The global incidence of Head and Neck (HN) cancer has dramatically increased over the past few decades, primarily due to an increasing incidence of HPV infection. HPV infection desensitizes cells to apoptosis through the E6-enabled accelerated degradation of several pro-apoptotic molecules, including p53 and procaspase 8. To block this activity, we used 30-hydroxygambogic acid, GA-OH, a small molecule that binds to HPV16 E6 and inhibits the interactions of E6 with its cellular partners. We found that treatment with GA-OH affects the viability of both HPV(+) and HPV(-) oral cancer cells. Further analysis of gene expression patterns of these cell lines showed that GA-OH induces cell death through both independent and overlapping apoptotic pathways by altering gene expression in both HPV(+) and HPV(-) cancer cells.

Feasibility of using geometry-based synthetic dose distributions for Knowledge-based planning model training.

Proton therapy for head-and-neck (HN) cancer offers superior organ-at-risk sparing compared to photon therapy, but is challenged by frequent anatomical changes during treatment. These changes need to be monitored with routine verification CTs (vCTs), which are used to trigger adaptive replans when deemed necessary by the clinical team. To investigate whether nominal plan robustness evaluation (RE) data-specifically the magnitude and spatial characteristics of high-dose regions (hotspots)-can predict the development of clinically significant hotspots on verification CTs (vCTs) and guide planning strategies that minimize the need for adaptive replanning. This retrospective study analyzed 46 patients with p16-positive oropharyngeal cancer treated with proton therapy. Clinical treatment plans were robustly evaluated using 12 uncertainty scenarios combining 3mm setup and±3.5% range errors. Each plan was recalculated on periodic vCTs throughout the treatment course to assess plan degradation. The maximum RE hotspot magnitude and location were compared with vCT hotspot characteristics. A subset of five cases underwent proof-of-concept replanning to reduce RE hotspots and assess downstream vCT dose effects. Patients requiring adaptive replanning due to vCT hotspots had significantly higher RE hotspot magnitudes of the nominal plan compared to those who did not (p=0.008). For replanned cases, higher RE hotspots were moderately correlated with closer proximity of RE and vCT hotspots (r=-0.59, p=0.009). Across all patients, a modest correlation (r=0.58, p<0.001) was observed between RE and vCT hotspot magnitudes. Further, the rate of plan degradation over the course of treatment via hotspot formation was found to increase with increasing RE hotspot magnitude. Replanning to reduce RE hotspots led to an average 5.6% reduction in vCT hotspot dose for the five patients studied, suggesting that reducing RE hotspots may reduce the frequency of replans. Nominal plan robustness evaluation is predictive of both the magnitude and location of hotspots observed on vCTs, and plans with higher RE hotspots tend to degrade faster over the treatment course. Minimizing RE hotspots during treatment planning may reduce the need for adaptive replanning and enhance clinical workflow efficiency.

Reducing re-setups and treatment time in head and neck proton therapy: The role of quantitative imaging protocol.

Despite the widespread clinical adoption of volumetric modulated arc therapy (VMAT), advances in its fundamental optimization methodology have remained relatively limited, particularly with respect to open and researcher-accessible optimization frameworks. This study introduces a novel machine learning (ML) inspired approach for VMAT optimization, reformulating the problem as a multilayer neural network solvable with modern ML toolkits. In this framework, multileaf collimator (MLC) leaf positions and control-point weights are optimized. They are represented as trainable parameters embedded within parameterized activation functions and the final weighting layer, respectively. The dose-deposition matrix provides a fixed linear mapping. Optimization was performed using PyTorch's built-in L-BFGS optimizer with GPU acceleration. Machine-specific constraints, including maximum dose rate, gantry speed, MLC motion limits, and trajectory smoothness, were incorporated as regularization terms. The framework was evaluated using prostate cases with two arcs and head-and-neck (HN) cases with two and four arcs, with results compared against corresponding benchmark IMRT plans. All VMAT optimizations converged successfully, with stable reduction of total objective values and reasonable trends in machine-related regularization terms. The optimized plans were successfully imported into Eclipse TPS and delivered on a TrueBeam linac without interlocks, confirming deliverability. For prostate cases, two-arc VMAT plans achieved planning target volume (PTV) coverage and organ-at-risk (OAR) sparing comparable to benchmark IMRT plans with similar DVH characteristics. For HN cases, four-arc VMAT plans provided plan quality comparable to benchmark IMRT, and consistently improved target dose conformity and OAR sparing compared with two-arc plans, particularly in regions adjacent to complex target geometries. All observations and comparisons are consistent with established clinical experience on VMAT optimization. The proposed ML based VMAT optimization framework bridges modern machine learning optimization with treatment plan optimization and demonstrates strong potential as a flexible and extensible platform for future algorithmic development and research-driven innovations.

