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Bakgrund Under 2021 drabbades ungefär 1400 personer i Sverige av en tumör i det centrala nervsystemet, vilket ledde till 675 dödsfall. Hjärntumörer kan orsaka svåra symtom som huvudvärk, aptitförlust, personlighetsförändringar och psykiska problem. Sjuksköterskan ansvarar då för att hjälpa patienten men också att stödja de anhöriga som går igenom en utmanande tid. I och med att patientens självständighet minskar, hamnar anhöriga ofta i en ny roll med ökat ansvar. Detta understryker behovet av att belysa anhörigas upplevelser och stödja dem på ett värdigt sätt. Syfte Syftet med studien var att belysa anhörigvårdarens upplevelser av att vårda en patient med hjärntumör. Metod En icke-systematisk litteraturöversikt baserad på 10 vetenskapliga artiklar. Sökningarna gjordes på PubMed och CINAHL. De valda artiklarna granskades Sophiahemmet högskolas bedömningsmall. En integrerad analys användes för att analysera och sammanställa resultatet. Resultat Fyra kategorier framkom efter analysen av materialet. Dessa var vårdgivarens börda, känslomässiga reaktioner, relationer samt stöd. Resultatet visade att anhöriga hade mycket negativa upplevelser från att ha vårdat sina anhöriga med hjärntumörer. Anhörigvårdare upplevde omfattande förändringar i vardagen, där de fick andra ansvar som de inte haft tidigare vilka krävde tid, energi och tillgänglighet. Denna förändring medförde starka känslomässiga reaktioner, förändringar i relationer och ett stort stödbehov. Slutsats De anhörigas upplevelser var främst negativa och de skulle behöva mer stöd och hjälp för att klara av den befintliga situationen bättre. Litteraturöversikten identifierade att de anhöriga upplevde starka känslomässiga reaktioner, förändringar i relationer, en börda i det ansvar de behöver ta och ett behov av mer stöd. Med denna ökade kunskap och förståelse kan vårdpersonalen hjälpa till och förhoppningsvis också lyckas minska de negativa konsekvenserna., Background During 2021, approximately 1400 people in Sweden were diagnosed with central nervous system tumors, resulting in 675 deaths. Brain tumors can cause severe symptoms such as headaches, decreased appetite, personality changes and psychological problems. Nurses are responsible to help the patient and supporting the family during this challenging time. As the patient's independence diminishes, relatives often find themselves in a new role with increased responsibilities. This highlights the need to illuminate the experiences of family members and support them in a dignified manner. Aim This study aims to explore family caregivers' experiences of taking care of a patient with a brain tumor. Method A non-systematic review of 10 studies was conducted using PubMed and CINAHL databases. The selected articles were reviewed using an assessment template. An integrated analysis was used to analyze and compile the results. Results Four categories emerged after the analysis of the material. These were caregiver burden, emotional reactions, relationships, and support. The results showed that relatives had very negative experiences from caring for their relatives with brain tumors. Family caregivers experienced extensive changes in their everyday lives, where they were given other responsibilities they had not previously had, requiring time, energy, and availability. This change brought strong emotional reactions, changes in relationships, and a great need for support. Conclusions The family's experiences were mainly negative and they would need more support and help to be able to cope with the situation they have been put in in a better way. The literature review identified that the family experienced strong emotional reactions, changes in relationships, a burden in new responsibilities they got and a need for more support. With this increased knowledge and understanding, the healthcare staff can help and hopefully also succeed in reducing the negative consequences.

BACKGROUND: This study assessed the cost-effectiveness of proton beam therapy (PBT) compared to conventional radiotherapy (CRT) for treating patients with brain tumors in Sweden. METHODS: Data from a longitudinal non-randomized study performed between 2015 and 2020 was used, and included adult patients with brain tumors, followed during treatment and through a one-year follow-up. Clinical and demographic data were sourced from the longitudinal study and linked to Swedish national registers to get information on healthcare resource use. A cost-utility framework was used to evaluate the cost-effectiveness of PBT vs. CRT. Patients in PBT group (n = 310) were matched with patients in CRT group (n = 40) on relevant observables using propensity score matching with replacement. Costs were estimated from a healthcare perspective and included costs related to inpatient and specialized outpatient care, and prescribed medications. The health outcome was quality-adjusted life-years (QALYs), derived from the EORTC-QLQ-C30. Generalized linear models (GLM) and two-part models were used to estimate differences in costs and QALYs. RESULTS: PBT yielded higher total costs, 14,639 US$, than CRT, 13,308 US$, with a difference of 1,372 US$ (95% CI, -4,914-7,659) over a 58 weeks' time horizon. Further, PBT resulted in non-significantly lower QALYs, 0.746 compared to CRT, 0.774, with a difference of -0.049 (95% CI, -0.195-0.097). The probability of PBT being cost-effective was < 30% at any willingness to pay. CONCLUSIONS: These results suggest that PBT cannot be considered a cost-effective treatment for brain tumours, compared to CRT.

Background: This study assessed the cost-effectiveness of proton beam therapy (PBT) compared to conventional radiotherapy (CRT) for treating patients with brain tumors in Sweden. Methods: Data from a longitudinal non-randomized study performed between 2015 and 2020 was used, and included adult patients with brain tumors, followed during treatment and through a one-year follow-up. Clinical and demographic data were sourced from the longitudinal study and linked to Swedish national registers to get information on healthcare resource use. A cost-utility framework was used to evaluate the cost-effectiveness of PBT vs. CRT. Patients in PBT group (n = 310) were matched with patients in CRT group (n = 40) on relevant observables using propensity score matching with replacement. Costs were estimated from a healthcare perspective and included costs related to inpatient and specialized outpatient care, and prescribed medications. The health outcome was quality-adjusted life-years (QALYs), derived from the EORTC-QLQ-C30. Generalized linear models (GLM) and two-part models were used to estimate differences in costs and QALYs. Results: PBT yielded higher total costs, 14,639 US$, than CRT, 13,308 US$, with a difference of 1,372 US$ (95% CI, -4,914–7,659) over a 58 weeks’ time horizon. Further, PBT resulted in non-significantly lower QALYs, 0.746 compared to CRT, 0.774, with a difference of -0.049 (95% CI, -0.195–0.097). The probability of PBT being cost-effective was < 30% at any willingness to pay. Conclusions: These results suggest that PBT cannot be considered a cost-effective treatment for brain tumours, compared to CRT. Trial registration: Not applicable.

Introduction: Globally, many regions have an urgent, unmet need of neurosurgical care. A multi-step neurosurgical twinning technique, International Neurosurgical Twinning Modeled for Africa (INTIMA), was proved to be successful during a previous mission to Neurosurgical Unit, Enugu, Nigeria. The Swedish African Neurosurgical Collaboration (SANC) performed a developmental mission together with the local neurosurgical unit in The Gambia, adopting the INTIMA model. Methods: A multidisciplinary team visited for a 2-week collaborative mission at the Neurosurgical Department of the Edward Francis Small Teaching Hospital in Banjul, The Gambia. The mission followed the data of neurosurgical operations during and after the mission as well as about the operations 3 months prior to and after the mission was collected. Results: During the mission, a total of 22 operations was carried out, the most common being degenerative spinal conditions (n = 9). In the 3 months following the mission, 43 operations were performed compared to 24 during the 3 months leading up to the mission. The complexity of the performed procedures increased after the mission. An operating microscope (Moller-Wedel) was donated and installed and the neurosurgeons on site underwent training in microneurosurgery. The surgical nurses, nurses at the postoperative ward, and the physiotherapists underwent training. A biomedical engineer serviced multiple appliances and devices improving the patient care on site while training local technicians. Conclusion: This study validated the use of the INTIMA model previously described in a mission by Swedish African Neurosurgical Collaboration (SANC). The model is sustainable and produces notable results. The core strength of the model is in the multidisciplinary team securing all the aspects and steps of the neurosurgical care. Installation of an operating microscope opened for further microsurgical possibilities, improving the neurosurgical care in The Gambia.

