Deepak Gupta

Thymic epithelial tumors (TETs) are rare malignancies with diverse histologic subtypes and complex clinical behavior, necessitating a multidisciplinary approach to care. We conducted a retrospective analysis of 52 patients with TETs treated at a tertiary cancer center, including 41 with thymoma and 11 with thymic carcinoma. The median age was 50 years for thymoma and 56 years for thymic carcinoma. Paraneoplastic syndromes, particularly myasthenia gravis, were more frequent in thymoma (46.3%) than in thymic carcinoma (9.1%). Most patients presented with advanced-stage disease (stage IIIA or higher accounted for 75.7% of thymoma cases and 72.8% of thymic carcinoma cases). Neoadjuvant chemotherapy was administered to 26.8% of thymoma cases and 27.3% of thymic carcinoma cases. Surgical resection was performed in 90.2% of thymoma and 63.6% of thymic carcinoma patients, with complete resection (R0) achieved in 83.8% and 71.4%, respectively. Post-operative radiotherapy (RT) was widely utilized, delivered via volumetric-modulated arc therapy or tomotherapy. Four patients with stage IVA thymoma received hyperthermic intrathoracic chemotherapy after cytoreductive surgery. At a median follow-up of 6 years, the 5-year overall survival for thymoma and thymic carcinoma was 70.0% and 30.7%, respectively, while the 5-year progression-free survival for thymoma and thymic carcinoma was 85.1% and 26%, respectively. Relapse occurred in 17.1% of thymoma and 63.6% of thymic carcinoma patients. Despite the predominance of advanced-stage disease and large tumor burden at presentation, an aggressive, multimodal treatment approach—including high rates of R0 resection, advanced RT techniques (e.g., volumetric modulated arc therapy, tomotherapy), multiline systemic therapy, and selective hyperthermic intrathoracic chemotherapy—demonstrates potential to improve treatment outcomes. Recurrence is the strongest predictor of mortality in both thymoma and thymic carcinoma.

The integration of artificial intelligence (AI) into radiation oncology is significantly enhancing treatment precision and efficiency. However, this technological advancement presents substantial ethical challenges, particularly in the context of informed consent. Traditional consent taking, which relies on physician discretion, must evolve to address the complexities introduced by AI systems. Key ethical considerations include ensuring that patients understand the role of AI in their treatment, transparency in AI-driven decision-making processes, and the protection of patient privacy. A refined approach to informed consent is necessary to navigate these challenges. This approach should prioritize clear communication, emphasize the complementary role of AI in treatment and the decision-making process, and address potential biases inherent in AI algorithms.

Background: Glossopharyngeal Neuralgia (GPN) is a rare and debilitating condition characterized by severe, episodic pain in the distribution of the glossopharyngeal nerve. Traditional treatment options include pharmacological management and invasive surgical procedures, which may not always be effective or suitable for all patients. Stereotactic Radiosurgery (SRS) using the Cyberknife system offers a non-invasive alternative with potential for high precision and efficacy. Objective: This study aims to evaluate the safety and efficacy of Cyberknife-based radiosurgery in the treatment of patients with refractory glossopharyngeal neuralgia. Methods: A retrospective review was conducted on patients diagnosed with glossopharyngeal neuralgia who underwent Cyberknife radiosurgery at our institution. Patient selection criteria included refractory GPN despite medical therapy. Treatment parameters, including target dose and volume, were tailored individually. Outcomes were assessed in terms of pain relief, medication usage, and adverse effects at follow-up intervals. Conclusion: Cyberknife-based radiosurgery appears to be a safe and effective treatment option for patients with refractory glossopharyngeal neuralgia, providing significant pain relief and reducing the need for pharmacological intervention. Further studies with larger sample sizes and longer follow-up periods are warranted to confirm these findings and optimize treatment protocols. Keywords: Glossopharyngeal neuralgia; Cyberknife; Stereotactic radiosurgery; Pain management; Non-invasive treatment