To assess the impact of defacing-based deidentification techniques on reidentification risk and data utility across multimodal imaging in radiation therapy. We applied 4 defacing techniques: biometric_mask, quickshear, mri_reface, and Carina's deidentifier, to imaging from 88 brain patients (magnetic resonance imaging, computed tomography [CT], and RTDose) and 97 head and neck patients (positron emission tomography, CT, and RTDose) in The Cancer Imaging Archive. Reidentification risk was assessed using ArcFace, a deep learning-based facial recognition model, by measuring cosine similarity scores and conducting receiver operating characteristic analysis to distinguish between original and defaced images. Data integrity was evaluated by statistically comparing the volume and image intensity changes between the original and defaced images across 9 critical organs and gross tumor volume. Quickshear provides the highest privacy protection, achieving the lowest area under the curve across imaging modalities (area under the curve, 0.61-0.74), followed by Carina (0.59-0.80). Mri_reface showed moderate protection (0.70-0.91), whereas biometric_mask offered the least (0.76-0.94). Carina preserved structure volumes, and mri_reface produced minor volumetric changes in the eyes (5%) and lens (9%). In contrast, biometric_mask substantially affected the mandible (39%) and oral cavity (69%), whereas quickshear significantly altered multiple structures (10.0%-86.6%). Median changes in mean CT intensity after defacing were -48.3% (interquartile range [IQR], -65.9% to -33.9%) with biometric_mask and -77.7% (IQR, -89.4% to -51.7%) with quickshear in the oral cavity. For eyes, Carina and mri_reface produced changes of +24.5% (IQR, 11.6% to 38.2%) and +54.9% (IQR, 16.2% to 82.3%), respectively. In the brain data set, biometric_mask and quickshear decreased oral cavity Dmean by 0.72 Gy (IQR, 0.33 Gy to 0.89 Gy) and 1.61 Gy (IQR, 0.72 Gy to 2.12 Gy), respectively. Carina and mri_reface reduced eyes Dmax by 2.11 Gy (IQR, 0.00 Gy to 3.39 Gy) and 1.05 Gy (IQR, 0.21 Gy to 1.16 Gy), respectively. A similar trend was observed in the head and neck data set with larger deviations. Carina's deidentifier and mri_reface showed favorable privacy-utility trade-offs relative to facial removal; the optimal choice may vary by application priorities.

Concordance between intraoperative frozen section (FS) and subsequent corresponding permanent section (PS) diagnoses is essential for quality patient care. Reported FS-PS discrepancy rates for head and neck (HN) cases average 3% to 4%, mostly due to inadequate sampling and interpretation errors. We sought to review our experience with FS and concordant PS from HN pathology cases diagnosed at a high-volume university medical center. A concordance review was performed for intraoperative FS and corresponding PS of HN surgical pathology cases over a 3-year period. A focused analysis was performed on FS-PS discordances related to margin status (MS) control. The FS-PS discrepancies were classified as due to "sampling" or "interpretation." In total, 2337 HN cases yielded 11 108 intraoperative FS diagnoses. The FS-PS concordance review identified 143 (1.29%) discrepancies. Of these, 82 (57.4%) were for MS control, with just over half due to "sampling." The FS slides generally represented a single top section taken from the tissue, with "sampling" discrepancies due to discordant diagnoses identified on a deeper cut of tissue on the PS slides. Common "interpretation" discrepancies included overlooking high-grade squamous dysplasia (HGD) present on the FS. Our institutional intraoperative FS-PS discrepancy rate for HN pathology cases is 1.29% and well within the national quality standards. Our FS-PS discrepancy rate for HN cases related to MS control was mostly due to "sampling." Interpretation discordances included failure to report HGD on the FS slide. Based on our review, the FS-PS discrepancies for MS control did not have a major clinical impact on most patients.

