In orthopedic surgery, patient-specific bone plates are used for fixation when conventional bone plates do not fit the specific anatomy of a patient. However, plate failure can occur due to a lack of properly established design parameters that support optimal biomechanical properties of the plate. This review provides an overview of design parameters and biomechanical properties of patient-specific bone plates, which can assist in the design of the optimal plate. A literature search was conducted through PubMed and Embase, resulting in the inclusion of 78 studies, comprising clinical studies using patient-specific bone plates for fracture fixation or experimental studies that evaluated biomechanical properties or design parameters of bone plates. Biomechanical properties of the plates, including elastic stiffness, yield strength, tensile strength, and Poisson’s ratio are influenced by various factors, such as material properties, geometry, interface distance, fixation mechanism, screw pattern, working length and manufacturing techniques. Although variations within studies challenge direct translation of experimental results into clinical practice, this review serves as a useful reference guide to determine which parameters must be carefully considered during the design and manufacturing process to achieve the desired biomechanical properties of a plate for fixation of a specific type of fracture. Graphical Abstract: [Figure not available: see fulltext.]

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Image-to-patient registration in navigated mandibular surgery is complex due to the mobile nature of the mandible compared with other craniofacial bones. As a result, surgical navigation is rarely employed in the mandibular region. This systematic review provides an overview of the different registration methods that are used for surgical navigation of the mandible. A systematic search was performed in the MEDLINE Ovid, Scopus, and Embase databases on March 25, 2021. Search terms included synonyms for mandibular surgery, surgical navigation, and registration methods. Articles about navigated mandibular surgery, where the registration method was explicitly mentioned, were included. The database search yielded a total of 2952 articles, from which 81 articles remained for analysis. Four main registration methods were identified: point registration, surface registration, hybrid registration, and computer vision-based registration. The mobility of the mandible is accounted for by either keeping the mandible in a fixed position during preoperative imaging and surgery, or by tracking the mandibular movements. Although different registration methods are available for navigated mandibular surgery, there is always a trade-off between accuracy, registration time, usability, and invasiveness. Future studies should focus on testing the different methods in larger patient studies and should report the registration accuracy.

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Background and Objectives: In patients with rectal cancer who received neoadjuvant (chemo)radiotherapy, fibrosis is induced in and around the tumor area. As tumors and fibrosis have similar visual and tactile feedback, they are hard to distinguish during surgery. To prevent positive resection margins during surgery and spare healthy tissue, it would be of great benefit to have a real-time tissue classification technology that can be used in vivo. Study Design/Materials and Methods: In this study diffuse reflectance spectroscopy (DRS) was evaluated for real-time tissue classification of tumor and fibrosis. DRS spectra of fibrosis and tumor were obtained on excised rectal specimens. After normalization using the area under the curve, a support vector machine was trained using a 10-fold cross-validation. Results: Using spectra of pure tumor tissue and pure fibrosis tissue, we obtained a mean accuracy of 0.88. This decreased to a mean accuracy of 0.61 when tumor measurements were used in which a layer of healthy tissue, mainly fibrosis, was present between the tumor and the measurement surface. Conclusion: It is possible to distinguish pure fibrosis from pure tumor. However, when the measurements on tumor also involve fibrotic tissue, the classification accuracy decreases. Lasers Surg. Med.

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In vivo data acquisition using fiberoptic diffuse reflectance spectroscopy (DRS) is more complicated and less controlled compared to ex vivo data acquisition. It would be of great benefit if classifiers for in vivo tissue discrimination based on DRS could be trained on data obtained ex vivo. In this study, in vivo and ex vivo DRS measurements are obtained during colorectal cancer surgery. A mixed model statistical analysis is used to examine the differences between the two datasets. Furthermore, classifiers are trained and tested using in vivo and ex vivo data. It is found that with a classifier trained on ex vivo data and tested on in vivo data, similar results are obtained compared to a classifier trained and tested on in vivo data. In conclusion, under the conditions used in this study, classifiers intended for in vivo tissue discrimination can be trained on ex vivo data.@en