Introduction: Globally, many regions have an urgent, unmet need of neurosurgical care. A multi-step neurosurgical twinning technique, International Neurosurgical Twinning Modeled for Africa (INTIMA), was proved to be successful during a previous mission to Neurosurgical Unit, Enugu, Nigeria. The Swedish African Neurosurgical Collaboration (SANC) performed a developmental mission together with the local neurosurgical unit in The Gambia, adopting the INTIMA model. Methods: A multidisciplinary team visited for a 2-week collaborative mission at the Neurosurgical Department of the Edward Francis Small Teaching Hospital in Banjul, The Gambia. The mission followed the data of neurosurgical operations during and after the mission as well as about the operations 3 months prior to and after the mission was collected. Results: During the mission, a total of 22 operations was carried out, the most common being degenerative spinal conditions (n = 9). In the 3 months following the mission, 43 operations were performed compared to 24 during the 3 months leading up to the mission. The complexity of the performed procedures increased after the mission. An operating microscope (Moller-Wedel) was donated and installed and the neurosurgeons on site underwent training in microneurosurgery. The surgical nurses, nurses at the postoperative ward, and the physiotherapists underwent training. A biomedical engineer serviced multiple appliances and devices improving the patient care on site while training local technicians. Conclusion: This study validated the use of the INTIMA model previously described in a mission by Swedish African Neurosurgical Collaboration (SANC). The model is sustainable and produces notable results. The core strength of the model is in the multidisciplinary team securing all the aspects and steps of the neurosurgical care. Installation of an operating microscope opened for further microsurgical possibilities, improving the neurosurgical care in The Gambia.

Background: This study assessed the cost-effectiveness of proton beam therapy (PBT) compared to conventional radiotherapy (CRT) for treating patients with brain tumors in Sweden. Methods: Data from a longitudinal non-randomized study performed between 2015 and 2020 was used, and included adult patients with brain tumors, followed during treatment and through a one-year follow-up. Clinical and demographic data were sourced from the longitudinal study and linked to Swedish national registers to get information on healthcare resource use. A cost-utility framework was used to evaluate the cost-effectiveness of PBT vs. CRT. Patients in PBT group (n = 310) were matched with patients in CRT group (n = 40) on relevant observables using propensity score matching with replacement. Costs were estimated from a healthcare perspective and included costs related to inpatient and specialized outpatient care, and prescribed medications. The health outcome was quality-adjusted life-years (QALYs), derived from the EORTC-QLQ-C30. Generalized linear models (GLM) and two-part models were used to estimate differences in costs and QALYs. Results: PBT yielded higher total costs, 14,639 US$, than CRT, 13,308 US$, with a difference of 1,372 US$ (95% CI, -4,914–7,659) over a 58 weeks’ time horizon. Further, PBT resulted in non-significantly lower QALYs, 0.746 compared to CRT, 0.774, with a difference of -0.049 (95% CI, -0.195–0.097). The probability of PBT being cost-effective was < 30% at any willingness to pay. Conclusions: These results suggest that PBT cannot be considered a cost-effective treatment for brain tumours, compared to CRT. Trial registration: Not applicable.

Introduction: Globally, many regions have an urgent, unmet need of neurosurgical care. A multi-step neurosurgical twinning technique, International Neurosurgical Twinning Modeled for Africa (INTIMA), was proved to be successful during a previous mission to Neurosurgical Unit, Enugu, Nigeria. The Swedish African Neurosurgical Collaboration (SANC) performed a developmental mission together with the local neurosurgical unit in The Gambia, adopting the INTIMA model. Methods: A multidisciplinary team visited for a 2-week collaborative mission at the Neurosurgical Department of the Edward Francis Small Teaching Hospital in Banjul, The Gambia. The mission followed the data of neurosurgical operations during and after the mission as well as about the operations 3 months prior to and after the mission was collected. Results: During the mission, a total of 22 operations was carried out, the most common being degenerative spinal conditions (n = 9). In the 3 months following the mission, 43 operations were performed compared to 24 during the 3 months leading up to the mission. The complexity of the performed procedures increased after the mission. An operating microscope (Moller-Wedel) was donated and installed and the neurosurgeons on site underwent training in microneurosurgery. The surgical nurses, nurses at the postoperative ward, and the physiotherapists underwent training. A biomedical engineer serviced multiple appliances and devices improving the patient care on site while training local technicians. Conclusion: This study validated the use of the INTIMA model previously described in a mission by Swedish African Neurosurgical Collaboration (SANC). The model is sustainable and produces notable results. The core strength of the model is in the multidisciplinary team securing all the aspects and steps of the neurosurgical care. Installation of an operating microscope opened for further microsurgical possibilities, improving the neurosurgical care in The Gambia.

Bakgrund Under 2021 drabbades ungefär 1400 personer i Sverige av en tumör i det centrala nervsystemet, vilket ledde till 675 dödsfall. Hjärntumörer kan orsaka svåra symtom som huvudvärk, aptitförlust, personlighetsförändringar och psykiska problem. Sjuksköterskan ansvarar då för att hjälpa patienten men också att stödja de anhöriga som går igenom en utmanande tid. I och med att patientens självständighet minskar, hamnar anhöriga ofta i en ny roll med ökat ansvar. Detta understryker behovet av att belysa anhörigas upplevelser och stödja dem på ett värdigt sätt. Syfte Syftet med studien var att belysa anhörigvårdarens upplevelser av att vårda en patient med hjärntumör. Metod En icke-systematisk litteraturöversikt baserad på 10 vetenskapliga artiklar. Sökningarna gjordes på PubMed och CINAHL. De valda artiklarna granskades Sophiahemmet högskolas bedömningsmall. En integrerad analys användes för att analysera och sammanställa resultatet. Resultat Fyra kategorier framkom efter analysen av materialet. Dessa var vårdgivarens börda, känslomässiga reaktioner, relationer samt stöd. Resultatet visade att anhöriga hade mycket negativa upplevelser från att ha vårdat sina anhöriga med hjärntumörer. Anhörigvårdare upplevde omfattande förändringar i vardagen, där de fick andra ansvar som de inte haft tidigare vilka krävde tid, energi och tillgänglighet. Denna förändring medförde starka känslomässiga reaktioner, förändringar i relationer och ett stort stödbehov. Slutsats De anhörigas upplevelser var främst negativa och de skulle behöva mer stöd och hjälp för att klara av den befintliga situationen bättre. Litteraturöversikten identifierade att de anhöriga upplevde starka känslomässiga reaktioner, förändringar i relationer, en börda i det ansvar de behöver ta och ett behov av mer stöd. Med denna ökade kunskap och förståelse kan vårdpersonalen hjälpa till och förhoppningsvis också lyckas minska de negativa konsekvenserna., Background During 2021, approximately 1400 people in Sweden were diagnosed with central nervous system tumors, resulting in 675 deaths. Brain tumors can cause severe symptoms such as headaches, decreased appetite, personality changes and psychological problems. Nurses are responsible to help the patient and supporting the family during this challenging time. As the patient's independence diminishes, relatives often find themselves in a new role with increased responsibilities. This highlights the need to illuminate the experiences of family members and support them in a dignified manner. Aim This study aims to explore family caregivers' experiences of taking care of a patient with a brain tumor. Method A non-systematic review of 10 studies was conducted using PubMed and CINAHL databases. The selected articles were reviewed using an assessment template. An integrated analysis was used to analyze and compile the results. Results Four categories emerged after the analysis of the material. These were caregiver burden, emotional reactions, relationships, and support. The results showed that relatives had very negative experiences from caring for their relatives with brain tumors. Family caregivers experienced extensive changes in their everyday lives, where they were given other responsibilities they had not previously had, requiring time, energy, and availability. This change brought strong emotional reactions, changes in relationships, and a great need for support. Conclusions The family's experiences were mainly negative and they would need more support and help to be able to cope with the situation they have been put in in a better way. The literature review identified that the family experienced strong emotional reactions, changes in relationships, a burden in new responsibilities they got and a need for more support. With this increased knowledge and understanding, the healthcare staff can help and hopefully also succeed in reducing the negative consequences.

