rate of 70.4% compared to the reference diagnosis. (2) Identification of cell-specific parameters that can distinguish benign and malignant STT. Using an image analysis software (QuPath) and a cohort of 95 cases, several cell-specific parameters were found to be statistically significant, most notably cell count, nucleus/cell area ratio, nucleus hematoxylin density mean, and cell max caliper. (3) Evaluation of machine learning library (Scikit-learn) in differentiating benign and malignant STTs. A total of 195 STT cases (156 cases in the training group and 39 cases in the validation group) achieved approximately 70% sensitivity and specificity, and an AUC of 0.68. Our limited study suggests that the use of WSI and AI in soft tissue pathology has the potential to enhance diagnostic accuracy and identify parameters that can differentiate between benign and malignant STTs. We envision the integration of AI as a supportive tool to augment the pathologists' diagnostic capabilities.

Sarculator: how to improve further prognostication of all sarcomas

PURPOSE OF REVIEW

Prognostication of soft tissue sarcomas is challenging due to the diversity of prognostic factors, compounded by the rarity of these tumors. Nomograms are useful predictive tools that assess multiple variables simultaneously, providing estimates of individual likelihoods of specific outcomes at defined time points. Although these models show promising predictive ability, their use underscores the need for further methodological refinement to address gaps in prognosis accuracy.

RECENT FINDINGS

Ongoing efforts focus on improving prognostic tools by either enhancing existing models based on established parameters or integrating novel prognostic markers, such as radiomics, genomic, proteomic, and immunologic factors. Artificial intelligence is a new field that is starting to be explored, as it has the capacity to combine and analyze vast and intricate amounts of relevant data, ranging from multiomics information to real-time patient outcomes.

SUMMARY

The integration of these innovative markers and methods could enhance the prognostic ability of nomograms such as Sarculator and ultimately enable more accurate and individualized healthcare. Currently, clinical variables continue to be the most significant and effective factors in terms of predicting outcomes in patients with STS. This review firstly introduces the rationale for developing and employing nomograms such as Sarculator, secondly, reflects on some of the latest and ongoing methodological refinements, and provides future perspectives in the field of prognostication of sarcomas.

Discrimination between benign and malignant gallbladder lesions on enhanced CT imaging using radiomics

BACKGROUND

Gallbladder cancer is a rare but aggressive malignancy that is often diagnosed at an advanced stage and is associated with poor outcomes.

PURPOSE

To develop a radiomics model to discriminate between benign and malignant gallbladder lesions using enhanced computed tomography (CT) imaging.

MATERIAL AND METHODS

All patients had a preoperative contrast-enhanced CT scan, which was independently analyzed by two radiologists. Regions of interest were manually delineated on portal venous phase images, and radiomics features were extracted. Feature selection was performed using mRMR and LASSO methods. The patients were randomly divided into training and test groups at a ratio of 7:3. Clinical and radiomics parameters were identified in the training group, three models were constructed, and the models' prediction accuracy and ability were evaluated using AUC and calibration curves.

RESULTS

In the training group, the AUCs of the clinical model and radiomics model were 0.914 and 0.968, and that of the nomogram model was 0.980, respectively. There were statistically significant differences in diagnostic accuracy between nomograms and radiomics features (P <0.05). There was no significant difference in diagnostic accuracy between the nomograms and clinical features (P >0.05) or between the clinical features and radiomics features (P >0.05). In the testing group, the AUC of the clinical model and radiomics model were 0.904 and 0.941, and that of the nomogram model was 0.948, respectively. There was no significant difference in diagnostic accuracy between the three groups (P >0.05).

CONCLUSION

It was suggested that radiomics analysis using enhanced CT imaging can effectively discriminate between benign and malignant gallbladder lesions.

Self-healing hydrogels for enhancing chemotherapy drug efficacy: Advancements in anti-sarcoma and carcinoma therapies and clinical trial feasibility

•Site-specific administration is key for optimizing anticancer drug administration; self-healing hydrogels may allow this at reasonable costs and reproducibility.•Self-healing hydrogels have several real-world therapeutic applications, including drug administration.•Self-healing hydrogels are yet to be utilized for chemotherapy drug administration in clinical trials.•Clinical research on using self-healing hydrogels in anticancer therapeutics is feasible and valid compared to other advances in anticancer drug administration.