Introduction: Stereotactic body radiotherapy (SBRT) has been found to be an effective and safe modality with excellent oncological outcome in medically inoperable primary renal cell carcinoma (RCC) and oligometastases. There is scarcity of data on the synchronous delivery of SBRT to primary and oligometastatic RCC in patients unfit for nephrectomy. Here, we report the findings of a retrospective study of prospectively collected data on “total ablative SBRT.” Methods: Oligometastatic RCC patients with intact primary tumors were enrolled between May 2021 and June 2022. SBRT was synchronously delivered to the primary tumor and metastases. Demographics, treatment, oncologic outcomes, and toxicity were assessed. Kaplan–Meier estimates were generated for oncologic outcomes. The primary endpoint of this study was feasibility and tolerability. Results: Eleven patients were enrolled between May 2021 and June 2022. One patient died at 2 months after SBRT due to viral pneumonitis (possibly COVID pneumonia). Nine patients (82%) had metastatic disease, while 2 (18%) were stage II. The average maximal diameter of primary was 68.7 mm (range, 23–128 mm). The SBRT doses for primary and metastasis ranged from 40 to 55 Gray (Gy) in 5 to 7 fractions and 22 to 40Gy in 2 to 5 fractions, respectively. The median follow-up period was 10.5 months (Range: 4–15 months). Response assessment was available for ten patients. Local control, marginal control, regional control and initial oligometastatic control (OMC) rates were 100%. OMC declined to 87.5% as one patient had recurrence in irradiated subcarinal lymphnode at 7 months. The metastatic control rate was 80% and loco-regional progression-free survival was 8 months (range, 4–15 months). Toxicities were minimal and manageable. At the last follow–up, 7 of 11 patients were alive with an overall survival of 63.5%. Six patients received systemic therapy after SBRT. Conclusions: Synchronous delivery of SBRT to primary and oligometastatic sites in patients unfit for nephrectomy was feasible and tolerable with good locoregional control. The total ablative SBRT strategy needs to be explored in similar cohorts.

Purpose/Objective(s): Accurate delineation of target is key to any successful radiosurgery. C-arm Dyna CT/ 3DCT angiography (3DCTA) has the potential of improving the accuracy of nidus delineation in intracranial arteriovenous malformations (AVM) due to high temporo-spatial resolution of vessel architecture. Here, we present a comparison of nidus delineation and dosimetric parameters between digital 3DCTA and MRI. Materials/Methods: Ten consecutive patients treated for intracranial AVMs were included in this study. All patients underwent MRI/MRA, and 3DCTA and all images were co-registered. AVM were delineated using 3DCTA (GTV(3DCTA)) and contrast enhanced MRI/MRA (GTV(MRI)). Hausdorff distance (HD) matrices and dice similarity coefficient (DSC) matrices were analysed. Stereotactic radiosurgery plans were developed for both the volumes for all patients and statistical analysis were performed with T-test. Results: Mean volumes of GTV(3DCTA) and GTV(MRI) were 1.771 cc (SD 1.794cc, range 0.124-4.191cc) and 2.183cc (SD 2.16cc, range 0.221-6.133cc), respectively. Significant deviation (p=0.018) was found when taking GTV(MRI) as a primary and comparing it to GTV(3DCTA) (MD=0.723cc +/- 0.816cc). Similar result was observed with GTV(3DCTA) as primary and GTV(MRI) as secondary (MD=0.188cc, SD=0.193cc, p=0.024). Maximum HD was in the range of 1.71 to 7.44mm (mean=4.27mm, SD=1.56). For GTV(3DCTA) based plans, significant deviation was found between GTV(MRI) and GTV(3DCTA) in dose coverage and the mean difference was 22.17% (SD 16.73). In GTV(MRI) based plans, the mean CIRTOG deteriorated from 1.33 to 2.18 for GTV(MRI) and GTV(3DCTA), respectively. Significant deviation was found in CIRTOG (0.005) and mean deviation was 0.86(SD=0.72) when comparing GTV(MRI) and GTV(3DCTA). Highly significant (p=0.002) deviation was found in CIPaddick between GTV(MRI) and GTV(3DCTA) for GTV(MRI) based plans with mean difference of 0.26(SD=0.4, for GTV(MRI)=0.3, GTV(3DCTA)=0.46). Conclusion: Nidus volume was significantly altered with the use of 3DCTA compared to that of MRA/MRI images. Multimodality imaging is crucial for accurate target delineation, and successful radiosurgical obliteration of nidus.

Artificial intelligence (AI) applications, in the form of machine learning and deep learning, are being incorporated into practice in various aspects of medicine, including radiation oncology. Ample evidence from recent publications explores its utility and future use in external beam radiotherapy. However, the discussion on its role in brachytherapy is sparse. This article summarizes available current literature and discusses potential uses of AI in brachytherapy, including future directions. AI has been applied for brachytherapy procedures during almost all steps, starting from decision-making till treatment completion. AI use has led to improvement in efficiency and accuracy by reducing the human errors and saving time in certain aspects. Apart from direct use in brachytherapy, AI also contributes to contemporary advancements in radiology and associated sciences that can affect brachytherapy decisions and treatment. There is a renewal of interest in brachytherapy as a technique in recent years, contributed largely by the understanding that contemporary advances such as intensity modulated radiotherapy and stereotactic external beam radiotherapy cannot match the geometric gains and conformality of brachytherapy, and the integrated efforts of international brachytherapy societies to promote brachytherapy training and awareness. Use of AI technologies may consolidate it further by reducing human effort and time. Prospective validation over larger studies and incorporation of AI technologies for a larger patient population would help improve the efficiency and acceptance of brachytherapy. The enthusiasm favoring AI needs to be balanced against the short duration and quantum of experience with AI in limited patient subsets, need for constant learning and re-learning to train the AI algorithms, and the inevitability of humans having to take responsibility for the correctness and safety of treatments.