Introduction: Globally, many regions have an urgent, unmet need of neurosurgical care. A multi-step neurosurgical twinning technique, International Neurosurgical Twinning Modeled for Africa (INTIMA), was proved to be successful during a previous mission to Neurosurgical Unit, Enugu, Nigeria. The Swedish African Neurosurgical Collaboration (SANC) performed a developmental mission together with the local neurosurgical unit in The Gambia, adopting the INTIMA model. Methods: A multidisciplinary team visited for a 2-week collaborative mission at the Neurosurgical Department of the Edward Francis Small Teaching Hospital in Banjul, The Gambia. The mission followed the data of neurosurgical operations during and after the mission as well as about the operations 3 months prior to and after the mission was collected. Results: During the mission, a total of 22 operations was carried out, the most common being degenerative spinal conditions (n = 9). In the 3 months following the mission, 43 operations were performed compared to 24 during the 3 months leading up to the mission. The complexity of the performed procedures increased after the mission. An operating microscope (Moller-Wedel) was donated and installed and the neurosurgeons on site underwent training in microneurosurgery. The surgical nurses, nurses at the postoperative ward, and the physiotherapists underwent training. A biomedical engineer serviced multiple appliances and devices improving the patient care on site while training local technicians. Conclusion: This study validated the use of the INTIMA model previously described in a mission by Swedish African Neurosurgical Collaboration (SANC). The model is sustainable and produces notable results. The core strength of the model is in the multidisciplinary team securing all the aspects and steps of the neurosurgical care. Installation of an operating microscope opened for further microsurgical possibilities, improving the neurosurgical care in The Gambia.

This study is part of a longitudinal research program, in which patients diagnosed with low-grade gliomas (LGG: n = 13), as well as healthy controls (n = 13), were consecutively recruited and neuropsychologically followed for 7 years. The patients are followed up regardless of variations in treatment. A composite score is used (Global Deficit Score: GDS) included cognitive measures where at least five patients had a negative change: information processing speed, speed of naming, construction ability, verbal fluency, non-verbal thinking, and immediate non-verbal memory. The most important finding in this 7-year follow-up study is that two-thirds of the patients developed cognitive impairment. The remaining third of the patients showed stability in their cognitive ability and were still alive 17 years after diagnosis. Younger patients with tumors in the right frontal or posterior regions showed a more favorable development. Patients with frontal tumors and a declined GDS show also significant changes in executive functions. Given the limited number, no firm conclusions can be drawn regarding the impact of tumor localization. The impact of LGG on cognition and the survival time after diagnosis varies considerably between patients. However, most of the patients (69%) showed cognitive impairment during the seven years we followed them., Financial support was provided by the Research Committee at the Örebro University Hospital and the Regional Research Council of the Uppsala-Örebro Region.

Optic nerve tumors represent approximately 4% of orbital tumors and can occur at any age. In recent years, authors have related this pathology to neurofibromatosis, especially NF-1, which produces multiple manifestations in the ophthalmological field, with gliomas of the pathway being the most frequent tumors of the central nervous system in the disease. They generally have the ability to affect the chiasm and their course is usually indolent for the patient. Among the clinical manifestations, proptosis stands out, which is usually gradual, there is swelling or paleness of the optic disc, loss of visual acuity, loss of visual field and relative afferent pupillary defect due to the compressive effects caused by the tumor. Treatment is conservative. . with close follow-up until progression is detected., Los tumores del nervio óptico representan aproximadamente un 4% de los tumores orbitarios y pueden presentarse a cualquier edad. En los últimos años, autores han relacionado esta patología con la neurofibromatosis, especialmente la NF-1, la cual, produce múltiples manifestaciones en el campo oftalmológico, siendo los gliomas de la vía óptica los tumores del sistema nervioso central más frecuentes en la enfermedad. Generalmente tienen la capacidad de afectar al quiasma y su curso suele ser indolente para el paciente. Dentro de las manifestaciones clínicas destacan la proptosis que suele ser gradual, se presenta hinchazón o palidez del disco óptico, pérdida de agudeza visual, pérdida del campo visual y defecto pupilar aferente relativo debido a los efectos compresivos ocasionados por el tumor, el tratamiento es conservador con seguimiento estrecho hasta que se detecte progresión.

Brain tumour microstructure is potentially predictive of changes following treatment to cognitive functions subserved by the functional networks in which they are embedded. To test this hypothesis, intra-tumoural microstructure was quantified from diffusion-weighted MRI to identify which tumour subregions (if any) had a greater impact on participants' cognitive recovery after surgical resection. Additionally, we studied the role of tumour microstructure in the functional interaction between the tumour and the rest of the brain. Sixteen patients (22-56 years, 7 females) with brain tumours located in or near speech-eloquent areas of the brain were included in the analyses. Two different approaches were adopted for tumour segmentation from a multishell diffusion MRI acquisition: the first used a two-dimensional four group partition of feature space, whilst the second used data-driven clustering with Gaussian mixture modelling. For each approach, we assessed the capability of tumour microstructure to predict participants' cognitive outcomes after surgery and the strength of association between the BOLD signal of individual tumour subregions and the global BOLD signal. With both methodologies, the volumes of partially overlapped subregions within the tumour significantly predicted cognitive decline in verbal skills after surgery. We also found that these particular subregions were among those that showed greater functional interaction with the unaffected cortex. Our results indicate that tumour microstructure measured by MRI multishell diffusion is associated with cognitive recovery after surgery.

Optic nerve tumors represent approximately 4% of orbital tumors and can occur at any age. In recent years, authors have related this pathology to neurofibromatosis, especially NF-1, which produces multiple manifestations in the ophthalmological field, with gliomas of the pathway being the most frequent tumors of the central nervous system in the disease. They generally have the ability to affect the chiasm and their course is usually indolent for the patient. Among the clinical manifestations, proptosis stands out, which is usually gradual, there is swelling or paleness of the optic disc, loss of visual acuity, loss of visual field and relative afferent pupillary defect due to the compressive effects caused by the tumor. Treatment is conservative. . with close follow-up until progression is detected., Los tumores del nervio óptico representan aproximadamente un 4% de los tumores orbitarios y pueden presentarse a cualquier edad. En los últimos años, autores han relacionado esta patología con la neurofibromatosis, especialmente la NF-1, la cual, produce múltiples manifestaciones en el campo oftalmológico, siendo los gliomas de la vía óptica los tumores del sistema nervioso central más frecuentes en la enfermedad. Generalmente tienen la capacidad de afectar al quiasma y su curso suele ser indolente para el paciente. Dentro de las manifestaciones clínicas destacan la proptosis que suele ser gradual, se presenta hinchazón o palidez del disco óptico, pérdida de agudeza visual, pérdida del campo visual y defecto pupilar aferente relativo debido a los efectos compresivos ocasionados por el tumor, el tratamiento es conservador con seguimiento estrecho hasta que se detecte progresión.

