Radiologic findings of osteonecrosis, osteoradionecrosis, osteomyelitis and jaw metastatic disease with cone beam CT

Medication related osteonecrosis (MRONJ) in the management of CTIBL in breast and prostate cancer patients. Joint report by SIPMO AND SIOMMMS

Background

Low-doses of bone modifying agents (LD-BMAs) compared to those used to treat bone metastases are used in breast or prostate cancer patients on adjuvant endocrine therapy to prevent Cancer Treatment Induced Bone Loss (CTIBL). Their use is associated with an increased risk of developing Medication-Related Osteonecrosis of the Jaw (MRONJ). However, there is not clarity about strategies aimed to minimize the MRONJ risk in cancer patients at different conditions as low- vs high-doses of BMA. This joint report from the Italian Societies of Oral Pathology and Medicine (SIPMO) and of Italian Society of Osteoporosis, Mineral Metabolism and Skeletal Diseases (SIOMMMS) aims to define the dental management of breast and prostate cancer patients with CTIBL under LD-BMAs, to reduce their risk to develop MRONJ.

Methods

This interdisciplinary SIPMO-SIOMMMS Expert Italian Panel reviewed the available international scientific literature and developed a set of recommendations to implement strategies of MRONJ prevention in breast (BC) and prostate cancer (PC) patients undertaking LD-BMAs to prevent CTIBL.

Results

The Expert Panel, after addressing some introductive topics (i.e., CTIBL and its management, pharmacology and pharmacodynamics of BMAs, definition and diagnosis of MRONJ), developed a joint report on the following five issues: a) prevention and dental management in cancer patients candidates to LD-BMAs, or under LD-BMAs; b) prophylactic drug holiday; c) MRONJ treatment; d) LD-BMAs therapeutic drug holiday; and e) restart of LD-BMA treatment after successful healing of MRONJ.Finally, ten key questions with answers were prepared and placed at the end of the document.

Conclusions

Despite obvious weaknesses of the available international literature, the Expert Panel recognized the need to tailor separate MRONJ preventive approach for breast and prostate cancer patients on adjuvant endocrine therapy who begin low-dose BMA therapy to prevent CTIBL and provided this practical guidance for bone specialists and oral healthcare providers. In view of a MRONJ risk for BC and PC patients receiving low-dose BMAs, which approximates that of patients with osteoporosis and other non-malignant diseases undergoing similar treatment schedules, the SIPMO-SIOMMMS Expert Panel recognizes the need for less stringent preventive strategies than those already developed for BC or PC patients with bone metastases taking HD-BMAs.

Deep Learning Radiomic Analysis of MRI Combined with Clinical Characteristics Diagnoses Placenta Accreta Spectrum and its Subtypes

BACKGROUND

Different placenta accreta spectrum (PAS) subtypes pose varying surgical risks to the parturient. Machine learning model has the potential to diagnose PAS disorder.

PURPOSE

To develop a cascaded deep semantic-radiomic-clinical (DRC) model for diagnosing PAS and its subtypes based on T2-weighted MRI.

STUDY TYPE

Retrospective.

POPULATION

361 pregnant women (mean age: 33.10 ± 4.37 years), suspected of PAS, divided into segment training cohort (N = 40), internal training cohort (N = 139), internal testing cohort (N = 60), and external testing cohort (N = 122).

FIELD STRENGTH/SEQUENCE

Coronal T2-weighted sequence at 1.5 T and 3.0 T.

ASSESSMENT

Clinical characteristics such as history of uterine surgery and the presence of placenta previa, complete placenta previa and dangerous placenta previa were extracted from clinical records. The DRC model (incorporating radiomics, deep semantic features, and clinical characteristics), a cumulative radiological score method performed by radiologists, and other models (including a radiomics and clinical, the clinical, radiomics and deep learning models) were developed for PAS disorder diagnosing (existence of PAS and its subtypes).

STATISTICAL TESTS

AUC, ACC, Student's t-test, the Mann-Whitney U test, chi-squared test, dice coefficient, intraclass correlation coefficients, least absolute shrinkage and selection operator regression, receiver operating characteristic curve, calibration curve with the Hosmer-Lemeshow test, decision curve analysis, DeLong test, and McNemar test. P < 0.05 indicated a significant difference.

RESULTS

In PAS diagnosis, the DRC-1 outperformed than other models (AUC = 0.850 and 0.841 in internal and external testing cohorts, respectively). In PAS subtype classification (abnormal adherent placenta and abnormal invasive placenta), DRC-2 model performed similarly with radiologists (P = 0.773 and 0.579 in the internal testing cohort and P = 0.429 and 0.874 in the external testing cohort, respectively).