Background: In colorectal cancer surgery there is a delicate balance between complete removal of the tumor and sparing as much healthy tissue as possible. Especially in rectal cancer, intraoperative tissue recognition could be of great benefit in preventing positive resection margins and sparing as much healthy tissue as possible. To better guide the surgeon, we evaluated the accuracy of diffuse reflectance spectroscopy (DRS) for tissue characterization during colorectal cancer surgery and determined the added value of DRS when compared to clinical judgement. Methods: DRS spectra were obtained from fat, healthy colorectal wall and tumor tissue during colorectal cancer surgery and results were compared to histopathology examination of the measurement locations. All spectra were first normalized at 800 nm, thereafter two support vector machines (SVM) were trained using a tenfold cross-validation. With the first SVM fat was separated from healthy colorectal wall and tumor tissue, the second SVM distinguished healthy colorectal wall from tumor tissue. Results: Patients were included based on preoperative imaging, indicating advanced local stage colorectal cancer. Based on the measurement results of 32 patients, the classification resulted in a mean accuracy for fat, healthy colorectal wall and tumor of 0.92, 0.89 and 0.95 respectively. If the classification threshold was adjusted such that no false negatives were allowed, the percentage of false positive measurement locations by DRS was 25% compared to 69% by clinical judgement. Conclusion: This study shows the potential of DRS for the use of tissue classification during colorectal cancer surgery. Especially the low false positive rate obtained for a false negative rate of zero shows the added value for the surgeons. Trail registration This trail was performed under approval from the internal review board committee (Dutch Trail Register NTR5315), registered on 04/13/2015, https://www.trialregister.nl/trial/5175.

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Neoadjuvant radiotherapy, as part of the conventional treatment of rectal cancer, can induce fibrotic tissue formation around the tumor. This complicates the exact determination of the tumor borders during surgery, which might increase the chance of positive resection margins. In a previous ex vivo study, we distinguished tumor tissue from healthy rectal wall and fat with an accuracy of 0.95, using diffuse reflectance spectroscopy (DRS). Since this study did not include fibrosis, the aim of the current ex vivo study was to examine whether differentiation of tumor and fibrosis with DRS is possible. DRS measurements from freshly resected specimen of 16 patients were obtained. In eight patients fibrosis was measured, in the other eight patients tumor was measured. The measurements were performed using a DRS probe with a source-detector distance of 2 mm. The spectra were obtained in the wavelength range of 450-1600 nm. Classification of the measurements was done using a support vector machine (SVM) and a set of features extracted from the spectra. The SVM was evaluated using an eight-fold cross-validation, which was repeated ten times. For all repetitions, the area under the ROC curve was greater than 0.85 (mean = 0.87, STD = 0.02). The mean sensitivity and specificity were 0.85 (STD = 0.03) and 0.88 (STD = 0.01) respectively. It can be concluded that tumor tissue can be distinguished from fibrosis based on spectral features from DRS measurements. The next step will be to conduct an in vivo study, to verify these results during surgery.@en

Background and Objectives: There is a clinical need to assess the resection margins of tongue cancer specimens, intraoperatively. In the current ex vivo study, we evaluated the feasibility of hyperspectral diffuse reflectance imaging (HSI) for distinguishing tumor from the healthy tongue tissue. Study Design/Materials and Methods: Fresh surgical specimens (n = 14) of squamous cell carcinoma of the tongue were scanned with two hyperspectral cameras that cover the visible and near-infrared spectrum (400–1,700 nm). Each pixel of the hyperspectral image represents a measure of the diffuse optical reflectance. A neural network was used for tissue-type prediction of the hyperspectral images of the visual and near-infrared data sets separately as well as both data sets combined. Results: HSI was able to distinguish tumor from muscle with a good accuracy. The diagnostic performance of both wavelength ranges (sensitivity/specificity of visual and near-infrared were 84%/80% and 77%/77%, respectively) appears to be comparable and there is no additional benefit of combining the two wavelength ranges (sensitivity and specificity were 83%/76%). Conclusions: HSI has a strong potential for intra-operative assessment of tumor resection margins of squamous cell carcinoma of the tongue. This may optimize surgery, as the entire resection surface can be scanned in a single run and the results can be readily available. Lasers Surg. Med.@en