Introduction: Globally, many regions have an urgent, unmet need of neurosurgical care. A multi-step neurosurgical twinning technique, International Neurosurgical Twinning Modeled for Africa (INTIMA), was proved to be successful during a previous mission to Neurosurgical Unit, Enugu, Nigeria. The Swedish African Neurosurgical Collaboration (SANC) performed a developmental mission together with the local neurosurgical unit in The Gambia, adopting the INTIMA model. Methods: A multidisciplinary team visited for a 2-week collaborative mission at the Neurosurgical Department of the Edward Francis Small Teaching Hospital in Banjul, The Gambia. The mission followed the data of neurosurgical operations during and after the mission as well as about the operations 3 months prior to and after the mission was collected. Results: During the mission, a total of 22 operations was carried out, the most common being degenerative spinal conditions (n = 9). In the 3 months following the mission, 43 operations were performed compared to 24 during the 3 months leading up to the mission. The complexity of the performed procedures increased after the mission. An operating microscope (Moller-Wedel) was donated and installed and the neurosurgeons on site underwent training in microneurosurgery. The surgical nurses, nurses at the postoperative ward, and the physiotherapists underwent training. A biomedical engineer serviced multiple appliances and devices improving the patient care on site while training local technicians. Conclusion: This study validated the use of the INTIMA model previously described in a mission by Swedish African Neurosurgical Collaboration (SANC). The model is sustainable and produces notable results. The core strength of the model is in the multidisciplinary team securing all the aspects and steps of the neurosurgical care. Installation of an operating microscope opened for further microsurgical possibilities, improving the neurosurgical care in The Gambia.

Brain tumors are a widespread and serious neurological phenomenon that can be life- threatening. The computing field has allowed for the development of artificial intelligence (AI), which can mimic the neural network of the human brain. One use of this technology has been to help researchers capture hidden, high-dimensional images of brain tumors. These images can provide new insights into the nature of brain tumors and help to improve treatment options. AI and precision medicine (PM) are converging to revolutionize healthcare. AI has the potential to improve cancer imaging interpretation in several ways, including more accurate tumor genotyping, more precise delineation of tumor volume, and better prediction of clinical outcomes. AI-assisted brain surgery can be an effective and safe option for treating brain tumors. This review discusses various AI and PM techniques that can be used in brain tumor treatment. These new techniques for the treatment of brain tumors, i.e., genomic profiling, microRNA panels, quantitative imaging, and radiomics, hold great promise for the future. However, there are challenges that must be overcome for these technologies to reach their full potential and improve healthcare.

Brain tumors are a widespread and serious neurological phenomenon that can be life- threatening. The computing field has allowed for the development of artificial intelligence (AI), which can mimic the neural network of the human brain. One use of this technology has been to help researchers capture hidden, high-dimensional images of brain tumors. These images can provide new insights into the nature of brain tumors and help to improve treatment options. AI and precision medicine (PM) are converging to revolutionize healthcare. AI has the potential to improve cancer imaging interpretation in several ways, including more accurate tumor genotyping, more precise delineation of tumor volume, and better prediction of clinical outcomes. AI-assisted brain surgery can be an effective and safe option for treating brain tumors. This review discusses various AI and PM techniques that can be used in brain tumor treatment. These new techniques for the treatment of brain tumors, i.e., genomic profiling, microRNA panels, quantitative imaging, and radiomics, hold great promise for the future. However, there are challenges that must be overcome for these technologies to reach their full potential and improve healthcare.

Brain tumors are a widespread and serious neurological phenomenon that can be life- threatening. The computing field has allowed for the development of artificial intelligence (AI), which can mimic the neural network of the human brain. One use of this technology has been to help researchers capture hidden, high-dimensional images of brain tumors. These images can provide new insights into the nature of brain tumors and help to improve treatment options. AI and precision medicine (PM) are converging to revolutionize healthcare. AI has the potential to improve cancer imaging interpretation in several ways, including more accurate tumor genotyping, more precise delineation of tumor volume, and better prediction of clinical outcomes. AI-assisted brain surgery can be an effective and safe option for treating brain tumors. This review discusses various AI and PM techniques that can be used in brain tumor treatment. These new techniques for the treatment of brain tumors, i.e., genomic profiling, microRNA panels, quantitative imaging, and radiomics, hold great promise for the future. However, there are challenges that must be overcome for these technologies to reach their full potential and improve healthcare.

Brain tumors are a widespread and serious neurological phenomenon that can be life- threatening. The computing field has allowed for the development of artificial intelligence (AI), which can mimic the neural network of the human brain. One use of this technology has been to help researchers capture hidden, high-dimensional images of brain tumors. These images can provide new insights into the nature of brain tumors and help to improve treatment options. AI and precision medicine (PM) are converging to revolutionize healthcare. AI has the potential to improve cancer imaging interpretation in several ways, including more accurate tumor genotyping, more precise delineation of tumor volume, and better prediction of clinical outcomes. AI-assisted brain surgery can be an effective and safe option for treating brain tumors. This review discusses various AI and PM techniques that can be used in brain tumor treatment. These new techniques for the treatment of brain tumors, i.e., genomic profiling, microRNA panels, quantitative imaging, and radiomics, hold great promise for the future. However, there are challenges that must be overcome for these technologies to reach their full potential and improve healthcare.

Pediatric brain and spinal cancers are collectively the leading disease-related cause of death in children; thus, we urgently need curative therapeutic strategies for these tumors. To accelerate such discoveries, the Childrens Brain Tumor Network (CBTN) and Pacific Pediatric Neuro-Oncology Consortium (PNOC) created a systematic process for tumor biobanking, model generation, and sequencing with immediate access to harmonized data. We leverage these data to establish OpenPBTA, an open collaborative project with over 40 scalable analysis modules that genomically characterize 1,074 pediatric brain tumors. Transcriptomic classification reveals universal TP53 dysregulation in mismatch repair-deficient hypermutant high-grade gliomas and TP53 loss as a significant marker for poor overall survival in ependymomas and H3 K28-mutant diffuse midline gliomas. Already being actively applied to other pediatric cancers and PNOC molecular tumor board decision-making, OpenPBTA is an invaluable resource to the pediatric oncology community.

Histotripsy is a non-thermal, non-invasive focused ultrasound therapy using controlled acoustic cavitation to mechanically disintegrate tissue into an acellular homogenate. Histotripsy applies microsecond-length, high pressure (> 10 MPa) pulses to initiate the rapid expansion and collapse of nuclei in a millimeter-scale focal region, applying large stresses and strains to targeted tissues. The cavitation "bubble cloud" generated during histotripsy treatment can be visualized in real time on ultrasound imaging, assisting with treatment guidance and monitoring. Past studies have demonstrated histotripsy's potential for a variety of applications, but histotripsy has not yet been investigated for superficial musculoskeletal tumor ablation. Additionally, preliminary investigations using histotripsy to ablate brain tumors are underway, but require advanced histotripsy devices capable of overcoming attenuation of the therapeutic ultrasound signal by the skull and rely on MRI for real-time guidance. As a result, open questions remain regarding ultrasound-guided histotripsy for brain tumors. Early evidence also suggests that histotripsy ablation may induce immunogenic changes in the tumor microenvironment. Continued research is needed to explain and corroborate these findings under conditions more immunologically representative of human cancers, such as in large animal models with spontaneous tumors. This dissertation investigates the safety and feasibility of using ultrasound-guided histotripsy to ablate superficial soft tissue sarcomas (STS), osteosarcomas (OS), and brain tumors and considers the immunological impacts of histotripsy treatment for STS and OS. The research described herein (1) investigates the ability of histotripsy to treat superficial STS tumors in companion animals with spontaneous tumors, (2) investigates the feasibility of treating bone tumors with histotripsy through a series of ex vivo and in vivo studies, and (3) applies histotripsy for the minimally

