A user-friendly deep learning application for accurate lung cancer diagnosis.

BACKGROUND: Accurate diagnosis and subsequent delineated treatment planning require the experience of clinicians in the handling of their case numbers. However, applying deep learning in image processing is useful in creating tools that promise faster high-quality diagnoses, but the accuracy and precision of 3-D image processing from 2-D data may be limited by factors such as superposition of organs, distortion and magnification, and detection of new pathologies. The purpose of this research is to use radiomics and deep learning to develop a tool for lung cancer diagnosis. METHODS: This study applies radiomics and deep learning in the diagnosis of lung cancer to help clinicians accurately analyze the images and thereby provide the appropriate treatment planning. 86 patients were recruited from Bach Mai Hospital, and 1012 patients were collected from an open-source database. First, deep learning has been applied in the process of segmentation by U-NET and cancer classification via the use of the DenseNet model. Second, the radiomics were applied for measuring and calculating diameter, surface area, and volume. Finally, the hardware also was designed by connecting between Arduino Nano and MFRC522 module for reading data from the tag. In addition, the displayed interface was created on a web platform using Python through Streamlit. RESULTS: The applied segmentation model yielded a validation loss of 0.498, a train loss of 0.27, a cancer classification validation loss of 0.78, and a training accuracy of 0.98. The outcomes of the diagnostic capabilities of lung cancer (recognition and classification of lung cancer from chest CT scans) were quite successful. CONCLUSIONS: The model provided means for storing and updating patients' data directly on the interface which allowed the results to be readily available for the health care providers. The developed system will improve clinical communication and information exchange. Moreover, it can manage efforts by generating correlated and coherent summaries of cancer diagnoses.

Exploring the merits of research performance measures that comply with the San Francisco Declaration on Research Assessment and strategies to overcome barriers of adoption: qualitative interviews with administrators and researchers.

BACKGROUND: In prior research, we identified and prioritized ten measures to assess research performance that comply with the San Francisco Declaration on Research Assessment, a principle adopted worldwide that discourages metrics-based assessment. Given the shift away from assessment based on Journal Impact Factor, we explored potential barriers to implementing and adopting the prioritized measures. METHODS: We identified administrators and researchers across six research institutes, conducted telephone interviews with consenting participants, and used qualitative description and inductive content analysis to derive themes. RESULTS: We interviewed 18 participants: 6 administrators (research institute business managers and directors) and 12 researchers (7 on appointment committees) who varied by career stage (2 early, 5 mid, 5 late). Participants appreciated that the measures were similar to those currently in use, comprehensive, relevant across disciplines, and generated using a rigorous process. They also said the reporting template was easy to understand and use. In contrast, a few administrators thought the measures were not relevant across disciplines. A few participants said it would be time-consuming and difficult to prepare narratives when reporting the measures, and several thought that it would be difficult to objectively evaluate researchers from a different discipline without considerable effort to read their work. Strategies viewed as necessary to overcome barriers and support implementation of the measures included high-level endorsement of the measures, an official launch accompanied by a multi-pronged communication strategy, training for both researchers and evaluators, administrative support or automated reporting for researchers, guidance for evaluators, and sharing of approaches across research institutes. CONCLUSIONS: While participants identified many strengths of the measures, they also identified a few limitations and offered corresponding strategies to address the barriers that we will apply at our organization. Ongoing work is needed to develop a framework to help evaluators translate the measures into an overall assessment. Given little prior research that identified research assessment measures and strategies to support adoption of those measures, this research may be of interest to other organizations that assess the quality and impact of research.

Component placement accuracy in two generations of handheld robotics-assisted knee arthroplasty.