DATA CONCLUSION

The DRC model offers efficiency and high diagnostic sensitivity in diagnosis, aiding in surgical planning.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Medication-related osteonecrosis of the jaw: a retrospective single center study of recurrence-related factors after surgical treatment

OBJECTIVES

To provide an overview of the features of patients with medication-related osteonecrosis of the jaw (MRONJ) and explore recurrence-related factors after surgery.

MATERIALS AND METHODS

All pathological records of patients diagnosed with osteonecrosis or osteomyelitis of the jaw were reviewed. Only patients who had a history of use of medication related to bone turnover were included. All demographic and clinical characteristics were collected during review. Univariate and logistic regression analyses were performed to evaluate the associations between risk factors and recurrence. A p value < 0.05 was considered to indicate statistical significance in all analyses.

RESULTS

A total of 313 patients were ultimately included. Most patients (89.14%) underwent bone turnover-related treatment due to malignancy. The breast and prostate were the most common locations of primary tumors in females and males, respectively. Almost all MRONJ patients experienced inflammatory symptoms. Recurrence occurred in 55 patients at 60 locations. The total recurrence rate was 16.85%, with no significant differences between the maxilla and mandible. Extensive surgery and flap transfer were strongly related to a lower recurrence risk. Nearly 80% of patients had recurrence-related symptoms within 6 months.

CONCLUSION

When MRONJ is treated with surgical methods, extensive resection and flap transfer can reduce recurrence risk. Six-month follow-up is needed to exclude recurrence after surgery.

CLINICAL RELEVANCE

This study revealed the surgical-related risk factors, such as extensive surgery and flap transfer, when treating MRONJ patients, and 6-month follow-up is needed to detect recurrence. This could provide clinical guidance for head and neck surgeons.

The Role of Cone Beam Computed Tomography (CBCT) in the Diagnosis and Clinical Management of Medication-Related Osteonecrosis of the Jaw (MRONJ)

Medication-related osteonecrosis of the jaw (MRONJ) is a debilitating condition associated with antiresorptive and antiangiogenic medications that are frequently used in treating osteoporosis and cancers. With the ability to produce high-resolution images with a lower radiation dose, cone beam computed tomography (CBCT) is an emerging technology in maxillofacial imaging that offers several advantages in evaluating MRONJ. This review aims to summarise the radiological features of MRONJ as observed via CBCT and highlight its advantages over two-dimensional plain films in assessing MRONJ. CBCT has the capability to detect early MRONJ lesions, characterise the extent and nature of lesions, distinguish MRONJ from other osseous pathologies, and assist in treatment planning. By leveraging the advantages of CBCT, clinicians can enhance their understanding of MRONJ, improve decision making, and ultimately optimize patient care.

Classification of Ameloblastoma, Periapical Cyst, and Chronic Suppurative Osteomyelitis with Semi-Supervised Learning: The WaveletFusion-ViT Model Approach

Ameloblastoma (AM), periapical cyst (PC), and chronic suppurative osteomyelitis (CSO) are prevalent maxillofacial diseases with similar imaging characteristics but different treatments, thus making preoperative differential diagnosis crucial. Existing deep learning methods for diagnosis often require manual delineation in tagging the regions of interest (ROIs), which triggers some challenges in practical application. We propose a new model of Wavelet Extraction and Fusion Module with Vision Transformer (WaveletFusion-ViT) for automatic diagnosis using CBCT panoramic images. In this study, 539 samples containing healthy (n = 154), AM (n = 181), PC (n = 102), and CSO (n = 102) were acquired by CBCT for classification, with an additional 2000 healthy samples for pre-training the domain-adaptive network (DAN). The WaveletFusion-ViT model was initialized with pre-trained weights obtained from the DAN and further trained using semi-supervised learning (SSL) methods. After five-fold cross-validation, the model achieved average sensitivity, specificity, accuracy, and AUC scores of 79.60%, 94.48%, 91.47%, and 0.942, respectively. Remarkably, our method achieved 91.47% accuracy using less than 20% labeled samples, surpassing the fully supervised approach's accuracy of 89.05%. Despite these promising results, this study's limitations include a low number of CSO cases and a relatively lower accuracy for this condition, which should be addressed in future research. This research is regarded as an innovative approach as it deviates from the fully supervised learning paradigm typically employed in previous studies. The WaveletFusion-ViT model effectively combines SSL methods to effectively diagnose three types of CBCT panoramic images using only a small portion of labeled data.