PurposeTo develop an MR multitasking-based dynamic imaging for cerebrovascular evaluation (MT-DICE) technique for simultaneous quantification of permeability and leakage-insensitive perfusion with a single-dose contrast injection.MethodsMT-DICE builds on a saturation-recovery prepared multi-echo fast low-angle shot sequence. The k-space is randomly sampled for 7.6 min, with single-dose contrast agent injected 1.5 min into the scan. MR multitasking is used to model the data into six dimensions, including three spatial dimensions for whole-brain coverage, a saturation-recovery time dimension, and a TE dimension for dynamic T1$$ {\mathrm{T}}_1 $$ and T2*$$ {\mathrm{T}}_2^{\ast } $$ quantification, respectively, and a contrast dynamics dimension for capturing contrast kinetics. The derived pixel-wise T1/T2*$$ {\mathrm{T}}_1/{\mathrm{T}}_2^{\ast } $$ time series are converted into contrast concentration-time curves for calculation of kinetic metrics. The technique was assessed for its agreement with reference methods in T1$$ {\mathrm{T}}_1 $$ and T2*$$ {\mathrm{T}}_2^{\ast } $$ measurements in eight healthy subjects and, in three of them, inter-session repeatability of permeability and leakage-insensitive perfusion parameters. Its feasibility was also demonstrated in four patients with brain tumors.ResultsMT-DICE T1/T2*$$ {\mathrm{T}}_1/{\mathrm{T}}_2^{\ast } $$ values of normal gray matter and white matter were in excellent agreement with reference values (intraclass correlation coefficients = 0.860/0.962 for gray matter and 0.925/0.975 for white matter ). Both permeability and perfusion parameters demonstrated good to excellent intersession agreement with the lowest intraclass correlation coefficients at 0.694. Contrast kinetic parameters in all healthy subjects and patients were within the literature range.ConclusionBased on dynamic T1/T2*$$ {\mathrm{T}}_1/{\mathrm{T}}_2^{\ast } $$ mapping, MT-DICE allows for simultaneous quantification of permeabi

In vivo magnetic resonance spectroscopy (MRS) has two modalities, single-voxel (SV) and multivoxel (MV), in which one or more contiguous grids of SVs are acquired. Purpose: To test whether MV grids can be classified with models trained with SV. Methods: Retrospective study. Training dataset: Multicenter multiformat SV INTERPRET, 1.5T. Testing dataset: MV eTumour, 3T. Two classification tasks were completed: 3-class (meningioma vs. aggressive vs. normal) and 4-class (meningioma vs. low-grade glioma vs. aggressive vs. normal). Five different methods were tested for feature selection. The classification was implemented using linear discriminant analysis (LDA), random forest, and support vector machines. The evaluation was completed with balanced error rate (BER) and area under the curve (AUC) on both sets. The accuracy in class prediction was calculated by developing a solid tumor index (STI) and segmentation accuracy with the Dice score. Results: The best method was sequential forward feature selection combined with LDA, with AUCs = 0.95 (meningioma), 0.89 (aggressive), 0.82 (low-grade glioma), and 0.82 (normal). STI was 66% (4-class task) and 71% (3-class task) because two cases failed completely and two more had suboptimal STI as defined by us. Discussion: The reasons for failure in the classification of the MV test set were related to the presence of artifacts., H2020-EU.1.3.—EXCELLENT SCIENCE—Marie Skłodowska-Curie Actions, grant number H2020-MSCA-ITN-2018-813120. Proyectos de investigación en salud 2020, grant numbers PI20/00064 and PI20/00360. Spanish Ministerio de Economía y Competitividad SAF2014-52332-R. Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN [http://www.ciber-bbn.es/en, accessed on 12 January 2023], CB06/01/0010), an initiative of the Instituto de Salud Carlos III (Spain) co-funded by the EU Fondo Europeo de Desarrollo Regional (FEDER). Spanish AEI PID2019-104551RB-I00 grant. Xartecsalut, 2018 XARDI 00,016 and 2021 XARDI 00021. eTUMOUR: FP6-2002-LIFESCIHEALTH- 503094. INTERPRET: FP5-IST-1999-10310., Peer Reviewed, Postprint (published version)

BackgroundMedulloblastoma (MB) is the most common cerebellar malignancy during childhood. Among MB, MYC-amplified Group 3 tumors display the worst prognosis. MYC is an oncogenic transcription factor currently thought to be undruggable. Nevertheless, targeting MYC-dependent processes (i.e. transcription and RNA processing regulation) represents a promising approach.MethodsWe have tested the sensitivity of MYC-driven Group 3 MB cells to a pool of transcription and splicing inhibitors that display a wide spectrum of targets. Among them, we focus on THZ531, an inhibitor of the transcriptional cyclin-dependent kinases (CDK) 12 and 13. High-throughput RNA-sequencing analyses followed by bioinformatics and functional analyses were carried out to elucidate the molecular mechanism(s) underlying the susceptibility of Group 3 MB to CDK12/13 chemical inhibition. Data from International Cancer Genome Consortium (ICGC) and other public databases were mined to evaluate the functional relevance of the cellular pathway/s affected by the treatment with THZ531 in Group 3 MB patients.ResultsWe found that pharmacological inhibition of CDK12/13 is highly selective for MYC-high Group 3 MB cells with respect to MYC-low MB cells. We identified a subset of genes enriched in functional terms related to the DNA damage response (DDR) that are up-regulated in Group 3 MB and repressed by CDK12/13 inhibition. Accordingly, MYC- and CDK12/13-dependent higher expression of DDR genes in Group 3 MB cells limits the toxic effects of endogenous DNA lesions in these cells. More importantly, chemical inhibition of CDK12/13 impaired the DDR and induced irreparable DNA damage exclusively in MYC-high Group 3 MB cells. The augmented sensitivity of MYC-high MB cells to CDK12/13 inhibition relies on the higher elongation rate of the RNA polymerase II in DDR genes. Lastly, combined treatments with THZ531 and DNA damage-inducing agents synergically suppressed viability of MYC-high Group 3 MB cells.ConclusionsOur s

Immunotherapy is a promising treatment modality for pediatric brain tumors, it is important to note that the drugs used can cause side effects that can affect any organ in the body or put the health of patients at risk. Therefore, it is crucial to watch for and recognize symptoms early, thus avoiding exacerbation of these problems, as the side effects of immunotherapy can range from mild and moderate to life-threatening., La inmunoterapia es una modalidad de tratamiento prometedora para los tumores cerebrales pediátricos, es importante tener en cuenta que los medicamentos utilizados pueden ocasionar efectos secundarios que pueden afectar cualquier órgano del cuerpo o poner en riesgo la salud de los pacientes. Por lo tanto, es crucial vigilar y reconocer tempranamente los síntomas, evitando así la exacerbación de estos problemas, ya que los efectos secundarios de la inmunoterapia pueden variar desde leves y moderados hasta potencialmente mortales.

Brain tumor is one of the most fatal diseases that can afflict anyone regardless of gender or age necessitating prompt and accurate treatment as well as early discovery of symptoms. Brain tumors can be identified using Magnetic Resonance Imaging (MRI) to detect abnormal tissue or cell development in the brain and surrounding the brain. Biopsy is another option, but it takes approximately 10 to 15 days after the inspection, so technology is required to classify the image. The goal of this study is to conduct a comparative analysis of the greatest accuracy value attained while classifying using segmentation with thresholding versus segmentation without thresholding on the CNN method. Images are assigned threshold values of 150, 100, and 50. The dataset consists of 7023 MRI scans of four types of brain tumors: glioma, notumor, meningioma, and pituitary. Without utilising thresholding segmentation, the classification yielded the highest degree of accuracy, 92%. At the threshold of 100, classification by segmentation received the highest score of 88%. This demonstrates that thresholding segmentation during CNN model preprocessing is less effective for brain image classification

Brain tumor is one of the most fatal diseases that can afflict anyone regardless of gender or age necessitating prompt and accurate treatment as well as early discovery of symptoms. Brain tumors can be identified using Magnetic Resonance Imaging (MRI) to detect abnormal tissue or cell development in the brain and surrounding the brain. Biopsy is another option, but it takes approximately 10 to 15 days after the inspection, so technology is required to classify the image. The goal of this study is to conduct a comparative analysis of the greatest accuracy value attained while classifying using segmentation with thresholding versus segmentation without thresholding on the CNN method. Images are assigned threshold values of 150, 100, and 50. The dataset consists of 7023 MRI scans of four types of brain tumors: glioma, notumor, meningioma, and pituitary. Without utilising thresholding segmentation, the classification yielded the highest degree of accuracy, 92%. At the threshold of 100, classification by segmentation received the highest score of 88%. This demonstrates that thresholding segmentation during CNN model preprocessing is less effective for brain image classification

