Application of Artificial Intelligence in the Diagnosis, Treatment, and Prognostic Evaluation of Mediastinal Malignant Tumors

Imaging at the nexus: how state of the art imaging techniques can enhance our understanding of cancer and fibrosis

Both cancer and fibrosis are diseases involving dysregulation of cell signaling pathways resulting in an altered cellular microenvironment which ultimately leads to progression of the condition. The two disease entities share common molecular pathophysiology and recent research has illuminated the how each promotes the other. Multiple imaging techniques have been developed to aid in the early and accurate diagnosis of each disease, and given the commonalities between the pathophysiology of the conditions, advances in imaging one disease have opened new avenues to study the other. Here, we detail the most up-to-date advances in imaging techniques for each disease and how they have crossed over to improve detection and monitoring of the other. We explore techniques in positron emission tomography (PET), magnetic resonance imaging (MRI), second generation harmonic Imaging (SGHI), ultrasound (US), radiomics, and artificial intelligence (AI). A new diagnostic imaging tool in PET/computed tomography (CT) is the use of radiolabeled fibroblast activation protein inhibitor (FAPI). SGHI uses high-frequency sound waves to penetrate deeper into the tissue, providing a more detailed view of the tumor microenvironment. Artificial intelligence with the aid of advanced deep learning (DL) algorithms has been highly effective in training computer systems to diagnose and classify neoplastic lesions in multiple organs. Ultimately, advancing imaging techniques in cancer and fibrosis can lead to significantly more timely and accurate diagnoses of both diseases resulting in better patient outcomes.

Effects of Interobserver Segmentation Variability and Intensity Discretization on MRI-Based Radiomic Feature Reproducibility of Lipoma and Atypical Lipomatous Tumor

Segmentation and image intensity discretization impact on radiomics workflow. The aim of this study is to investigate the influence of interobserver segmentation variability and intensity discretization methods on the reproducibility of MRI-based radiomic features in lipoma and atypical lipomatous tumor (ALT). Thirty patients with lipoma or ALT were retrospectively included. Three readers independently performed manual contour-focused segmentation on T1-weighted and T2-weighted sequences, including the whole tumor volume. Additionally, a marginal erosion was applied to segmentations to evaluate its influence on feature reproducibility. After image pre-processing, with included intensity discretization employing both fixed bin number and width approaches, 1106 radiomic features were extracted from each sequence. Intraclass correlation coefficient (ICC) 95% confidence interval lower bound ≥ 0.75 defined feature stability. In contour-focused vs. margin shrinkage segmentation, the rates of stable features extracted from T1-weighted and T2-weighted images ranged from 92.68 to 95.21% vs. 90.69 to 95.66% after fixed bin number discretization and from 95.75 to 97.65% vs. 95.39 to 96.47% after fixed bin width discretization, respectively, with no difference between the two segmentation approaches (p ≥ 0.175). Higher stable feature rates and higher feature ICC values were found when implementing discretization with fixed bin width compared to fixed bin number, regardless of the segmentation approach (p < 0.001). In conclusion, MRI radiomic features of lipoma and ALT are reproducible regardless of the segmentation approach and intensity discretization method, although a certain degree of interobserver variability highlights the need for a preliminary reliability analysis in future studies.

Personalized Management of Malignant and Non-Malignant Ectopic Mediastinal Thyroid: A Proposed 10-Item Algorithm Approach

We aimed to analyze the management of the ectopic mediastinal thyroid (EMT) with respect to EMT-related cancer and non-malignant findings related to the pathological report, clinical presentation, imaging traits, endocrine profile, connective tissue to the cervical (eutopic) thyroid gland, biopsy or fine needle aspiration (FNA) results, surgical techniques and post-operatory outcome. This was a comprehensive review based on revising any type of freely PubMed-accessible English, full-length original papers including the keywords "ectopic thyroid" and "mediastinum" from inception until March 2024. We included 89 original articles that specified EMTs data. We classified them into four main groups: (I) studies/case series (n = 10; N = 36 EMT patients); (II) malignant EMTs (N = 22 subjects; except for one newborn with immature teratoma in the EMT, only adults were reported; mean age of 62.94 years; ranges: 34 to 90 years; female to male ratio of 0.9). Histological analysis in adults showed the following: papillary (N = 11/21); follicular variant of the papillary type (N = 2/21); Hürthle cell thyroid follicular malignancy (N = 1/21); poorly differentiated (N = 1/21); anaplastic (N = 2/21); medullary (N = 1/21); lymphoma (N = 2/21); and MALT (mucosa-associated lymphoid tissue) (N = 1/21); (III) benign EMTs with no thyroid anomalies (N = 37 subjects; mean age of 56.32 years; ranges: 30 to 80 years; female to male ratio of 1.8); (IV) benign EMTs with thyroid anomalies (N = 23; female to male ratio of 5.6; average age of 52.1 years). This panel involved clinical/subclinical hypothyroidism (iatrogenic, congenital, thyroiditis-induced, and transitory type upon EMT removal); thyrotoxicosis (including autonomous activity in EMTs that suppressed eutopic gland); autoimmune thyroiditis/Graves's disease; nodules/multinodular goiter and cancer in eutopic thyroid or prior thyroidectomy (before EMT detection). We propose a 10-item algorithm that might help navigate through the EMT domain. To conclude, across this focused-sample analysis (to our knowledge, the largest of its kind) of EMTs, the EMT clinical index of suspicion remains low; a higher rate of cancer is reported than prior data (18.8%), incident imagery-based detection was found in 10-14% of the EMTs; surgery offered an overall good outcome. A wide range of imagery, biopsy/FNA and surgical procedures is part of an otherwise complex personalized management.