INTRODUCTION: Total knee arthroplasty (TKA) is the gold standard for treatment of end-stage osteoarthritis. Previous studies have shown that successful outcomes following TKA depend on accurate implant alignment and soft tissue balancing. Robotic-assisted TKA have demonstrated improved accuracy in component placement and have been associated with better outcomes and patient satisfaction. This study aims to report on the execution accuracy of two generations of handheld robotic-assisted surgical systems. METHODS: This was a retrospective analysis of TKA procedures with two sequential generations of the same handheld robotic-assisted surgical system. Intra-operative data captured included pre-operative limb deformity, limb axes, range of motion, kinematic balance, and the resulting plan for component placement in three-dimensional space. Patients were stratified based on their preoperative coronal lower limb mechanical alignment (> 3° varus, < 3° varus, < 3° valgus, and ≥ 3° valgus). Measurements of component placement (overall lower limb alignment, medial and lateral flexion gaps, and tibial and femoral resection depths) were assessed using descriptive statistics. RESULTS: A total of 435 patients were included and stratified based on preoperative lower limb alignment: 229 with ≥ 3° varus, 78 with varus < 3° and 58 with valgus < 3°, and 70 with valgus > 3°. The mean difference between planned and achieved alignment in the lower limb valgus patients was < 1° across all groups. Mean differences between planned and achieved medial flexion gap was higher in the > 3° subgroup in the varus patient cohort ([< 3°]: 1.15 ± 1.92 vs. [> 3°]: 1.90 ± 2.57); this value was higher in the < 3° subgroup for valgus patients ([< 3°]: 1.34 ± 1.83 vs. [> 3°]: 0.956 ± 1.65). Average resection depth ranged from 9.46 to 10.4 mm in the posterior medial femur, 9.25 to 9.95 mm in the posterior lateral femur, 7.45 to 8.79 mm in the distal medial femur, 8.22 to 9.18 mm in the distal lateral femur, 6.70 to 7.07 mm in the medial tibial condyle and 6.40 to 7.19 mm in the lateral tibial condyle. Non-inferiority testing demonstrated the newer generation is non-inferior to the older generation. CONCLUSION: Robotic-assisted knee replacement using handheld image-free systems is able to maintain accuracy of component placement. Further investigation of patient reported outcomes as well as long-term implant longevity are needed.

Artificial intelligence in radiotherapy.

Artificial intelligence (AI) has already been implemented widely in the medical field in the recent years. This paper first reviews the background of AI and radiotherapy. Then it explores the basic concepts of different AI algorithms and machine learning methods, such as neural networks, that are available to us today and how they are being implemented in radiotherapy and diagnostic processes, such as medical imaging, treatment planning, patient simulation, quality assurance and radiation dose delivery. It also explores the ongoing research on AI methods that are to be implemented in radiotherapy in the future. The review shows very promising progress and future for AI to be widely used in various areas of radiotherapy. However, basing on various concerns such as availability and security of using big data, and further work on polishing and testing AI algorithms, it is found that we may not ready to use AI primarily in radiotherapy at the moment.

Dosimetric verification of lung phantom calculated by collapsed cone convolution: A Monte Carlo and experimental evaluation.

OBJECTIVE: To evaluate the dose calculation accuracy in the Prowess Panther treatment planning system (TPS) using the collapsed cone convolution (CCC) algorithm. METHODS: The BEAMnrc Monte Carlo (MC) package was used to predict the dose distribution of photon beams produced by the Oncor® linear accelerator (linac). The MC model of an 18 MV photon beam was verified by measurement using a p-type diode dosimeter. Percent depth dose (PDD) and dose profiles were used for comparison based on three field sizes: 5×5, 10×10, and 20×20cm2. The accuracy of the CCC dosimetry was also evaluated using a plan composed of a simple parallel-opposed field (11×16cm2) in a lung phantom comprised of four tissue simulating media namely, lung, soft tissue, bone and spinal cord. The CCC dose calculation accuracy was evaluated by MC simulation and measurements according to the dose difference and 3D gamma analysis. Gamma analysis was carried out through comparison of the Monte Carlo simulation and the TPS calculated dose. RESULTS: Compared to the dosimetric results measured by the Farmer chamber, the CCC algorithm underestimated dose in the planning target volume (PTV), right lung and lung-tissue interface regions by about -0.11%, -1.6 %, and -2.9%, respectively. Moreover, the CCC algorithm underestimated the dose at the PTV, right lung and lung-tissue interface regions in the order of -0.34%, -0.4% and -3.5%, respectively, when compared to the MC simulation. Gamma analysis results showed that the passing rates within the PTV and heterogeneous region were above 59% and 76%. For the right lung and spinal cord, the passing rates were above 80% for all gamma criteria. CONCLUSIONS: This study demonstrates that the CCC algorithm has potential to calculate dose with sufficient accuracy for 3D conformal radiotherapy within the thorax where a significant amount of tissue heterogeneity exists.

Benchmarking of Monte Carlo model of Siemens Oncor® linear accelerator for 18MV photon beam: Determination of initial electron beam parameters.