Available online 25 January 2023, Previous evidence suggests that distinct ventral and dorsal streams respectively underpin the semantic processing of object and action knowledge. Recently, we found that brain tumor patients with dorsal gliomas in frontoparietal hubs show a selective longitudinal compensation (post-vs. pre-surgery) during the retrieval of lexicosemantic information about actions (but not objects), indexed by power increases in beta rhythms (13–28 Hz). Here, we move one-step further and ask whether a similar organizational principle also stands across the different languages a bilingual speaks. To test this hypothesis, we combined a picture-naming task with MEG recordings and evaluated highly proficient Spanish-Basque bilinguals undergoing surgery for tumor resection in left frontoparietal regions. We assessed patients before and three months after surgery. At the behavioral level, we observed a similar performance across sessions irrespectively of the language at use, suggesting overall successful function preservation. At the oscillatory level, we found longitudinal selective power increases in beta for action naming in Spanish and Basque. Nevertheless, tumor resection triggered a differential reorganization of the L1 and the L2, with the latter one additionally recruiting the right hemisphere. Overall, our results provide evidence for (i) the specific involvement of frontoparietal regions in the semantic retrieval/representation of action knowledge across languages; (ii) a key role of beta oscillations as a signature of language compensation and (iii) the existence of divergent plasticity trajectories in L1 and L2 after surgery. By doing so, they provide new insights into the spectro-temporal dynamics supporting postoperative recovery in the bilingual brain.

Brain tumors are a widespread and serious neurological phenomenon that can be life- threatening. The computing field has allowed for the development of artificial intelligence (AI), which can mimic the neural network of the human brain. One use of this technology has been to help researchers capture hidden, high-dimensional images of brain tumors. These images can provide new insights into the nature of brain tumors and help to improve treatment options. AI and precision medicine (PM) are converging to revolutionize healthcare. AI has the potential to improve cancer imaging interpretation in several ways, including more accurate tumor genotyping, more precise delineation of tumor volume, and better prediction of clinical outcomes. AI-assisted brain surgery can be an effective and safe option for treating brain tumors. This review discusses various AI and PM techniques that can be used in brain tumor treatment. These new techniques for the treatment of brain tumors, i.e., genomic profiling, microRNA panels, quantitative imaging, and radiomics, hold great promise for the future. However, there are challenges that must be overcome for these technologies to reach their full potential and improve healthcare.

Brain tumors are a widespread and serious neurological phenomenon that can be life- threatening. The computing field has allowed for the development of artificial intelligence (AI), which can mimic the neural network of the human brain. One use of this technology has been to help researchers capture hidden, high-dimensional images of brain tumors. These images can provide new insights into the nature of brain tumors and help to improve treatment options. AI and precision medicine (PM) are converging to revolutionize healthcare. AI has the potential to improve cancer imaging interpretation in several ways, including more accurate tumor genotyping, more precise delineation of tumor volume, and better prediction of clinical outcomes. AI-assisted brain surgery can be an effective and safe option for treating brain tumors. This review discusses various AI and PM techniques that can be used in brain tumor treatment. These new techniques for the treatment of brain tumors, i.e., genomic profiling, microRNA panels, quantitative imaging, and radiomics, hold great promise for the future. However, there are challenges that must be overcome for these technologies to reach their full potential and improve healthcare.

In recent years, research on brain cancers has turned towards the study of the interplay between the tumor and its host, the normal brain. Starting from the establishment of a parallelism between neurogenesis and gliomagenesis, the influence of neuronal activity on the development of brain tumors, particularly gliomas, has been partially unveiled. Notably, direct electrochemical synapses between neurons and glioma cells have been identified, paving the way for new approaches for the cure of brain cancers. Since this novel field of study has been defined “cancer neuroscience”, anticancer therapeutic approaches exploiting these discoveries can be referred to as “cancer neuromodulation”. In the present review, we provide an up-to-date description of the novel findings and of the therapeutic neuromodulation perspectives in cancer neuroscience. We focus both on more traditional oncologic approaches, aimed at modulating the major pathways involved in cancer neuroscience through drugs or genetic engineering techniques, and on electric stimulation proposals; the latter is at the cutting-edge of neuro-oncology.

Biology can often be explained by structures and interactions between molecules. It is therefore of importance for medical sciences that the chemical reactions and molecular compositions of biological samples can be measured. The purpose of this thesis is twofold. Firstly, to develop methods for measuring the underlying mechanisms of the lung disorder pulmonary hypertension. Secondly, to investigating the possibility of classifying brain tissue for safer resection of brain tumors. A multimodal approach is often motivated, as there exist many different measurement techniques. In this thesis Raman spectroscopy has been applied both as the main measurement modality and in cooperation with other methods. Raman spectroscopy is a label free optical measurement technique that measure inelastic scattering from materials that are illuminated by a monochromatic light source. Raman scattering results in a weak signal that is uniquely proportional to the chemical structure of the sample. When measuring biological samples, Raman scattering is accompanied by a strong intrinsic fluorescent signature that can overshadow the signal. To elucidate the underlying Raman spectrum, it is generally preprocessed to allow further analysis. There are many methods available for preprocessing; a selection of commonly used methodologies has been included here, as well as methods of my own design. The most novel approach being a neural network that was trained on synthetic spectra to perform preprocessing without relying on user defined variables, which is common for other methods (Paper A). The neural network resulted in improved preprocessing when compared to a control predictor, with test data from paraffin, ethanol, and polyethylene, as well as spectra based on simulations. Hypoxic pulmonary hypertension (PH) is a condition where the arteries in the lung-walls are blocked due to prolonged oxygen deprivation or lung disease. People who suffer from PH, often experience shortness of breath and f

Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019-2020 task on uncertainty quantification (QU-BraTS), and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions, and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentages of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, and hence highlight the need for uncertainty quantification in medical image analyses. Our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS

Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019-2020 task on uncertainty quantification (QU-BraTS), and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions, and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentages of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, and hence highlight the need for uncertainty quantification in medical image analyses. Our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS

Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019-2020 task on uncertainty quantification (QU-BraTS), and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions, and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentages of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, and hence highlight the need for uncertainty quantification in medical image analyses. Our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS

Lymph node metastasis (LNM) from primary tumors of the central nervous system (CNS) is an infrequent condition, and classically it was thought that CNS tumors could not spread via the lymphatic route. Recent discoveries about this route of dissemination make its knowledge necessary for surgeons and pathologists to avoid delays in diagnosis and unnecessary treatments. The aim of this paper is to review the literature and to discuss the relevant pathogenetic mechanism and the cytologic features along with recommendations for surgical treatment of these cervical LNM. Using PRISMA guidelines, we conducted a systematic review of the literature published from 1944 to 2021, updating the comprehensive review published in 2010 by our group. Our review includes data of 143 articles obtaining 174 patients with LNM from a primary CNS tumor. The mean age of the patients was 31.9 years (range, 0.1-87) and there were 61 females (35.1%) and 103 males (59.2%), and in 10 cases (5.7%) the gender was not specified. The more frequent sites of distant metastasis were bones (23%), lungs (11.5%) and non-cervical lymph nodes (11%). Cervical LNM from CNS tumors is infrequent. Pathologic diagnosis can be obtained by fine-needle aspiration cytology in most cases, giving surgeons the option to plan the appropriate surgical treatment. Given the poor prognosis of these cases, the most conservative possible cervical dissection is usually the treatment of choice.