Head and neck (HaN) cancer is a common type of cancer. Radiotherapy is used to treat this cancer by targeting cancerous cells while avoiding healthy organs. A precise description of targeted areas and adjacent organs at risk (OARs) is required, which is done using Computed Tomography (CT) images. However, some OARs in the head and neck region are better observed in magnetic resonance (MR) images. Therefore, we propose a fully automated system for OAR segmentation using CT images and other imaging modalities. More specifically, we want to use the patient's CT and MR images to identify 30 organs that may be at risk. We proposed 3D-UNet, a model for volumetric segmentation that accurately captures spatial relationships. The model has to skip connections for feature propagation, improving segmentation. In addition, it can handle multimodal inputs to integrate complementary imaging information for more precise segmentation. Our proposed model achieved a training accuracy of 94.2% and a test accuracy of 94.1% which competes with the related works.

Head and neck cancers (HNCs) are a significant health care issue worldwide. Insufficient data in Libya makes it difficult to fully comprehend its local impact. This study aims to provide a retrospective analysis of the epidemiology of head and neck cancer among cancer patients at the Sebha Oncology Centre (SOC) over a period of 9 years, and to compare the results locally and regionally. The patient’s medical records from 2016 to 2025 were obtained and examined. Age and sex information, as well as the tumor's location, were gathered. The study included 2460 patients’ files; 63 patients were diagnosed with head and neck cancers, with a percentage of 2.5%. The most common type of head and neck cancer was non-Hodgkin lymphoma (46%), followed by thyroid gland cancer (22.2%) and oral cancer (9.6%). The tongue and soft palate were the most anatomical sites for oral cancer (3.2% for each). The least frequent kind of HNCs was salivary gland cancer (3.2%). The male-to-female ratio was 0.75:1, with more female patients having thyroid gland cancer and non-Hodgkin lymphoma, and more male cases having nasopharyngeal and laryngeal cancers. Patients ranged in age from 20 to 86, with the majority of cases occurring between 41 and 60, with predominance of females in this age group. The center had a lower rate of oral cancer compared with local and international studies. To increase public awareness of cancer risks, a comprehensive public education program must be established. Such programs are crucial for reducing prevalence, encouraging early detection, and guaranteeing prompt management.

The FLASH effect can significantly reduce radiation-induced normal tissue damage while maintaining tumour control, but requires ultra-high dose rates and high doses. This work proposes a single-field-uniform-dose-per-fraction simultaneous dose and dose rate optimization (SFUDPF-SDDRO) method for proton FLASH radiotherapy to ensure both dose rate and dose meet FLASH effect thresholds. The SFUDPF method focuses on delivering the prescription dose for each fraction from only a single field instead of multiple fields, which inherently supports the ultra-high dose rate and high dose necessary for the FLASH effect. We performed retrospective FLASH treatment planning utilizing SFUDPF-SDDRO on four clinical head-and-neck (HN) cases for this study. SFUDPF planning involves delivering each prescription fraction (8Gyx5 fx) in 1 beam angle as opposed to multiple beam angles per fraction for IMPT. For each beam delivery, we maximized the FLASH effect in a 1cm expansion of the HN CTV (CTV+1cm) by enforcing FLASH dose-rate and dose thresholds of 40Gy/s and 5Gy, respectively, in this region. The pencil-beam-scanning dose rate (PBSDR) was calculated voxel-wise by modeling the raster-scanning spot trajectory, while neglecting energy switching times under the assumption of a range modulator capable of expanding a single-energy beam into a spread-out Bragg peak (SOBP). Robust optimization at 3mm/3.5% was performed to address setup and range uncertainties. We employed iterative convex relaxation and alternating direction method of multipliers algorithms to solve the non-convex optimization problem posed by the SFUDPF-SDDRO model. The FLASH effect was modelled within this work by multiplying the proton dose with a constant 0.7 dose modification factor for voxels fulfilling the dose-rate and dose thresholds to obtain the FLASH effective dose (FED). Effects of FLASH sparing maximization via SFUDPF-SDDRO are verified by comparing with IMPT and VMAT on plan qualities such as (i) high-dose area sparing, (ii) conformity index (CI), and (iii) OAR doses. FLASH RT via SFUDPF-SDDRO compared with IMPT and VMAT was evaluated for four clinical HN cases with different tumor geometries. When compared with their VMAT counterparts, SFUD-SDDRO achieved a considerable reduction of FED for OAR directly adjacent to the CTV. Specifically in case 1, the brainstem D1% decreased from 87.57% to 62.26%, and the spinal cord D10% decreased from 87.36% to 60.74%; in case 2, the D10% of the carotid decreased from 102.46% to 63.30%; in case 3, the D10%of the oral cavity decreased from 94.72% to 62.66%, and the D10% of the oropharynx decreased from 102.5% to 69.09%; in case 4, the D10% of the oral cavity decreased from 88.56% to 59.81%. The SFUDPF-SDDRO achieved a satisfactory CI in terms of FED, indicating that conformity was not sacrificed to achieve the FLASH effect. The proposed SFUDPF-SDDRO method is feasible and shows potential clinical benefits for FLASH treatment planning. Maximizing the FLASH effect within a 1cm ring around the target substantially limits high-dose spillage and enhances OAR sparing compared with conventional approaches.