OBJECTIVE: This study aims to benchmark a Monte Carlo (MC) model of the 18 MV photon beam produced by the Siemens Oncor® linac using the BEAMnrc and DOSXYZnrc codes. METHODS: By matching the percentage depth doses and beam profiles calculated by MC simulations with measurements, the initial electron beam parameters including electron energy, full width at half maximum (spatial FWHM), and mean angular spread were derived for the 10×10 cm2 and 20×20 cm2 field sizes. The MC model of the 18 MV photon beam was then validated against the measurements for different field sizes (5×5, 30×30 and 40×40 cm2) by gamma index analysis. RESULTS: The optimum values for electron energy, spatial FWHM and mean angular spread were 14.2 MeV, 0.08 cm and 0.8 degree, respectively. The MC simulations yielded the comparable measurement results of these optimum parameters. The gamma passing rates (with acceptance criteria of 1% /1 mm) for percentage depth doses were found to be 100% for all field sizes. For cross-line profiles, the gamma passing rates were 100%, 97%, 95%, 96% and 95% for 5×5, 10×10, 20×20, 30×30 and 40×40 cm2 field sizes, respectively. CONCLUSIONS: By validation of the MC model of Siemens Oncor® linac using various field sizes, it was found that both dose profiles of small and large field sizes were very sensitive to the changes in spatial FWHM and mean angular spread of the primary electron beam from the bending magnet. Hence, it is recommended that both small and large field sizes of the 18 MV photon beams should be considered in the Monte Carlo linac modeling.

Dosimetric and Monte Carlo verification of jaws-only IMRT plans calculated by the Collapsed Cone Convolution algorithm for head and neck cancers.

AIM: The aim of this study is to verify the Prowess Panther jaws-only intensity modulated radiation therapy (JO-IMRT) treatment planning (TP) by comparing the TP dose distributions for head-and-neck (H&N) cancer with the ones simulated by Monte Carlo (MC). BACKGROUND: To date, dose distributions planned using JO-IMRT for H&N patients were found superior to the corresponding three-dimensional conformal radiotherapy (3D-CRT) plans. Dosimetry of the JO-IMRT plans were also experimentally verified using an ionization chamber, MapCHECK 2, and Octavius 4D and good agreements were shown. MATERIALS AND METHODS: Dose distributions of 15 JO-IMRT plans of nasopharyngeal patients were recalculated using the EGSnrc Monte Carlo code. The clinical photon beams were simulated using the BEAMnrc. The absorbed dose to patients treated by fixed-field IMRT was computed using the DOSXYZnrc. The simulated dose distributions were then compared with the ones calculated by the Collapsed Cone Convolution (CCC) algorithm on the TPS, using the relative dose error comparison and the gamma index using global methods implemented in PTW-VeriSoft with 3%/3 mm, 2%/2 mm, 1%/1 mm criteria. RESULTS: There is a good agreement between the MC and TPS dose. The average gamma passing rates were 93.3 ± 3.1%, 92.8 ± 3.2%, 92.4 ± 3.4% based on the 3%/3 mm, 2%/2 mm, 1%/1 mm criteria, respectively. CONCLUSIONS: According to the results, it is concluded that the CCC algorithm was adequate for most of the IMRT H&N cases where the target was not immediately adjacent to the critical structures.

Internet-based computer technology on radiotherapy.

Recent rapid development of Internet-based computer technologies has made possible many novel applications in radiation dose delivery. However, translational speed of applying these new technologies in radiotherapy could hardly catch up due to the complex commissioning process and quality assurance protocol. Implementing novel Internet-based technology in radiotherapy requires corresponding design of algorithm and infrastructure of the application, set up of related clinical policies, purchase and development of software and hardware, computer programming and debugging, and national to international collaboration. Although such implementation processes are time consuming, some recent computer advancements in the radiation dose delivery are still noticeable. In this review, we will present the background and concept of some recent Internet-based computer technologies such as cloud computing, big data processing and machine learning, followed by their potential applications in radiotherapy, such as treatment planning and dose delivery. We will also discuss the current progress of these applications and their impacts on radiotherapy. We will explore and evaluate the expected benefits and challenges in implementation as well.

A surface energy spectral study on the bone heterogeneity and beam obliquity using the flattened and unflattened photon beams.