Deep learning on tertiary lymphoid structures in hematoxylin-eosin predicts cancer prognosis and immunotherapy response

Tertiary lymphoid structures (TLSs) have been associated with favorable immunotherapy responses and prognosis in various cancers. Despite their significance, their quantification using multiplex immunohistochemistry (mIHC) staining of T and B lymphocytes remains labor-intensive, limiting its clinical utility. To address this challenge, we curated a dataset from matched mIHC and H&E whole-slide images (WSIs) and developed a deep learning model for automated segmentation of TLSs. The model achieved Dice coefficients of 0.91 on the internal test set and 0.866 on the external validation set, along with intersection over union (IoU) scores of 0.819 and 0.787, respectively. The TLS ratio, defined as the segmented TLS area over the total tissue area, correlated with B lymphocyte levels and the expression of CXCL13, a chemokine associated with TLS formation, in 6140 patients spanning 16 tumor types from The Cancer Genome Atlas (TCGA). The prognostic models for overall survival indicated that the inclusion of the TLS ratio with TNM staging significantly enhanced the models' discriminative ability, outperforming the traditional models that solely incorporated TNM staging, in 10 out of 15 TCGA tumor types. Furthermore, when applied to biopsied treatment-naïve tumor samples, higher TLS ratios predicted a positive immunotherapy response across multiple cohorts, including specific therapies for esophageal squamous cell carcinoma, non-small cell lung cancer, and stomach adenocarcinoma. In conclusion, our deep learning-based approach offers an automated and reproducible method for TLS segmentation and quantification, highlighting its potential in predicting immunotherapy response and informing cancer prognosis.

The impact of radiomics in the management of soft tissue sarcoma

INTRODUCTION

Soft tissue sarcomas (STSs) are rare malignancies. Pre-therapeutic tumour grading and assessment are crucial in making treatment decisions. Radiomics is a high-throughput method for analysing imaging data, providing quantitative information beyond expert assessment. This review highlights the role of radiomic texture analysis in STSs evaluation.

MATERIALS AND METHODS

We conducted a systematic review according to the Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive search was conducted in PubMed/MEDLINE and Scopus using the search terms: 'radiomics [All Fields] AND ("soft tissue sarcoma" [All Fields] OR "soft tissue sarcomas" [All Fields])'. Only original articles, referring to humans, were included.

RESULTS

A preliminary search conducted on PubMed/MEDLINE and Scopus provided 74 and 93 studies respectively. Based on the previously described criteria, 49 papers were selected, with a publication range from July 2015 to June 2023. The main domains of interest were risk stratification, histological grading prediction, technical feasibility/reproductive aspects, treatment response.

CONCLUSIONS

With an increasing interest over the last years, the use of radiomics appears to have potential for assessing STSs from initial diagnosis to predicting treatment response. However, additional and extensive research is necessary to validate the effectiveness of radiomics parameters and to integrate them into a comprehensive decision support system.

Tertiary lymphoid structures in cancer: immune mechanisms and clinical implications

Cancer is a major cause of death globally, and traditional treatments often have limited efficacy and adverse effects. Immunotherapy has shown promise in various malignancies but is less effective in tumors with low immunogenicity or immunosuppressive microenvironment, especially sarcomas. Tertiary lymphoid structures (TLSs) have been associated with a favorable response to immunotherapy and improved survival in cancer patients. However, the immunological mechanisms and clinical significance of TLS in malignant tumors are not fully understood. In this review, we elucidate the composition, neogenesis, and immune characteristics of TLS in tumors, as well as the inflammatory response in cancer development. An in-depth discussion of the unique immune characteristics of TLSs in lung cancer, breast cancer, melanoma, and soft tissue sarcomas will be presented. Additionally, the therapeutic implications of TLS, including its role as a marker of therapeutic response and prognosis, and strategies to promote TLS formation and maturation will be explored. Overall, we aim to provide a comprehensive understanding of the role of TLS in the tumor immune microenvironment and suggest potential interventions for cancer treatment.

