A Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis analysis to evaluate the quality of reporting of postoperative pancreatic fistula prediction models after pancreatoduodenectomy: A systematic review.

BACKGROUND: Postoperative pancreatic fistula is a frequent and potentially lethal complication after pancreatoduodenectomy. Several models have been developed to predict postoperative pancreatic fistula risk. This study was performed to evaluate the quality of reporting of postoperative pancreatic fistula prediction models after pancreatoduodenectomy using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist that provides guidelines on reporting prediction models to enhance transparency and to help in the decision-making regarding the implementation of the appropriate risk models into clinical practice. METHODS: Studies that described prediction models to predict postoperative pancreatic fistula after pancreatoduodenectomy were searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The TRIPOD checklist was used to evaluate the adherence rate. The area under the curve and other performance measures were extracted if reported. A quadrant matrix chart is created to plot the area under the curve against TRIPOD adherence rate to find models with a combination of above-average TRIPOD adherence and area under the curve. RESULTS: In total, 52 predictive models were included (23 development, 15 external validation, 4 incremental value, and 10 development and external validation). No risk model achieved 100% adherence to the TRIPOD. The mean adherence rate was 65%. Most authors failed to report on missing data and actions to blind assessment of predictors. Thirteen models had an above-average performance for TRIPOD checklist adherence and area under the curve. CONCLUSION: Although the average TRIPOD adherence rate for postoperative pancreatic fistula models after pancreatoduodenectomy was 65%, higher compared to other published models, it does not meet TRIPOD standards for transparency. This study identified 13 models that performed above average in TRIPOD adherence and area under the curve, which could be the appropriate models to be used in clinical practice.

External Validation of Mortality Prediction Models for Critical Illness Reveals Preserved Discrimination but Poor Calibration.

OBJECTIVES: In a recent scoping review, we identified 43 mortality prediction models for critically ill patients. We aimed to assess the performances of these models through external validation. DESIGN: Multicenter study. SETTING: External validation of models was performed in the Simple Intensive Care Studies-I (SICS-I) and the Finnish Acute Kidney Injury (FINNAKI) study. PATIENTS: The SICS-I study consisted of 1,075 patients, and the FINNAKI study consisted of 2,901 critically ill patients. MEASUREMENTS AND MAIN RESULTS: For each model, we assessed: 1) the original publications for the data needed for model reconstruction, 2) availability of the variables, 3) model performance in two independent cohorts, and 4) the effects of recalibration on model performance. The models were recalibrated using data of the SICS-I and subsequently validated using data of the FINNAKI study. We evaluated overall model performance using various indexes, including the (scaled) Brier score, discrimination (area under the curve of the receiver operating characteristics), calibration (intercepts and slopes), and decision curves. Eleven models (26%) could be externally validated. The Acute Physiology And Chronic Health Evaluation (APACHE) II, APACHE IV, Simplified Acute Physiology Score (SAPS)-Reduced (SAPS-R)' and Simplified Mortality Score for the ICU models showed the best scaled Brier scores of 0.11' 0.10' 0.10' and 0.06' respectively. SAPS II, APACHE II, and APACHE IV discriminated best; overall discrimination of models ranged from area under the curve of the receiver operating characteristics of 0.63 (0.61-0.66) to 0.83 (0.81-0.85). We observed poor calibration in most models, which improved to at least moderate after recalibration of intercepts and slopes. The decision curve showed a positive net benefit in the 0-60% threshold probability range for APACHE IV and SAPS-R. CONCLUSIONS: In only 11 out of 43 available mortality prediction models, the performance could be studied using two cohorts of critically ill patients. External validation showed that the discriminative ability of APACHE II, APACHE IV, and SAPS II was acceptable to excellent, whereas calibration was poor.

External Validation of a Risk Model for Severe Complications following Pancreatoduodenectomy Based on Three Preoperative Variables.