AIM: Using flattened and unflattened photon beams, this study investigated the spectral variations of surface photon energy and energy fluence in the bone heterogeneity and beam obliquity. BACKGROUND: Surface dose enhancement is a dosimetric concern when using unflattened photon beam in radiotherapy. It is because the unflattened photon beam contains more low-energy photons which are removed by the flattening filter of the flattened photon beam. MATERIALS AND METHODS: We used a water and bone heterogeneity phantom to study the distributions of energy, energy fluence and mean energy of the 6 MV flattened and unflattened photon beams (field size = 10 cm × 10 cm) produced by a Varian TrueBEAM linear accelerator. These elements were calculated at the phantom surfaces using Monte Carlo simulations. The photon energy and energy fluence calculations were repeated with the beam angle turned from 0° to 15°, 30° and 45° in the water and bone phantom. RESULTS: Spectral results at the phantom surfaces showed that the unflattened photon beams contained more photons concentrated mainly in the low-energy range (0-2 MeV) than the flattened beams associated with a flattening filter. With a bone layer of 1 cm under the phantom surface and within the build-up region of the 6 MV photon beam, it is found that both the flattened and unflattened beams had slightly less photons in the energy range <0.4 MeV compared to the water phantom. This shows that the presence of the bone decreased the low-energy photon backscatters to the phantom surface. When both the flattened and unflattened photon beams were rotated from 0° to 45°, the number of photon and mean photon energy increased. This indicates that both photon beams became more hardened or penetrate when the beam angle increased. In the presence of bone, the mean energies of both photon beams increased. This is due to the absorption of low-energy photons by the bone, resulting in more beam hardening. CONCLUSIONS: This study explores the spectral relationships of surface photon energy and energy fluence with bone heterogeneity and beam obliquity for the flattened and unflattened photon beams. The photon spectral information is important in studies on the patient's surface dose enhancement using unflattened photon beams in radiotherapy.

Application of Nanomaterials in Biomedical Imaging and Cancer Therapy II.

Following the successful publication of the first edition of our Special Issue entitled "Application of Nanomaterials in Biomedical Imaging and Cancer Therapy" [...].

Ethical Considerations in Human-Centered AI: Advancing Oncology Chatbots through Large Language Models.

UNSTRUCTURED: The integration of chatbots in oncology underscores the pressing need for human-centered AI that addresses patient and family concerns with empathy and precision. Human-centered AI emphasizes ethical principles, empathy, and user-centric approaches, ensuring technology aligns with human values and needs. This review critically examines the ethical implications of employing Large Language Models (LLMs) like GPT-3 and GPT-4 in oncology chatbots. It examines how these models replicate human-like language patterns, impacting the design of ethical AI systems. The paper identifies key strategies for ethically developing oncology chatbots, focusing on potential biases arising from extensive datasets and neural networks. Specific datasets, such as those sourced from predominantly Western medical literature and patient interactions, may introduce biases by over-representing certain demographic groups. Moreover, the training methodologies of LLMs, including fine-tuning processes, can exacerbate these biases, leading to outputs that may disproportionately favor affluent or Western populations while neglecting marginalized communities. By providing examples of biased outputs in oncology chatbots, the review highlights the ethical challenges LLMs present and the need for mitigation strategies. The study emphasizes integrating human-centric values into AI to mitigate these biases, ultimately advocating for the development of oncology chatbots that are aligned with ethical principles and capable of serving diverse patient populations equitably.

Mechanisms of Action in FLASH Radiotherapy: A Comprehensive Review of Physicochemical and Biological Processes on Cancerous and Normal Cells.

The advent of FLASH radiotherapy (FLASH-RT) has brought forth a paradigm shift in cancer treatment, showcasing remarkable normal cell sparing effects with ultra-high dose rates (>40 Gy/s). This review delves into the multifaceted mechanisms underpinning the efficacy of FLASH effect, examining both physicochemical and biological hypotheses in cell biophysics. The physicochemical process encompasses oxygen depletion, reactive oxygen species, and free radical recombination. In parallel, the biological process explores the FLASH effect on the immune system and on blood vessels in treatment sites such as the brain, lung, gastrointestinal tract, skin, and subcutaneous tissue. This review investigated the selective targeting of cancer cells and the modulation of the tumor microenvironment through FLASH-RT. Examining these mechanisms, we explore the implications and challenges of integrating FLASH-RT into cancer treatment. The potential to spare normal cells, boost the immune response, and modify the tumor vasculature offers new therapeutic strategies. Despite progress in understanding FLASH-RT, this review highlights knowledge gaps, emphasizing the need for further research to optimize its clinical applications. The synthesis of physicochemical and biological insights serves as a comprehensive resource for cell biology, molecular biology, and biophysics researchers and clinicians navigating the evolution of FLASH-RT in cancer therapy.