Gut microbiota reshapes cancer immunotherapy efficacy: Mechanisms and therapeutic strategies

Gut microbiota is essential for maintaining local and systemic immune homeostasis in the presence of bacterial challenges. It has been demonstrated that microbiota play contrasting roles in cancer development as well as anticancer immunity. Cancer immunotherapy, a novel anticancer therapy that relies on the stimulation of host immunity, has suffered from a low responding rate and incidence of severe immune-related adverse events (irAEs). Previous studies have demonstrated that the diversity and composition of gut microbiota were associated with the heterogeneity of therapeutic effects. Therefore, alteration in microbiota taxa can lead to improved clinical outcomes in immunotherapy. In this review, we determine whether microbiota composition or microbiota-derived metabolites are linked to responses to immunotherapy and irAEs. Moreover, we discuss various approaches to improve immunotherapy efficacy or reduce toxicities by modulating microbiota composition.

Tailoring biomaterials for monitoring and evoking tertiary lymphoid structures

Despite the remarkable clinical success of immune checkpoint blockade (ICB) in the treatment of cancer, the response rate to ICB therapy remains suboptimal. Recent studies have strongly demonstrated that intratumoral tertiary lymphoid structures (TLSs) are associated with a good prognosis and a successful clinical response to immunotherapy. However, there is still a shortage of efficient and wieldy approaches to image and induce intratumoral TLSs in vivo. Biomaterials have made great strides in overcoming the deficiencies of conventional diagnosis and therapies for cancer, and antitumor therapy has also benefited from biomaterial-based drug delivery models. In this review, we summarize the reported methods for TLS imaging and induction based on biomaterials and provide potential strategies that can further enhance the effectiveness of imaging and stimulating intratumoral TLSs to predict and promote the response rates of ICB therapies for patients. STATEMENT OF SIGNIFICANCE: In this review, we focused on the promising of biomaterials for imaging and induction of TLSs. We reviewed the applications of biomaterials in molecular imaging and immunotherapy, identified the biomaterials that may be suitable for TLS imaging and induction, and provided outlooks for further research. Accurate imaging and effective induction of TLSs are of great significance for understanding the mechanism and clinical application. We highlighted the need for multidisciplinary coordination and cooperation in this field, and proposed the possible future direction of noninvasive imaging and artificial induction of TLSs based on biomaterials. We believe that it can facilitate collaboration and galvanize a broader effort.

Staging Systems and Nomograms for Soft Tissue Sarcoma

Reliable tools for prognosis prediction are crucially needed by oncologists so they can tailor individual treatments. However, the wide spectrum of histologies and prognostic behaviors of sarcomas challenges their development. In this field, nomograms could definitely better account for their granularity compared to the more widely used AJCC/UICC TNM staging system. Nomograms are predictive tools that incorporate multiple risk factors and return a numerical probability of a clinical event. Since the development of the first nomogram in 2002, several other nomograms have been built, either general, site-specific, histology-specific, or both. Recently, some new “dynamic” nomograms and prognostic tools have been developed, allowing doctors to “recalculate” a patient’s prognosis by taking into account the time since primary surgery, the event history, and the potential time-dependent effect of covariates. Due to these new tools, prognosis prediction is no longer limited to the time of the first computation but can be adapted and recalculated based on the occurrence (or not) of any event as time passes from the first computation. In this review, we aimed to give an overview of the available nomograms for STS and to help clinicians in the process of selecting the best tool for each patient.

Soft-tissue sarcoma in adults: Imaging appearances, pitfalls and diagnostic algorithms

This article provides an overview of the current knowledge regarding diagnostic imaging of patients with soft-tissue sarcomas, which is a heterogeneous group of rare mesenchymal malignancies. After an initial contextualization, diagnostic flow-chart based on initial radiological findings of soft-tissue masses (with specific focus on adipocytic soft-tissue tumors [STTs], hemorragic STTs and retroperitoneal STTs) are provided considering relevant results from novel researches, guidelines, and experts' viewpoints, with the aim to help radiologists and clinicians in their practice. Particularly, the central place of sarcoma reference centers in the diagnostic and therapeutic management is highlighted, as well as the pivotal role that radiologists should play to correctly identify patients with soft-tissue sarcoma at the initial stage of the disease. Indications and methods for performing imaging-guided biopsies are also discussed, as well as clues to improve soft-tissue sarcoma grading with conventional and quantitative imaging.