Bildfusion von SPECT und CT als präzisierende Diagnostik von malignen Tumoren im Mund-Kiefer-Gesichtsbereich

Synergy between artificial intelligence and precision medicine for computer-assisted oral and maxillofacial surgical planning

OBJECTIVES

The aim of this review was to investigate the application of artificial intelligence (AI) in maxillofacial computer-assisted surgical planning (CASP) workflows with the discussion of limitations and possible future directions.

MATERIALS AND METHODS

An in-depth search of the literature was undertaken to review articles concerned with the application of AI for segmentation, multimodal image registration, virtual surgical planning (VSP), and three-dimensional (3D) printing steps of the maxillofacial CASP workflows.

RESULTS

The existing AI models were trained to address individual steps of CASP, and no single intelligent workflow was found encompassing all steps of the planning process. Segmentation of dentomaxillofacial tissue from computed tomography (CT)/cone-beam CT imaging was the most commonly explored area which could be applicable in a clinical setting. Nevertheless, a lack of generalizability was the main issue, as the majority of models were trained with the data derived from a single device and imaging protocol which might not offer similar performance when considering other devices. In relation to registration, VSP and 3D printing, the presence of inadequate heterogeneous data limits the automatization of these tasks.

CONCLUSION

The synergy between AI and CASP workflows has the potential to improve the planning precision and efficacy. However, there is a need for future studies with big data before the emergent technology finds application in a real clinical setting.

CLINICAL RELEVANCE

The implementation of AI models in maxillofacial CASP workflows could minimize a surgeon's workload and increase efficiency and consistency of the planning process, meanwhile enhancing the patient-specific predictability.

Defining secure surgical bone margins in head and neck squamous cell carcinomas: The diagnostic impact of intraoperative cytological assessment of bone resection margins compared with preoperative imaging

BACKGROUND

Imaging provides crucial staging information for treatment planning of head and neck squamous cell carcinomas (HNSCCs). Despite technical progress in imaging techniques, defining the extent of bone involvement preoperatively remains challenging and requires intraoperative information to control for adequate resection. The intraoperative cytological assessment of the bone resection margins (ICAB) provides information whether bone is infiltrated by carcinoma. The aim of this study was to assess the diagnostic value of preoperative imaging compared with ICAB in order to achieve carcinoma-free bone margins.

MATERIALS AND METHODS

108 HNSCC patients underwent preoperative computed tomography (CT), magnetic resonance imaging (MRI) and orthopantomogram (OPG) for staging and surgical planning. Curative resection was planned based on imaging. Intraoperatively, the resection margins were controlled by ICAB. The diagnostic value of preoperative imaging and ICAB was assessed with reference to the histological findings.

RESULTS

CT showed a sensitivity of 89.7%, specificity of 63.0%, positive predictive value (PPV) of 85.9%, and negative predictive value (NPV) of 70.8%. MRI revealed a sensitivity of 45.5%, specificity of 66.7%, PPV of 71.4% and NPV of 40.0%. OPG-imaging had a sensitivity of 64.7%, specificity of 76.2%, PPV of 81.5%, NPV 57.1%. In comparison, ICAB provided a sensitivity of 78.6%, specificity of 95.7%, PPV 73.3%, and NPV 96.7%. The accuracy was 82.1%, 52.9%, 69.0%, and 93.5% for CT, MRI, OPG, and ICAB, respectively.

CONCLUSION

Preoperative imaging lacks accuracy in defining adequate bone resection margins, compared with ICAB. ICAB supports preoperative imaging and intraoperative frozen sections to improve bone margin control.