Harnessing the effector mechanisms of the immune system to combat brain tumors with antigen specificity and memory has been in research and clinical testing for many years. Government grant mechanisms and non-profit organizations have supported many innovative projects and trials while biotech companies have invested in the development of needed tools, assays and novel clinical approaches. The National Brain Tumor Society and the Parker Institute for Cancer Immunotherapy partnered to host a workshop to share recent data, ideas and identify both hurdles and new opportunities for harnessing immunotherapy against pediatric and adult brain tumors. Adoptively transferred cell therapies have recently shown promising early clinical results. Local cell delivery to the brain, new antigen targets and innovative engineering approaches are poised for testing in a new generation of clinical trials. Although several such advances have been made, several obstacles remain for the successful application of immunotherapies for brain tumors, including the need for more representative animal models that can better foreshadow human trial outcomes. Tumor and tumor microenvironment biopsies with multiomic analysis are critical to understand mechanisms of response and patient stratification, yet brain tumors are especially challenging for such biopsy collection. These workshop proceedings and commentary shed light on the status of immunotherapy in pediatric and adult brain tumor patients, including current research as well as opportunities for improving future efforts to bring immunotherapy to the forefront in the management of brain tumors.

BackgroundCognitive impairment and cerebral microbleeds (CMBs) are long-term side-effects of cranial radiation therapy (RT). Previously we showed that memory function is disrupted in young patients and that the rate of cognitive decline correlates with CMB development. However, vascular injury alone cannot explain RT-induced cognitive decline. Here we use resting-state functional MRI (rsfMRI) to further investigate the complex mechanisms underlying memory impairment after RT.MethodsNineteen young patients previously treated with or without focal or whole-brain RT for a brain tumor underwent cognitive testing followed by 7T rsfMRI and susceptibility-weighted imaging for CMB detection. Global brain modularity and efficiency, and rsfMRI signal variability within the dorsal attention, salience, and frontoparietal networks were computed. We evaluated whether MR metrics could distinguish age- and sex-matched controls (N = 19) from patients and differentiate patients based on RT exposure and aggressiveness. We also related MR metrics with memory performance, CMB burden, and risk factors for cognitive decline after RT.ResultsCompared to controls, patients exhibited widespread hyperconnectivity, similar modularity, and significantly increased efficiency (p < 0.001) and network variability (p < 0.001). The most abnormal values were detected in patients treated with high dose whole-brain RT, having supratentorial tumors, and who did not undergo RT but had hydrocephalus. MR metrics and memory performance were correlated (R = 0.34-0.53), though MR metrics were more strongly related to risk factors for cognitive worsening and CMB burden with evidence of functional recovery.ConclusionsMR metrics describing brain connectivity and variability represent promising candidate imaging biomarkers for monitoring of long-term cognitive side-effects after RT.

Imaging response assessment is a cornerstone of patient care and drug development in oncology. Clinicians/clinical researchers rely on tumor imaging to estimate the impact of new treatments and guide decision making for patients and candidate therapies. This is important in brain cancer, where associations between tumor size/growth and emerging neurological deficits are strong. Accurately measuring the impact of a new therapy on tumor growth early in clinical development, where patient numbers are small, would be valuable for decision making regarding late-stage development activation. Current attempts to measure the impact of a new therapy have limited influence on clinical development, as determination of progression, stability or response does not currently account for individual tumor growth kinetics prior to the initiation of experimental therapies. Therefore, we posit that imaging-based response assessment, often used as a tool for estimating clinical effect, is incomplete as it does not adequately account for growth trajectories or biological characteristics of tumors prior to the introduction of an investigational agent. Here, we propose modifications to the existing framework for evaluating imaging assessment in primary brain tumors that will provide a more reliable understanding of treatment effects. Measuring tumor growth trajectories prior to a given intervention may allow us to more confidently conclude whether there is an anti-tumor effect. This updated approach to imaging-based tumor response assessment is intended to improve our ability to select candidate therapies for later-stage development, including those that may not meet currently sought thresholds for "response" and ultimately lead to identification of effective treatments.

There is an urgent need for drug development in brain tumors. While current radiographic response assessment provides instructions for identifying large treatment effects in simple high- and low-grade gliomas, there remains a void of strategies to evaluate complex or difficult to measure tumors or tumors of mixed grade with enhancing and non-enhancing components. Furthermore, most patients exhibit some period of alteration in tumor growth after starting a new therapy, but simple response categorization (e.g., stable disease, progressive disease) fails to provide any meaningful insight into the depth or degree of potential "subclinical" therapeutic response. We propose a creative solution to these issues based on a tiered strategy meant to increase confidence in identifying therapeutic effects even in the most challenging tumor types, while also providing a framework for complex evaluation of combination and sequential treatment schemes. Specifically, we demonstrate the utility of digital "flipbooks" to quickly identify subtle changes in complex tumors. We show how a modified Levin criteria can be used to quantify the degree of visual changes, while establishing estimates of the association between tumor volume and visual inspection. Lastly, we introduce the concept of quantifying therapeutic response using control systems theory. We propose measuring changes in volume (proportional), the area under the volume vs. time curve (integral) and changes in growth rates (derivative) to utilize a "PID" controller model of single or combination therapeutic activity.

Pediatric brain tumors are the most common solid malignancies in children. Advances in the treatment of pediatric brain tumors have come in the form of imaging, biopsy, surgical techniques, and molecular profiling. This has led the way for targeted therapies and immunotherapy to be assessed in clinical trials for the most common types of pediatric brain tumors. Here we review the latest efforts and challenges in targeted molecular therapy, immunotherapy, and newer modalities such as laser interstitial thermal therapy.

Background: Postoperative seizures (PS) occur in 10-15% of patients. This study aims to provide an update on the role of surgery in PS. Methods: All children undergoing a craniotomy for supratentorial lesions in the last 10 years were considered except those with preoperative seizures, perioperative antiepileptic drugs prophylaxis, head-injury and infections, repeated surgery, or preoperative hyponatremia. Children undergoing surgery for intra-axial lesions (Group 1, 74 cases) were compared with those harboring extra-axial lesions (Group 2, 91 cases). Results: PS occurred in 9% of 165 cases and epilepsy in 3% of 165 cases (mean follow-up: 5.7 years). There was no difference between the two study groups with regard to demographic data or tumor size. Group 1 showed a higher rate of gross total tumor resection (p = 0.002), while Group 2 had a higher rate of postoperative hyponatremia (p < 0.0001). There were no differences between the two groups in the occurrence of seizures (6.7% vs. 11%) or epilepsy (2.7% vs. 3.2%). No correlations were found between seizures and age, tumor location, histotype, tumor size, or the extent of tumor resection. Hyponatremia affected the risk of PS in Group 2 (p = 0.02). Conclusions: This study shows a lower rate of PS and epilepsy than series including children with preoperative seizures. Hyponatremia has a significant role. Neurosurgery is safe but surgical complications may cause late epilepsy.