BACKGROUND: Pancreatoduodenectomy (PD) is the only cure for periampullary and pancreatic cancer. It has morbidity rates of 40-60%, with severe complications in 30%. Prediction models to predict complications are crucial. A risk model for severe complications was developed by Schroder et al. based on BMI, ASA classification and Hounsfield Units of the pancreatic body on the preoperative CT scan. These variables were independent predictors for severe complications upon internal validation. Our aim was to externally validate this model using an independent cohort of patients. METHODS: A retrospective analysis was performed on 318 patients who underwent PD at our institution from 2013 to 2021. The outcome of interest was severe complications Clavien-Dindo ≥ IIIa. Model calibration, discrimination and performance were assessed. RESULTS: A total of 308 patients were included. Patients with incomplete data were excluded. A total of 89 (28.9%) patients had severe complications. The externally validated model achieved: C-index = 0.67 (95% CI: 0.60-0.73), regression coefficient = 0.37, intercept = 0.13, Brier score = 0.25. CONCLUSIONS: The performance ability, discriminative power, and calibration of this model were acceptable. Our risk calculator can help surgeons identify high-risk patients for post-operative complications to improve shared decision-making and tailor perioperative management.

Head-to-head comparison of 14 prediction models for postoperative delirium in elderly non-ICU patients: an external validation study.

OBJECTIVES: Delirium is associated with increased morbidity, mortality, prolonged hospitalisation and increased healthcare costs. The number of clinical prediction models (CPM) to predict postoperative delirium has increased exponentially. Our goal is to perform a head-to-head comparison of CPMs predicting postoperative delirium in non-intensive care unit (non-ICU) elderly patients to identify the best performing models. SETTING: Single-site university hospital. DESIGN: Secondary analysis of prospective cohort study. PARTICIPANTS AND INCLUSION: CPMs published within the timeframe of 1 January 1990 to 1 May 2020 were checked for eligibility (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). For the time period of 1 January 1990 to 1 January 2017, included CPMs were identified in systematic reviews based on prespecified inclusion and exclusion criteria. An extended literature search for original studies was performed independently by two authors, including CPMs published between 1 January 2017 and 1 May 2020. External validation was performed using a surgical cohort consisting of 292 elderly non-ICU patients. PRIMARY OUTCOME MEASURES: Discrimination, calibration and clinical usefulness. RESULTS: 14 CPMs were eligible for analysis out of 366 full texts reviewed. External validation was previously published for 8/14 (57%) CPMs. C-indices ranged from 0.52 to 0.74, intercepts from -0.02 to 0.34, slopes from -0.74 to 1.96 and scaled Brier from -1.29 to 0.088. Based on predefined criteria, the two best performing models were those of Dai et al (c-index: 0.739; (95% CI: 0.664 to 0.813); intercept: -0.018; slope: 1.96; scaled Brier: 0.049) and Litaker et al (c-index: 0.706 (95% CI: 0.590 to 0.823); intercept: -0.015; slope: 0.995; scaled Brier: 0.088). For the remaining CPMs, model discrimination was considered poor with corresponding c-indices <0.70. CONCLUSION: Our head-to-head analysis identified 2 out of 14 CPMs as best-performing models with a fair discrimination and acceptable calibration. Based on our findings, these models might assist physicians in postoperative delirium risk estimation and patient selection for preventive measures.

Optimizing the risk threshold of lymph node involvement for performing extended pelvic lymph node dissection in prostate cancer patients: a cost-effectiveness analysis.