Enhanced lung cancer detection: Integrating improved random walker segmentation with artificial neural network and random forest classifier

Background

Medical image segmentation is a vital yet difficult job because of the multimodality of the acquired images. It is difficult to locate the polluted area before it spreads.

Methods

This research makes use of several machine learning tools, including an artificial neural network as well as a random forest classifier, to increase the system's reliability of pulmonary nodule classification. Anisotropic diffusion filtering is initially used to remove noise from a picture. After that, a modified random walk method is used to get the region of interest inside the lung parenchyma. Finally, the features corresponding to the consistency of the picture segments are extracted using texture-based feature extraction for pulmonary nodules. The final stage is to identify and classify the pulmonary nodules using a classifier algorithm.

Results

The studies employ cross-validation to demonstrate the validity of the diagnosis framework. In this instance, the proposed method is tested using CT scan information provided by the Lung Image Database Consortium. A random forest classifier showed 99.6 percent accuracy rate for detecting lung cancer, compared to a artificial neural network's 94.8 percent accuracy rate.

Conclusions

Due to this, current research is now primarily concerned with identifying lung nodules and classifying them as benign or malignant. The diagnostic potential of machine learning as well as image processing approaches are enormous for the categorization of lung cancer.

Application of artificial intelligence in the diagnosis, treatment, and recurrence prediction of peritoneal carcinomatosis

Peritoneal carcinomatosis (PC) is a type of secondary cancer which is not sensitive to conventional intravenous chemotherapy. Treatment strategies for PC are usually palliative rather than curative. Recently, artificial intelligence (AI) has been widely used in the medical field, making the early diagnosis, individualized treatment, and accurate prognostic evaluation of various cancers, including mediastinal malignancies, colorectal cancer, lung cancer more feasible. As a branch of computer science, AI specializes in image recognition, speech recognition, automatic large-scale data extraction and output. AI technologies have also made breakthrough progress in the field of peritoneal carcinomatosis (PC) based on its powerful learning capacity and efficient computational power. AI has been successfully applied in various approaches in PC diagnosis, including imaging, blood tests, proteomics, and pathological diagnosis. Due to the automatic extraction function of the convolutional neural network and the learning model based on machine learning algorithms, AI-assisted diagnosis types are associated with a higher accuracy rate compared to conventional diagnosis methods. In addition, AI is also used in the treatment of peritoneal cancer, including surgical resection, intraperitoneal chemotherapy, systemic chemotherapy, which significantly improves the survival of patients with PC. In particular, the recurrence prediction and emotion evaluation of PC patients are also combined with AI technology, further improving the quality of life of patients. Here we have comprehensively reviewed and summarized the latest developments in the application of AI in PC, helping oncologists to comprehensively diagnose PC and provide more precise treatment strategies for patients with PC.

Therapeutic Decision Making in Prevascular Mediastinal Tumors Using CT Radiomics and Clinical Features: Upfront Surgery or Pretreatment Needle Biopsy?

The study aimed to develop machine learning (ML) classification models for differentiating patients who needed direct surgery from patients who needed core needle biopsy among patients with prevascular mediastinal tumor (PMT). Patients with PMT who received a contrast-enhanced computed tomography (CECT) scan and initial management for PMT between January 2010 and December 2020 were included in this retrospective study. Fourteen ML algorithms were used to construct candidate classification models via the voting ensemble approach, based on preoperative clinical data and radiomic features extracted from the CECT. The classification accuracy of clinical diagnosis was 86.1%. The first ensemble learning model was built by randomly choosing seven ML models from a set of fourteen ML models and had a classification accuracy of 88.0% (95% CI = 85.8 to 90.3%). The second ensemble learning model was the combination of five ML models, including NeuralNetFastAI, NeuralNetTorch, RandomForest with Entropy, RandomForest with Gini, and XGBoost, and had a classification accuracy of 90.4% (95% CI = 87.9 to 93.0%), which significantly outperformed clinical diagnosis (p < 0.05). Due to the superior performance, the voting ensemble learning clinical-radiomic classification model may be used as a clinical decision support system to facilitate the selection of the initial management of PMT.

TNM Staging System in Thymoma: A Critical Appraisal?

Thymomas are rare tumors of the anterior mediastinum with peculiar clinical and pathological features. They have been deeply analyzed by pioneer authors, who strictly linked their name to the main pathological and staging classifications. Before the latest edition of the WHO classification of thymic epithelial tumors, the history of thymoma pathological classification inherited the name of the pathologists who systematically addressed the issue, from Levine-Rosai to Muller-Hermelink. Similarly, the thymoma staging system is intimately related to the name of two surgeons, Masaoka and Koga, who historically dealt with this disease. More recently, the traditional tumor-nodes-metastasis (TNM) system has been developed for the staging of this condition, in a rational attempt to put thymomas in conformity with the other solid tumors. The efforts of the International Thymic Malignancies Interest Group (ITMIG) and the Thymic Domain of the Staging and Prognostic Factors Committee (TD-SPFC) of the International Association for the Study of Lung Cancer (IASLC) resulted in the TNM classification of thymic tumors, which have been included in the eighth edition of the American Joint Committee on Cancer's (AJCC) Cancer Staging Manual. Herein, we report a narrative review of the evolution of the thymic epithelial tumors (TET) staging system and present a critical appraisal of the actual TNM classification compared with the historical Masaoka-Koga classification, with special focus on the proposal for the ninth edition of the TNM, expected in 2024.