Impact of Scattering Foil Composition on Electron Energy Distribution in a Clinical Linear Accelerator Modified for FLASH Radiotherapy: A Monte Carlo Study.

This study investigates how scattering foil materials and sampling holder placement affect electron energy distribution in electron beams from a modified medical linear accelerator for FLASH radiotherapy. We analyze electron energy spectra at various positions-ionization chamber, mirror, and jaw-to evaluate the impact of Cu, Pb-Cu, Pb, and Ta foils. Our findings show that close proximity to the source intensifies the dependence of electron energy distribution on foil material, enabling precise beam control through material selection. Monte Carlo simulations are effective for designing foils to achieve desired energy distributions. Moving the sampling holder farther from the source reduces foil material influence, promoting more uniform energy spreads, particularly in the 0.5-10 MeV range for 12 MeV electron beams. These insights emphasize the critical role of tailored material selection and sampling holder positioning in optimizing electron energy distribution and fluence intensity for FLASH radiotherapy research, benefiting both experimental design and clinical applications.

Optimizing Hearing Outcomes in Nasopharyngeal Cancer Survivors in the Era of Modern Radiotherapy and Systemic Therapy.

Intensity-modulated radiation therapy (IMRT) improves disease control and reduces treatment-related toxicity in patients with localized nasopharyngeal carcinoma (NPC). However, due to the proximity of the auditory apparatus to the treatment volume and the frequent incorporation of cisplatin-based chemotherapy, treatment-related sensorineural hearing loss (SNHL) remains a common debilitating complication among NPC survivors. The reported crude incidence of SNHL following IMRT for NPC varies widely at 1-46% due to differences in auditory assessment methods and thresholds, follow-up durations, chemotherapy usage, and patient compositions. International guidelines and radiation dosimetric studies have recommended constraining the cochlear mean dose to less than 44-50 Gy, but the risk of SNHL remains high despite adherence to these constraints. Potential strategies to improve hearing outcomes in NPC survivors include cautious de-escalation of radiotherapy dose and volume, individualization of cochlear constraints, optimization of radiotherapy planning techniques, and the use of substitutes or alternative schedules for cisplatin-based chemotherapy. The addition of immune checkpoint inhibitors to chemoradiotherapy did not impact ototoxicity. Prospective studies that employ both objective and patient-reported auditory outcomes are warranted to test the long-term benefits of various approaches. This article aims to provide a comprehensive review of the incidence and radiation dose-toxicity relationship of SNHL in NPC survivors and to summarize potential strategies to optimize hearing outcomes in relation to nuances in radiotherapy planning and the selection of systemic therapy.

Neurological complications of modern radiotherapy for head and neck cancer.

Radiotherapy is one of the mainstay treatment modalities for the management of non-metastatic head and neck cancer (HNC). Notable improvements in treatment outcomes have been observed in the recent decades. Modern radiotherapy techniques, such as intensity-modulated radiotherapy and charged particle therapy, have significantly improved tumor target conformity and enabled better preservation of normal structures. However, because of the intricate anatomy of the head and neck region, multiple critical neurological structures such as the brain, brainstem, spinal cord, cranial nerves, nerve plexuses, autonomic pathways, brain vasculature, and neurosensory organs, are variably irradiated during treatment, particularly when tumor targets are in close proximity. Consequently, a diverse spectrum of late neurological sequelae may manifest in HNC survivors. These neurological complications commonly result in irreversible symptoms, impair patients' quality of life, and contribute to a substantial proportion of non-cancer deaths. Although the relationship between radiation dose and toxicity has not been fully elucidated for all complications, appropriate application of dosimetric constraints during radiotherapy planning may reduce their incidence. Vigilant surveillance during the course of survivorship also enables early detection and intervention. This article endeavors to provide a comprehensive review of the various neurological complications of modern radiotherapy for HNC, summarize the current incidence data, discuss methods to minimize their risks during radiotherapy planning, and highlight potential strategies for managing these debilitating toxicities.