BACKGROUND: Extended pelvic lymph node dissection (ePLND) may be omitted in prostate cancer (CaP) patients with a low predicted risk of lymph node involvement (LNI). The aim of the current study was to quantify the cost-effectiveness of using different risk thresholds for predicted LNI in CaP patients to inform decision making on omitting ePLND. METHODS: Five different thresholds (2%, 5%, 10%, 20%, and 100%) used in practice for performing ePLND were compared using a decision analytic cohort model with the 100% threshold (i.e., no ePLND) as reference. Compared outcomes consisted of quality-adjusted life years (QALYs) and costs. Baseline characteristics for the hypothetical cohort were based on an actual Dutch patient cohort containing 925 patients who underwent ePLND with risks of LNI predicted by the Memorial Sloan Kettering Cancer Center web-calculator. The best strategy was selected based on the incremental cost effectiveness ratio when applying a willingness to pay (WTP) threshold of €20,000 per QALY gained. Probabilistic sensitivity analysis was performed with Monte Carlo simulation to assess the robustness of the results. RESULTS: Costs and health outcomes were lowest (€4,858 and 6.04 QALYs) for the 100% threshold, and highest (€10,939 and 6.21 QALYs) for the 2% threshold, respectively. The incremental cost effectiveness ratio for the 2%, 5%, 10%, and 20% threshold compared with the first threshold above (i.e., 5%, 10%, 20%, and 100%) were €189,222/QALY, €130,689/QALY, €51,920/QALY, and €23,187/QALY respectively. Applying a WTP threshold of €20.000 the probabilities for the 2%, 5%, 10%, 20%, and 100% threshold strategies being cost-effective were 0.0%, 0.3%, 4.9%, 30.3%, and 64.5% respectively. CONCLUSION: Applying a WTP threshold of €20.000, completely omitting ePLND in CaP patients is cost-effective compared to other risk-based strategies. However, applying a 20% threshold for probable LNI to the Briganti 2012 nomogram or the Memorial Sloan Kettering Cancer Center web-calculator, may be a feasible alternative, in particular when higher WTP values are considered.

The use of oximetry and a questionnaire in primary care enables exclusion of a subsequent obstructive sleep apnea diagnosis.

PURPOSE: The study aims to prospectively validate the prognostic value of oximetry alone or combined in a two-step strategy with a questionnaire for the exclusion of obstructive sleep apnea (OSA) in primary care. METHODS: A total of 140 subjects with suspected OSA were included from 54 participating primary care practices. All subjects completed the Philips questionnaire and underwent one night of oximetry prior to referral to a sleep center. The prognostic value of two strategies was evaluated against the diagnosis of the sleep center as the gold standard: (1) assume OSA and subsequently refer to a sleep center if the oxygen desaturation index (ODI) is ≥ 5 and (2) assume OSA and refer to a sleep center if the Philips questionnaire score is ≥ 55% (regardless of the ODI) or if the Philips questionnaire score is < 55% and the ODI is ≥ 5. RESULTS: OSA was diagnosed in the sleep centers in 100 (71%) of the included subjects. Using ODI ≥ 5 alone resulted in a sensitivity of 99.0%, a specificity of 50.0%, a negative predictive value of 95.2%, and a positive predictive value 83.2%. Using the two-step strategy, oximetry would be performed on 39% of the subjects. This strategy resulted in a sensitivity of 100%, a specificity of 35.0%, a negative predictive value of 100%, and a positive predictive value of 79.4%. CONCLUSIONS: In a Dutch primary care population with a clinical suspicion of OSA and low frequency of cardiovascular comorbidities, the use of oximetry alone or combined in a two-step strategy with a questionnaire enables exclusion of a sleep center diagnosis of OSA.

Mortality prediction models in the adult critically ill: A scoping review.

BACKGROUND: Mortality prediction models are applied in the intensive care unit (ICU) to stratify patients into different risk categories and to facilitate benchmarking. To ensure that the correct prediction models are applied for these purposes, the best performing models must be identified. As a first step, we aimed to establish a systematic review of mortality prediction models in critically ill patients. METHODS: Mortality prediction models were searched in four databases using the following criteria: developed for use in adult ICU patients in high-income countries, with mortality as primary or secondary outcome. Characteristics and performance measures of the models were summarized. Performance was presented in terms of discrimination, calibration and overall performance measures presented in the original publication. RESULTS: In total, 43 mortality prediction models were included in the final analysis. In all, 15 models were only internally validated (35%), 13 externally (30%) and 10 (23%) were both internally and externally validated by the original researchers. Discrimination was assessed in 42 models (98%). Commonly used calibration measures were the Hosmer-Lemeshow test (60%) and the calibration plot (28%). Calibration was not assessed in 11 models (26%). Overall performance was assessed in the Brier score (19%) and the Nagelkerke's R2 (4.7%). CONCLUSIONS: Mortality prediction models have varying methodology, and validation and performance of individual models differ. External validation by the original researchers is often lacking and head-to-head comparisons are urgently needed to identify the best performing mortality prediction models for guiding clinical care and research in different settings and populations.