This study quantified inter- and intra-fractional setup accuracies in the upright posture and compared them among setups with different immobilization methods. Two and four setups were examined for abdominal and head and neck (HN) cancer treatments, respectively. Fifteen asymptomatic volunteers were positioned to a replicated chair of an upright radiotherapy platform with leg immobilization devices, backrest attachments, thermoplastic masks, and vacuum cushions. The 3D positions of the subject body and masks were monitored by calculating 3D point clouds of 26 surface markers from three camera images. The inter-fractional setup errors were calculated by repeating the same setup five times. The intra-fractional displacements were evaluated while the subjects remained in the setups for 20min. These setup errors and displacements were compared among the setups with different immobilization methods. The intra-fractional displacements were also compared between this study and previous studies in the supine posture. Inter-fractional setup errors in the abdominal setups were reduced from 6.6±3.3 to 3.9±1.7mm by using the masks. The HN setup using both the leg immobilization devices and backrest attachments had the setup errors of 2.9±1.7mm. This was smaller than the setup errors observed in three other HN setups that did not use either or both of the devices together. Intra-fractional displacements of these abdominal and HN setups with the immobilization devices were 1.9±1.1 and 1.8±1.5mm, respectively, which were smaller than those in the other setups. These displacements were equivalent to those in the previous studies. Utilizing the masks increased upright setup accuracy in the abdominal setup. The leg immobilization devices and backrest attachments provided the highest setup accuracy in the upright HN setup. These findings will be useful to expand the applicability of upright radiotherapy for various cancer treatments.

Presence of intravenous contrast on computed tomography (CT) scans is often unreliably documented, especially in large research datasets. FALCON is an open-access fully automated deep learning model enabling large-scale intravenous contrast detection and body part classification for CT scans of the head and neck (HN), chest, abdomen, and pelvis (AP). This study used six independent datasets consisting of 3138 CT scans of the HN, chest, and AP of 3126 patients from five institutions between 1996 and 2023 to train and validate four CNN models for intravenous contrast detection and body part classification. The ground truth of intravenous contrast presence was verified by a radiologist. We used ResNet9 network architecture and integrated the four models into a graphical user interface. We assessed FALCON's performance with F1 scores and compared FALCON's annotation time to manual annotation by human experts. In the external test set containing 1348 scans, the F1 score for intravenous contrast detection was 99.4% (95%CI: 98.8, 99.9) for HN CT, 98.3% (95%CI: 96.9, 99.5) for chest CT, and 98.1% (95%CI: 96.9, 99.1) for AP CT. The F1 score for body part classification alone on unseen data was 100% for HN, chest, and AP CT. Compared to human experts, annotation of a single scan with FALCON required 1.3 s vs. 21 s for HN CT, 1.8 s vs. 33 s for chest CT, and 3.7 s vs. 1.6 s for AP CT. The open-access FALCON model ( https://github.com/FintelmannLabDevelopmentTeam/Falcon ) quickly and reliably detects intravenous contrast and classifies body part on CT scans.

Describe caregiver health literacy and assess its association with mental health outcomes of head and neck (HNC) dyads. This single institution cross-sectional cohort study involved 100 dyads from October 2020 to July 2021. Ordinary least squares regression was used to examine the association between caregiver health literacy using the Health Literacy of Caregivers Scale- Cancer (HLCS-C) and depression (PHQ-8), anxiety (GAD-7), quality of life (UW-QOL), and perceived stress (PSS) among caregivers and care recipients. Higher caregiver scores in the proactiveness domain were linked with lower caregiver PSS scores (β = -0.45, 95% confidence interval [CI]: -0.85 to -0.05). Higher caregiver self-care correlated with higher caregiver depression (β = 0.99, 95% CI: 0.72 to 1.27), anxiety (β = 0.76, 95% CI: 0.39-1.13), and perceived stress (β = 1.06, 95% CI: 0.63 to 1.50). Caregivers having sufficient information about cancer was linked to lower care recipient QOL [physical QOL (β = -3.08, 95% Cl: -4.84 to -1.33); socio-emotional QOL (β = -1.88, 95% CI: -3.54 to -0.22)] and higher care recipient depression (β = 0.80, 95% Cl: 0.18 to 1.41). Caregivers having higher social support was associated with lower care recipient depression (β = -0.59, 95% Cl: -1.12 to -0.07). Our findings suggest that caregiver health literacy may affect HNC mental health outcomes, with certain HLCS-C domains-self-care and adequate information- were linked to poorer outcomes. Encouraging a balanced information seeking behavior and strong social support among caregivers may lessen the psychological burden for both HNC caregivers and patients.