Dosimetric parameters predict radiation-induced temporal lobe necrosis in nasopharyngeal carcinoma patients: A systematic review and meta-analysis.

This systematic review examines the role of dosimetric parameters in predicting temporal lobe necrosis (TLN) risk in nasopharyngeal carcinoma (NPC) patients treated with three-dimensional conformal RT (3D-CRT), intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT). TLN is a serious late complication that can adversely affect the quality of life of NPC patients. Understanding the relationship between dosimetric parameters and TLN can guide treatment planning and minimize radiation-related complications. A comprehensive search identified relevant studies published up to July 2023. Studies reporting on dosimetric parameters and TLN in NPC patients undergoing 3D-CRT, IMRT, and VMAT were included. TLN incidence, follow-up duration, and correlation with dosimetric parameters of the temporal lobe were analyzed. The review included 30 studies with median follow-up durations ranging from 28 to 110 months. The crude incidence of TLN varied from 2.3 % to 47.3 % and the average crude incidence of TLN is approximately 14 %. Dmax and D1cc emerged as potential predictors of TLN in 3D-CRT and IMRT-treated NPC patients. Threshold values of >72 Gy for Dmax and >62 Gy for D1cc were associated with increased TLN risk. However, other factors should also be considered, including host characteristics, tumor-specific features and therapeutic factors. In conclusion, this systematic review highlights the significance of dosimetric parameters, particularly Dmax and D1cc, in predicting TLN risk in NPC patients undergoing 3D-CRT, IMRT, and VMAT. The findings provide valuable insights that can help in developing optimal treatment planning strategies and contribute to the development of clinical guidelines in this field.

Stereotactic Body Radiotherapy as a Curative Treatment for De Novo Mucosal Carcinoma of the Head and Neck: A Feasible Alternative Option for Fragile Patients with Small Lesion: A Systematic Review.

Stereotactic body radiotherapy (SBRT) is characterized by a high dose per fraction, well-defined small targets, superior dose conformity, and a steep off-target dose gradient. A literature search was conducted to examine the experience with SBRT as a curative treatment for newly diagnosed mucosal carcinoma of the head and neck (MCHN). Four retrospective case series and one prospective phase I clinical trial published between 2012 and 2020 described 124 patients. SBRT was mainly performed in older patients with different tumor sites. The median size of the planning target volumes ranged from 5.3 to 41 cm3. Different approaches were used to create margins. In two studies, limited elective nodal irradiation was performed. The equivalent doses used were 60-83.33 Gy delivered in five fractions. Considerable heterogeneity was observed in the radiation dose specification. The incidence of grade ≥3 late toxicity was 0-8.3%, with local and regional control ranging from 73% to 100%. Improved or stable quality of life after SBRT was reported in two studies. Curative-intent SBRT for de novo MCHN appears to be an effective and relatively safe treatment for small tumor targets, preferably without concomitant elective tissue irradiation. Standardization of SBRT practice and well-designed prospective clinical trials are needed to better define the role of SBRT in this setting.

Multi-domain neurocognitive impairment following definitive intensity-modulated radiotherapy for nasopharyngeal cancer: A cross-sectional study.

INTRODUCTION: Neurocognitive impairment from inadvertent brain irradiation is common following intensity-modulated radiotherapy (IMRT) for nasopharyngeal carcinoma (NPC). This study aimed to determine the prevalence, pattern, and radiation dose-toxicity relationship of this late complication. MATERIALS AND METHODS: We undertook a cross-sectional study of 190 post-IMRT NPC survivors. Neurocognitive function was screened using the Montreal Cognitive Assessment-Hong Kong (HK-MoCA). Detailed assessments of eight distinct neurocognitive domains were conducted: intellectual capacity (WAIS-IV), attention span (Digit Span and Visual Spatial Span), visual memory (Visual Reproduction Span), verbal memory (Auditory Verbal Learning Test), processing speed (Color Trail Test), executive function (Stroop Test), motor dexterity (Grooved Pegboard Test) and language ability (Verbal Fluency Test). The mean percentiles and Z-scores were compared with normative population data. Associations between radiation dose and brain substructures were explored using multivariable logistic regression. RESULTS: The median post-IMRT interval was 7.0 years. The prevalence of impaired HK-MoCA was 25.3 % (48/190). Among the participants, 151 (79.4 %) exhibited impairments in at least one neurocognitive domain. The predominantly impaired domains included verbal memory (short-term: mean Z-score, -0.56, p < 0.001; long-term: mean Z-score, -0.70, p < 0.001), processing speed (basic: mean Z-score, -1.04, p < 0.001; advanced: mean Z-score, -0.38, p < 0.001), executive function (mean Z-score, -1.90, p < 0.001), and motor dexterity (dominant hand: mean Z-score, -0.97, p < 0.001). Radiation dose to the whole brain, hippocampus, and temporal lobe was associated with impairments in executive function, verbal memory, processing speed, and motor dexterity. CONCLUSIONS: Neurocognitive impairment is prevalent and profound in post-IMRT NPC survivors. Cognitive assessment and rehabilitation should be considered part of survivorship care.