Cost-Effectiveness Analysis of the DiagnOSAS Screening Tool Compared With Polysomnography Diagnosis in Dutch Primary Care.

STUDY OBJECTIVES: The growing recognition of obstructive sleep apnea (OSA) as a serious health condition, increasing waiting lists for sleep tests, and a high proportion of unnecessary referrals from general practice highlight the need for alternative diagnostic strategies for OSA. This study's objective was to investigate the cost-effectiveness of DiagnOSAS, a screening tool that strives to facilitate fast and well-informed referral to hospitals and sleep clinics for diagnosis, in The Netherlands. METHODS: A Markov model was constructed to assess cost-effectiveness in men aged 50 years. The diagnostic process of OSA was simulated with and without DiagnOSAS, taking into account the occurrence of hazardous OSA effects: car accidents, myocardial infarction, and stroke. The cost-effectiveness of "DiagnOSAS Strategy" and a "Rapid Diagnosis Scenario," in which time to diagnosis was halved, was assessed. RESULTS: Base case results show that, within a 10-year time period, DiagnOSAS saves €226 per patient at a negligible decrease (< 0.01) in quality-adjusted life-years (QALYs), resulting in an incremental cost-effectiveness ratio of €56,997/QALY. The "Rapid Diagnosis Scenario" dominates usual care (ie, is both cheaper and more effective). For a willingness-to-pay threshold of €20,000/QALY the probability that the "DiagnOSAS Strategy" and "Rapid Diagnosis Scenario" are cost-effective equals 91.7% and 99.3%, respectively. CONCLUSIONS: DiagnOSAS appears to be a cost-saving alternative for the usual OSA diagnostic strategy in The Netherlands. When DiagnOSAS succeeds in decreasing time to diagnosis, it could substantially improve health outcomes as well.

External Validation of Models Predicting the Probability of Lymph Node Involvement in Prostate Cancer Patients.

BACKGROUND: Multiple statistical models predicting lymph node involvement (LNI) in prostate cancer (PCa) exist to support clinical decision-making regarding extended pelvic lymph node dissection (ePLND). OBJECTIVE: To validate models predicting LNI in Dutch PCa patients. DESIGN, SETTING, AND PARTICIPANTS: Sixteen prediction models were validated using a patient cohort of 1001 men who underwent ePLND. Patient characteristics included serum prostate specific antigen (PSA), cT stage, primary and secondary Gleason scores, number of biopsy cores taken, and number of positive biopsy cores. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Model performance was assessed using the area under the receiver operating characteristic curve (AUC). Calibration plots were used to visualize over- or underestimation by the models. RESULTS AND LIMITATIONS: LNI was identified in 276 patients (28%). Patients with LNI had higher PSA, higher primary Gleason pattern, higher Gleason score, higher number of nodes harvested, higher number of positive biopsy cores, and higher cT stage compared to patients without LNI. Predictions generated by the 2012 Briganti nomogram (AUC 0.76) and the Memorial Sloan Kettering Cancer Center (MSKCC) web calculator (AUC 0.75) were the most accurate. Calibration had a decisive role in selecting the most accurate models because of overlapping confidence intervals for the AUCs. Underestimation of LNI probability in patients had a predicted probability of <20%. The omission of model updating was a limitation of the study. CONCLUSIONS: Models predicting LNI in PCa patients were externally validated in a Dutch patient cohort. The 2012 Briganti and MSKCC nomograms were identified as the most accurate prediction models available. PATIENT SUMMARY: In this report we looked at how well models were able to predict the risk of prostate cancer spreading to the pelvic lymph nodes. We found that two models performed similarly in predicting the most accurate probabilities.