This ex-vivo study evaluates the feasibility of diffuse reflectance spectroscopy (DRS) for discriminating tumor from healthy tissue, with the aim to develop a technology that can assess resection margins for the presence of tumor cells during oral cavity cancer surgery. Diffuse reflectance spectra were acquired on fresh surgical specimens from 28 patients with oral cavity squamous cell carcinoma. The spectra (400 to 1600 nm) were detected after illuminating tissue with a source fiber at 0.3-, 0.7-, 1.0-, and 2.0-mm distances from a detection fiber, obtaining spectral information from different sampling depths. The spectra were correlated with histopathology. A total of 76 spectra were obtained from tumor tissue and 110 spectra from healthy muscle tissue. The first- and second-order derivatives of the spectra were calculated and a classification algorithm was developed using fivefold cross validation with a linear support vector machine. The best results were obtained by the reflectance measured with a 1-mm source-detector distance (sensitivity, specificity, and accuracy are 89%, 82%, and 86%, respectively). DRS can accurately discriminate tumor from healthy tissue in an ex-vivo setting using a 1-mm source-detector distance. Accurate validation methods are warranted for larger sampling depths to allow for guidance during oral cavity cancer excision.

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Colorectal surgery is the standard treatment for patients with colorectal cancer. To overcome two of the main challenges, the circumferential resection margin and postoperative complications, real-time tissue assessment could be of great benefit during surgery. In this ex vivo study, diffuse reflectance spectroscopy (DRS) was used to differentiate tumor tissue from healthy surrounding tissues in patients with colorectal neoplasia. DRS spectra were obtained from tumor tissue, healthy colon, or rectal wall and fat tissue, for every patient. Data were randomly divided into training (80%) and test (20%) sets. After spectral band selection, the spectra were classified using a quadratic classifier and a linear support vector machine. Of the 38 included patients, 36 had colorectal cancer and 2 had an adenoma. When the classifiers were applied to the test set, colorectal cancer could be discriminated from healthy tissue with an overall accuracy of 0.95 (±0.03). This study demonstrates the possibility to separate colorectal cancer from healthy surrounding tissue by applying DRS. High classification accuracies were obtained both in homogeneous and inhomogeneous tissues. This is a fundamental step toward the development of a tool for real-time in vivo tissue assessment during colorectal surgery.

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Background For patients with suspicious lung lesions found on chest x-ray or CT, endo/trans- bronchial biopsy of the lung is the preferred method for obtaining a diagnosis. With the addition of new screening programs, a higher number of patients will require diagnostic biopsy which will prove even more challenging due to the small size of lesions found with screening. There are many endobronchial tools available on the market today and a wide range of new tools under investigation to improve diagnostic yield. However, there is little information available about the optimal tool size required to reach the majority of lesions, especially peripheral ones. In this manuscript we investigate the percentage of lesions that can be reached for various diameter tools if the tools remain inside the airways (i.e. endobronchial biopsy) and the distance a tool must travel “off-road” (or outside of the airways) to reach all lesions. Methods and findings To further understand the distribution of lung lesions with respect to airway sizes and distances from the airways, six 3D models of the lung were generated. The airways were modeled at two different respiratory phases (inspiration and expiration). Three sets of 1,000 lesions were randomly distributed throughout the lung for each respiratory phase. The simulations showed that the percentage of reachable lesions decreases with increasing tool diameter and decreasing lesion diameter. A 1mm diameter tool will reach <25% of 1cm lesions if it remains inside the airways. To reach all 1cm lesions this 1mm tool would have to navigate through the parenchyma up to 8.5mm. CT scans of 21 patient lesions confirm these results reasonably well. Conclusions The smaller the tool diameter the more likely it will be able to reach a lung lesion, whether it be for diagnostic biopsy, ablation, or resection. However, even a 1mm tool is not small enough to reach the majority of small (1-2cm) lesions. Therefore, it is necessary for endobronchial tools to be able to navigate through the parenchyma to reach the majority of lesions.@en