Radiation therapy (RT) is an essential treatment modality for paediatric head and neck (HN) cancer, but irradiation during craniofacial development can cause dentofacial side effects, including facial deformation (FD). This systematic review evaluates the evidence on FD post-RT, including assessment methods, prevalence, and dose-response. A literature search was conducted in PubMed and EMBASE. Studies were included if they reported FD>2 years post-RT on average across the whole cohort in children treated for HN cancer aged 0-18years. Studies were assessed using the Cochrane Childhood Cancer Risk of Bias tool. Of 2766 articles identified, 25 met the inclusion criteria, reporting on 1155 children treated between 1965 and 2022 (median cohort size: 24, range: 6 - 192) participants. Median age at treatment/diagnosis was 4.9years. FD assessment methods were analysed in two categories: objective measures and clinical reporting. Objective measures were further categorised: 1) cephalometric (2D image), 2) external surface (3D), 3) internal anatomy (3D). Clinical reporting included clinician-reported and patient-reported outcomes. Measurement and grading criteria varied considerably. Where it could be calculated, prevalence of FD was between 21% and 97%. In seven of the eight studies which examined dose-response, a range of doses between 24-40Gy was linked to risk of FD, as was age<5-7years at the time of treatment. However, heterogeneity in tumour sites and treatment regimens limited comparability. Additionally, limited data on FD location and severity constrained dose-response study. This review highlights the need for standardised assessment tools and grading criteria that captures both location and severity of FD. Such standardisation would improve clinical reporting and support robust dose-response analysis in future research.

Background: Helical Tomotherapy (HT) enables daily verification of patient positioning using Megavoltage Computed Tomography (MVCT) during each treatment session. Objective: The present study aimed to investigate the effects of Automatic Registration (AR) compared to a combination of Automatic and Manual Registration (AR+MR) on setup errors. Additionally,the study aimed to determine the corresponding Margins of the Planning Target Volume (MPTV).Material and Methods: In this experimental study, a total of 1513 daily MVCT scans were analyzed from September 2020 to January 2024, which were obtained from 71 patients diagnosed with Head and Neck (HN),cervical, and gastrointestinal cancer. The scans were registered with the planning CT to determine the setup errors of the patients. The analysis compares the setup errors betweenthe AR and the AR+MR techniques in translational (X, Y, and Z axes) and rotational directions (RX, RY, and RZ). Additionally, the study calculated the MPTV. Results: In the AR and AR+MR techniques, the translational setup errors were significantly different in the Z-axis for HN patients. For cervical cancer patients, AR and AR+MR exhibitedsignificantly different translational errors across all axes. Furthermore, they also had notable differences in the Y and Z-axis translational errors for Gastro-Intestinal (GI) patients.Regarding the rotational setup errors, a substantial difference was observed in the Z-axis translational error for cervical cancer patients, and in the Y and Z-axes for GI patients. Conclusion: Human assessment after automatic registration helps ensure that the registration is clinically appropriate, especially in circumstances involving deformable patient anatomy.

Abstract Purpose: Head and neck (HN) radiotherapy contour quality directly impacts local control (LC) and survival; however, few departments peer review (PR) contours prior to radiotherapy planning (RTP). This study reports outcomes of a single institution’s formal HN contour PR process. Methods: A formal HN contour PR process was implemented. Prior to RTP, HN radiation oncologists reviewed contours, provided feedback and assigned contour grades as follows: R0 (no change), R1 (minor revision, not high risk) or R2 (major revision; high risk). The PR task was completed and the contour grade was recorded. Cochran-Armitage trend test was performed. Results: Pilot PR process was performed over a 7-month period for 88 patients, followed by a maintenance phase. Contours were graded as follows: R0 ( N = 51), R1 ( N = 20) and R2 ( N = 17). Over time, the number of R2 revisions decreased ( p = 0.0001); month 1 ( N = 7), month 2 ( N = 3), month 3 ( N = 5), month 4 ( N = 2) and months 5–7 ( N = 0). Conversely, the number of R0 revisions improved over time ( p = 0.0203); month 1 ( N = 5), months 2–3 ( N = 9), month 4 ( N = 5), month 5 ( N = 8), month 6 ( N = 12) and month 7 ( N = 3). Each radiation oncologist demonstrated reduction in R2 revisions during the pilot. During maintenance, all 3 radiation oncologists demonstrated low rates of R2 revisions of less than 3 cases per year. Conclusion: Incorporation of HN contour PR into routine clinical workflow is feasible. The collective experience of multiple high-volume HN radiation oncologists led to improved contour quality in the pilot for each radiation oncologist and continued to ensure high quality in the maintenance phase.