A nomogram to predict the probability of axillary lymph node metastasis in early breast cancer patients with positive axillary ultrasound.

Among patients with a preoperative positive axillary ultrasound, around 40% of them are pathologically proved to be free from axillary lymph node (ALN) metastasis. We aimed to develop and validate a model to predict the probability of ALN metastasis as a preoperative tool to support clinical decision-making. Clinicopathological features of 322 early breast cancer patients with positive axillary ultrasound findings were analyzed. Multivariate logistic regression analysis was performed to identify independent predictors of ALN metastasis. A model was created from the logistic regression analysis, comprising lymph node transverse diameter, cortex thickness, hilum status, clinical tumour size, histological grade and estrogen receptor, and it was subsequently validated in another 234 patients. Coefficient of determination (R(2)) and the area under the ROC curve (AUC) were calculated to be 0.9375 and 0.864, showing good calibration and discrimination of the model, respectively. The false-negative rates of the model were 0% and 5.3% for the predicted probability cut-off points of 7.1% and 13.8%, respectively. This means that omission of axillary surgery may be safe for patients with a predictive probability of less than 13.8%. After further validation in clinical practice, this model may support increasingly limited surgical approaches to the axilla in breast cancer.

Targeted imaging of bacterial infections: advances, hurdles and hopes.

Bacterial infections represent an increasing problem in modern health care, in particular due to ageing populations and accumulating bacterial resistance to antibiotics. Diagnosis is rarely straightforward and consequently treatment is often delayed or indefinite. Therefore, novel tools that can be clinically implemented are urgently needed to accurately and swiftly diagnose infections. Especially, the direct imaging of infections is an attractive option. The challenge of specifically imaging bacterial infections in vivo can be met by targeting bacteria with an imaging agent. Here we review the current status of targeted imaging of bacterial infections, and we discuss advantages and disadvantages of the different approaches. Indeed, significant progress has been made in this field and the clinical implementation of targeted imaging of bacterial infections seems highly feasible. This was recently highlighted by the use of so-called smart activatable probes and a fluorescently labelled derivative of the antibiotic vancomycin. A major challenge remains the selection of the best imaging probes, and we therefore present a set of target selection criteria for clinical implementation of targeted bacterial imaging. Altogether, we conclude that the spectrum of potential applications for targeted bacterial imaging is enormous, ranging from fundamental research on infectious diseases to diagnostic and therapeutic applications.

Antibody-based imaging strategies for cancer.

Although mainly developed for preclinical research and therapeutic use, antibodies have high antigen specificity, which can be used as a courier to selectively deliver a diagnostic probe or therapeutic agent to cancer. It is generally accepted that the optimal antigen for imaging will depend on both the expression in the tumor relative to normal tissue and the homogeneity of expression throughout the tumor mass and between patients. For the purpose of diagnostic imaging, novel antibodies can be developed to target antigens for disease detection, or current FDA-approved antibodies can be repurposed with the covalent addition of an imaging probe. Reuse of therapeutic antibodies for diagnostic purposes reduces translational costs since the safety profile of the antibody is well defined and the agent is already available under conditions suitable for human use. In this review, we will explore a wide range of antibodies and imaging modalities that are being translated to the clinic for cancer identification and surgical treatment.

89Zr-bevacizumab PET imaging in primary breast cancer.