Stereotactic Body Radiotherapy in Recurrent and Oligometastatic Head and Neck Tumours.

The treatment of head and neck cancers (HNCs) encompasses a complex paradigm involving a combination of surgery, radiotherapy, and systemic treatment. Locoregional recurrence is a common cause of treatment failure, and few patients are suitable for salvage surgery. Reirradiation with conventional radiation techniques is challenging due to normal tissue tolerance limits and the risk of significant toxicities. Stereotactic body radiotherapy (SBRT) has emerged as a highly conformal modality that offers the potential for cure while limiting the dose to surrounding tissue. There is also growing research that shows that those with oligometastatic disease can benefit from curative intent local ablative therapies such as SBRT. This review will look at published evidence regarding the use of SBRT in locoregional recurrent and oligometastatic HNCs.

Ninth Version of the AJCC and UICC Nasopharyngeal Cancer TNM Staging Classification.

Importance: Accurate staging is a fundamental step in treating patients with nasopharyngeal carcinoma (NPC) worldwide; this is crucial not only for prognostication, but also for guiding treatment decisions. The American Joint Committee on Cancer (AJCC)/Union for International Cancer Control (UICC) tumor-node-metastasis (TNM) system is the global language for clinicians, researchers, and cancer registries. Continual improvement that aligns with contemporary pattern of care is essential. Objective: To improve the prognostic accuracy and clinical applicability of the eighth edition (TNM-8) for NPC. Design, Setting, and Participants: This multicenter study analyzed patients with NPC with detailed tumor features during January 2014 and December 2015 and was reviewed by experienced radiologists. The data analysis was completed in December 2023. The findings were further confirmed with internal and external validation. Statistical analyses and clinical considerations were reviewed by the AJCC/UICC multidisciplinary head and neck panels and attained consensus. The recommendations were evaluated by the AJCC Evidence-Based Medicine Committee before final endorsement as the ninth version (TNM-9). Main Outcomes and Measures: The primary end point was overall survival. Adjusted hazard ratios of different subgroups were then assessed for confirmation of optimal stage grouping. Results: Of the 4914 patients analyzed, 1264 (25.7%) were female and 3650 (74.3%) were male; the median (SD) age was 48.1 (12.0) years. Advanced radiological extranodal extension (with involvement of adjacent muscles, skin, and/or neurovascular bundles) was identified as an independent adverse factor for all end points: this was added as a criterion for N3. Patients with nonmetastatic disease were regrouped into stages I to III instead of TNM-8 stages I to IVA. Significant hazard discrimination was achieved by grouping T1-2N0-1 as stage I, T3/N2 as stage II, and T4/N3 as stage III. Although the T1-2N0-1 subgroups had comparable 5-year overall survival, subdivisions into IA (T1-T2N0) and IB (T1-T2N1) were recommended due to the distinction in adjusted hazard ratios following adjustment for chemotherapy use. Metastatic disease was exclusively classified as stage IV, and prognostication was further refined by subdivision into IVA (M1a, ≤3 lesions) and IVB (M1b, >3 lesions). TNM-9 demonstrated superiority compared with TNM-8 in major statistical aspects. Conclusion and Relevance: The results of this diagnostic study suggest that the ninth version of TNM staging for NPC, based on robust analyses and a comprehensive review by the AJCC/UICC staging committees, provides an improved staging system for global application and a framework for future incorporation of nonanatomical factors. This will be launched for global application in January 2025.