Spread-Out Bragg Peak FLASH Radiotherapy for Head and Neck Reirradiation: A Treatment Planning Study.

Inverse consistency error for validating deformable image registration: an explorative study on computational phantoms.

Lymphangiomas are rare benign tumors typically occurring in children's head andneck regions. Pelvic lymphangiomas in adults are extremely uncommon and often mimic ovariantumors, making preoperative diagnosis challenging in peri- and postmenopausal women. A 53-year-old Japanese woman with a duodenal ulcer history developed amulticystic right adnexal mass during follow-up for uterine fibroid (initially diagnosed at age43). The uterine fibroid measured 11 cm on imaging at the start of follow-up, with no abnormalitiesdetected in the adnexa. Magnetic resonance imaging (MRI) performed at age 52 revealed a58×37 mm multicystic mass in the right adnexa, which enlarged to 80 mm over the followingyear. MRI revealed a multicystic mass near the right ovarian vessels ; the left adnexa appearednormal. Surgery included right adnexectomy, total hysterectomy, and left salpingo-oophorectomy.Intraoperatively, a well-defined multicystic lesion was identified along the infundibulopelvicligament with normal right ovary appearance. Histopathological examination confirmed lymphangioma,with positive immunostaining for D2-40 and CD31. The postoperative course was uneventful,with no recurrence at the 3-year follow-up. Pelvic lymphangioma should be considered in the differential diagnosis of multilocularpelvic masses. Comprehensive imaging and histopathological analysis are crucial for accurate diagnosisand management.

LATTICE, a form of spatially fractionated radiation therapy (SFRT) that delivers high-dose peaks and low-dose valleys within the target volume, has been clinically utilized for treating bulky tumors. However, its application to small-to-medium-sized target volumes remains challenging due to beam size limitations. To address the challenge of applying LATTICE radiation therapy to small-to-medium-sized targets, this work proposes a novel proton LATTICE (pLATTICE) modality using minibeams, namely minibeam-pLATTICE, that can extend the LATTICE approach for small-to-medium target volumes. Three minibeam-pLATTICE methods are introduced. (1) M0: a fixed minibeam aperture orientation (e.g., 0°) for all beam angles; (2) M1: alternated minibeam aperture orientations (e.g., between 0° and 90°), for consecutive beam angles; (3) M2: multiple minibeam aperture orientations (e.g., 0° and 90°) for each beam angle. The purpose of M1 or M2 is to correct anisotropic dose distribution at lattice peaks due to the planar spatial modulation of minibeams. For each minibeam-pLATTICE method, an optimization problem is formulated to optimize dose uniformity in target peaks and valleys, as well as dose-volume-histogram-based objectives. This optimization problem is solved using iterative convex relaxation and alternating direction method of multipliers (ADMM). Three minibeam-pLATTICE methods are validated to demonstrate the feasibility of minibeam-pLATTICE for two clinical head-and-neck (HN), one abdominal, and one brain case. The advantages of this modality over conventional beam (CONV) pLATTICE are evaluated by comparing peak-to-valley dose ratio (PVDR) and dose delivered to organs at risk (OAR). All three minibeam-pLATTICE modalities achieved improved plan quality compared to CONV, with M2 yielding the best results. For instance, in one HN case, the following improvements were observed: PVDR increased to 3.73 (M2), compared to 3.27 (CONV), 3.72 (M0), and 3.49 (M1), while the mean dose to the mandible was reduced to 0.18 Gy (M2), compared to 0.33 Gy (CONV), 0.17 Gy (M0), and 0.14 Gy (M1). A novel minibeam-pLATTICE modality is proposed that can generate lattice dose patterns for small-to-medium target volumes, which are not achievable with conventional pLATTICE due to beam size limitations. Peak dose anisotropy due to 1D planar minibeam apertures is corrected through inverse treatment planning with alternating or multiple minibeam apertures per beam angle.