UNLABELLED: Vascular endothelial growth factor (VEGF)-A is overexpressed in most malignant and premalignant breast lesions. VEGF-A can be visualized noninvasively with PET imaging and using the tracer (89)Zr-labeled bevacizumab. In this clinical feasibility study, we assessed whether VEGF-A in primary breast cancer can be visualized by (89)Zr-bevacizumab PET. METHODS: Before surgery, breast cancer patients underwent a PET/CT scan of the breasts and axillary regions 4 d after intravenous administration of 37 MBq of (89)Zr-bevacizumab per 5 mg. PET images were compared with standard imaging modalities. (89)Zr-bevacizumab uptake was quantified as the maximum standardized uptake value (SUV max). VEGF-A levels in tumor and normal breast tissues were assessed with enzyme-linked immunosorbent assay. Data are presented as mean ± SD. RESULTS: Twenty-five of 26 breast tumors (mean size ± SD, 25.1 ± 19.8 mm; range, 4-80 mm) in 23 patients were visualized. SUV max was higher in tumors (1.85 ± 1.22; range, 0.52-5.64) than in normal breasts (0.59 ± 0.37; range, 0.27-1.69; P < 0.001). The only tumor not detected on PET was 10 mm in diameter. Lymph node metastases were present in 10 axillary regions; 4 could be detected with PET (SUV max, 2.66 ± 2.03; range, 1.32-5.68). VEGF-A levels in the 17 assessable tumors were higher than in normal breast tissue in all cases (VEGF-A/mg protein, 184 ± 169 pg vs. 10 ± 21 pg; P = 0.001), whereas (89)Zr-bevacizumab tumor uptake correlated with VEGF-A tumor levels (r = 0.49). CONCLUSION: VEGF-A in primary breast cancer can be visualized by means of (89)Zr-bevacizumab PET.

A validated web-based nomogram for predicting positive surgical margins following breast-conserving surgery as a preoperative tool for clinical decision-making.

BACKGROUND: Breast-conserving therapy, consisting of lumpectomy and adjuvant radiotherapy, is considered standard treatment for early-stage breast cancer. One of the most important risk factors of local recurrence is the presence of positive surgical margins following lumpectomy. We aimed to develop and validate a predictive model (nomogram) to predict for positive margins following the first attempt at lumpectomy as a preoperative tool for clinical decision-making. METHODS: Patients with clinical T1-2N0-1Mx-0 histology-proven invasive breast carcinoma who underwent BCT throughout the North-East region of The Netherlands between June 2008 and July 2009 were selected from the Netherlands Cancer Registry (n = 1185). Results from multivariate logistic regression analyses served as the basis for development of the nomogram. Nomogram calibration and discrimination were assessed graphically and by calculation of a concordance index, respectively. Nomogram performance was validated on an external independent dataset (n = 331) from the University Medical Center Groningen. RESULTS: The final multivariate regression model included clinical, radiological, and pathological variables. Concordance indices were calculated of 0.70 (95% CI: 0.66-0.74) and 0.69 (95% CI: 0.63-0.76) for the modeling and the validation group, respectively. Calibration of the model was considered adequate in both groups. A nomogram was developed as a graphical representation of the model. Moreover, a web-based application (http://www.breastconservation.com) was build to facilitate the use of our nomogram in a clinical setting. CONCLUSION: We developed and validated a nomogram that enables estimation of the preoperative risk of positive margins in breast-conserving surgery. Our nomogram provides a valuable tool for identifying high-risk patients who might benefit from preoperative MRI and/or oncoplastic surgery.

Intraoperative tumor-specific fluorescence imaging in ovarian cancer by folate receptor-α targeting: first in-human results.

The prognosis in advanced-stage ovarian cancer remains poor. Tumor-specific intraoperative fluorescence imaging may improve staging and debulking efforts in cytoreductive surgery and thereby improve prognosis. The overexpression of folate receptor-α (FR-α) in 90-95% of epithelial ovarian cancers prompted the investigation of intraoperative tumor-specific fluorescence imaging in ovarian cancer surgery using an FR-α-targeted fluorescent agent. In patients with ovarian cancer, intraoperative tumor-specific fluorescence imaging with an FR-α-targeted fluorescent agent showcased the potential applications in patients with ovarian cancer for improved intraoperative staging and more radical cytoreductive surgery.

Intraoperative imaging in ovarian cancer: fact or fiction?

Tumor-targeted fluorescence imaging for cancer diagnosis and treatment is an evolving field of research that is on the verge of clinical implementation. As each tumor has its unique biologic profile, selection of the most promising targets is essential. In this review, we focus on target finding in ovarian cancer, a disease in which fluorescence imaging may be of value in both adequate staging and in improving cytoreductive efforts, and as such may have a beneficial effect on prognosis. Thus far, tumor-targeted imaging for ovarian cancer has been applied only in animal models. For clinical implementation, the five most prominent targets were identified: folate receptor α, vascular endothelial growth factor, epidermal growth factor receptor, chemokine receptor 4, and matrix metalloproteinase. These targets were selected based on expression rates in ovarian cancer, availability of an antibody or substrate aimed at the target approved by the Food and Drug Administration, and the likelihood of translation to human use. The purpose of this review is to present requirements for intraoperative imaging and to discuss possible tumor-specific targets for ovarian cancer, prioritizing for targets with substrates ready for introduction into the clinic.

Intraoperative multispectral fluorescence imaging for the detection of the sentinel lymph node in cervical cancer: a novel concept.

PURPOSE: Real-time intraoperative near-infrared fluorescence (NIRF) imaging is a promising technique for lymphatic mapping and sentinel lymph node (SLN) detection. The purpose of this technical feasibility pilot study was to evaluate the applicability of NIRF imaging with indocyanin green (ICG) for the detection of the SLN in cervical cancer. PROCEDURES: In ten patients with early stage cervical cancer, a mixture of patent blue and ICG was injected into the cervix uteri during surgery. Real-time color and fluorescence videos and images were acquired using a custom-made multispectral fluorescence camera system. RESULTS: Real-time fluorescence lymphatic mapping was observed in vivo in six patients; a total of nine SLNs were detected, of which one (11%) contained metastases. Ex vivo fluorescence imaging revealed the remaining fluorescent signal in 11 of 197 non-sentinel LNs (5%), of which one contained metastatic tumor tissue. None of the non-fluorescent LNs contained metastases. CONCLUSIONS: We conclude that lymphatic mapping and detection of the SLN in cervical cancer using intraoperative NIRF imaging is technically feasible. However, the technique needs to be refined for full applicability in cervical cancer in terms of sensitivity and specificity.

Multispectral real-time fluorescence imaging for intraoperative detection of the sentinel lymph node in gynecologic oncology.

The prognosis in virtually all solid tumors depends on the presence or absence of lymph node metastases. Surgical treatment most often combines radical excision of the tumor with a full lymphadenectomy in the drainage area of the tumor. However, removal of lymph nodes is associated with increased morbidity due to infection, wound breakdown and lymphedema. As an alternative, the sentinel lymph node procedure (SLN) was developed several decades ago to detect the first draining lymph node from the tumor. In case of lymphogenic dissemination, the SLN is the first lymph node that is affected (Figure 1). Hence, if the SLN does not contain metastases, downstream lymph nodes will also be free from tumor metastases and need not to be removed. The SLN procedure is part of the treatment for many tumor types, like breast cancer and melanoma, but also for cancer of the vulva and cervix. The current standard methodology for SLN-detection is by peritumoral injection of radiocolloid one day prior to surgery, and a colored dye intraoperatively. Disadvantages of the procedure in cervical and vulvar cancer are multiple injections in the genital area, leading to increased psychological distress for the patient, and the use of radioactive colloid. Multispectral fluorescence imaging is an emerging imaging modality that can be applied intraoperatively without the need for injection of radiocolloid. For intraoperative fluorescence imaging, two components are needed: a fluorescent agent and a quantitative optical system for intraoperative imaging. As a fluorophore we have used indocyanine green (ICG). ICG has been used for many decades to assess cardiac function, cerebral perfusion and liver perfusion. It is an inert drug with a safe pharmaco-biological profile. When excited at around 750 nm, it emits light in the near-infrared spectrum around 800 nm. A custom-made multispectral fluorescence imaging camera system was used. The aim of this video article is to demonstrate the detection of the SLN using intraoperative fluorescence imaging in patients with cervical and vulvar cancer. Fluorescence imaging is used in conjunction with the standard procedure, consisting of radiocolloid and a blue dye. In the future, intraoperative fluorescence imaging might replace the current method and is also easily transferable to other indications like breast cancer and melanoma.