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) chal- lenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher per- centage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmen- tation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS., Research reported in this publication was partly supported by the Informatics Technology for Cancer Research (ITCR) program of the National Cancer Institute (NCI) of the National Institutes of Health (NIH), under award numbers NIH/NCI/ITCR:U01CA242871 and NIH/NCI/ITCR:U24CA189523. It was also partly supported by the National Institute of Neurological Disorders and Stroke (NINDS) of the NIH, under award number NIH/NINDS:R01NS042645, The content of this publication is solely the responsibility of the authors and does not represent the official views of the NIH. This work was supported by a Canadian Natural Science and Engineering Research Council (NSERC) Collaborative Research and Development Grant (CRDPJ 505357 - 16), Synaptive Medical, and the Canada Institute for Advanced Research (CIFAR) Artificial Intelligence Chairs program., Peer Reviewed, Document signat per 92 autors/autores: Raghav Mehta1 , Angelos Filos2 , Ujjwal Baid3,4,5 , Chiharu Sako3,4 , Richard McKinley6 , Michael Rebsamen6 , Katrin D¨atwyler6,53, Raphael Meier54, Piotr Radojewski6 , Gowtham Krishnan Murugesan7 , Sahil Nalawade7 , Chandan Ganesh7 , Ben Wagner7 , Fang F. Yu7 , Baowei Fei8 , Ananth J. Madhuranthakam7,9 , Joseph A. Maldjian7,9 , Laura Daza10, Catalina Gómez10, Pablo Arbeláez10, Chengliang Dai11, Shuo Wang11, Hadrien Raynaud11, Yuanhan Mo11, Elsa Angelini12, Yike Guo11, Wenjia Bai11,13, Subhashis Banerjee14,15,16, Linmin Pei17, Murat AK17, Sarahi Rosas-González18, Illyess Zemmoura18,52, Clovis Tauber18 , Minh H. Vu19, Tufve Nyholm19, Tommy L¨ofstedt20, Laura Mora Ballestar21, Veronica Vilaplana21, Hugh McHugh22,23, Gonzalo Maso Talou24, Alan Wang22,24, Jay Patel25,26, Ken Chang25,26, Katharina Hoebel25,26, Mishka Gidwani25, Nishanth Arun25, Sharut Gupta25 , Mehak Aggarwal25, Praveer Singh25, Elizabeth R. Gerstner25, Jayashree Kalpathy-Cramer25 , Nicolas Boutry27, Alexis Huard27, Lasitha Vidyaratne28, Md Monibor Rahman28, Khan M. Iftekharuddin28, Joseph Chazalon29, Elodie Puybareau29, Guillaume Tochon29, Jun Ma30 , Mariano Cabezas31, Xavier Llado31, Arnau Oliver31, Liliana Valencia31, Sergi Valverde31 , Mehdi Amian32, Mohammadreza Soltaninejad33, Andriy Myronenko34, Ali Hatamizadeh34 , Xue Feng35, Quan Dou35, Nicholas Tustison36, Craig Meyer35,36, Nisarg A. Shah37, Sanjay Talbar38, Marc-Andr Weber39, Abhishek Mahajan48, Andras Jakab47, Roland Wiest6,46 Hassan M. Fathallah-Shaykh45, Arash Nazeri40, Mikhail Milchenko140,44, Daniel Marcus40,44 , Aikaterini Kotrotsou43, Rivka Colen43, John Freymann41,42, Justin Kirby41,42, Christos Davatzikos3,4 , Bjoern Menze49,50, Spyridon Bakas∗3,4,5 , Yarin Gal∗2 , Tal Arbel∗1,51 // 1Centre for Intelligent Machines (CIM), McGill University, Montreal, QC, Canada, 2Oxford Applied and Theoretical Machine Learning (OATML) Group, University of Oxford, Oxford, England, 3Center for Biomedical Image Comp, Postprint (published version)

Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019-2020 task on uncertainty quantification (QU-BraTS), and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions, and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentages of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, and hence highlight the need for uncertainty quantification in medical image analyses. Our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS

Funding agencies:European Social Fund (ESF) CUP J44I18000280006 AIM1839117-3 Cluster of Excellence Matters of ActivityGerman Research Foundation (DFG) EXC 2025-390648296

Malignant brain tumors constitute a disaster in the lives of patients, either in the form of extremely low survival in glioblastoma, or the serious long-term adverse effects of therapy in medulloblastoma. These two tumor types represent the most common malignant brain entities in adults and children, respectively. Ever since the early 2000’s, no major improvement of patient outcomes occurred. Immunotherapy, which recorded revolutionary successes in several tumor types, has so far failed in brain tumors. This disappointing phenomenon is the result of intrinsic characteristics of glioblastoma and medulloblastoma and their microenvironment. Therefore, other treatment modalities that exploit the distinct attributes of malignant brain tumors are urgently needed.In this thesis, I describe the features of both tumor types and the development of their therapy until today. Moreover, general features of tumor microenvironment are contrasted to the unique aspects of the brain tumor counterpart. Next, I outline the underlying mechanisms of conventional immunotherapy and recount the natural features of both tumors that prevent its effective deployment. Finally, I suggest alternative approaches that circumvent the challenges encountered so far, such as avoiding the blood brain barrier via local treatment administration or focusing on macrophages as the principal agent of immunotherapy instead of T cells. Antisecretory factor (AF), a new agent in cancer treatment, as well as modulation of CD24/Siglec10 “don’t eat me” signaling are examples of the latter.Following is a summary of the four projects. In publication I, the concept of intratumoral temozolomide treatment is investigated from the perspective of tumor immune microenvironment. Publication II describes the effects of AF16 on macrophages and glioblastoma cells. Publication III is a pilot clinical trial of an AF preparation in patients with newly diagnosed glioblastoma. Manuscript IV examines the modulation of CD24 and Siglec

Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019-2020 task on uncertainty quantification (QU-BraTS), and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions, and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentages of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, and hence highlight the need for uncertainty quantification in medical image analyses. Our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS

Diffusion MRI (dMRI) sensitizes the MR signal to the diffusion of water molecules at the microscopic level and thereby non-invasively probes tissue microstructure. This is relevant when determining biological properties of tissues, for example, cancer type and its malignancy. The problem is, however, that dMRI lacks sensitivity and specificity to distinct microstructural features because an image voxel contains vast number of different features that are mapped onto relatively few dMRI observables. To tackle this issue, we aimed at solving two gaps in current knowledge—the first was related to what microstructural aspects are of most importance and the second to how adding new observables to the dMRI measurement could improve brain tumor imaging.In this work, we first investigate the biological underpinnings of dMRI observables—focusing on the degree to which larger-scale microstructural arrangements are of relevance. In Paper I, we investigated the effects of non-straight propagation of axons and found that they are indistinguishable from those originating from the diameter of a straight axon, at least for typical measurements with a clinical scanner. We propose that the use of short diffusion times could help separate them. In Paper II, in a comparison between histology and microimaging of meningioma brain tumors, we quantified to what degree the common biological interpretation of one of the most used dMRI observable holds—mean diffusivity (MD) as reflecting cell density and fractional anisotropy reflecting tissue anisotropy. We found that the local variability in MD was explained in minority of the samples whereas FA in majority by the common interpretations. We suggested additional relevant features such as tumor vascularization, psammoma bodies, microcysts or tissue cohesivity for explaining MD variability.Second, we examined whether a framework that introduces a new measurement observable brings value in intracranial tumor imaging. This new variable is termed

Pediatric medulloblastomas (MBs) are the most common type of malignant brain tumors in children. They are among the most aggressive types of tumors due to their potential for metastasis. Although this disease was initially considered a single disease, pediatric MBs can be considerably heterogeneous. Current MB classification schemes are heavily reliant on histopathology. However, the classification of MB from histopathological images is a manual process that is expensive, time-consuming, and prone to error. Previous studies have classified MB subtypes using a single feature extraction method that was based on either deep learning or textural analysis. Here, we combine textural analysis with deep learning techniques to improve subtype identification using histopathological images from two medical centers. Three state-of-the-art deep learning models were trained with textural images created from two texture analysis methods in addition to the original histopathological images, enabling the proposed pipeline to benefit from both the spatial and textural information of the images. Using a relatively small number of features, we show that our automated pipeline can yield an increase in the accuracy of classification of pediatric MB compared with previously reported methods. A refined classification of pediatric MB subgroups may provide a powerful tool for individualized therapies and identification of children with increased risk of complications.

Biology can often be explained by structures and interactions between molecules. It is therefore of importance for medical sciences that the chemical reactions and molecular compositions of biological samples can be measured. The purpose of this thesis is twofold. Firstly, to develop methods for measuring the underlying mechanisms of the lung disorder pulmonary hypertension. Secondly, to investigating the possibility of classifying brain tissue for safer resection of brain tumors. A multimodal approach is often motivated, as there exist many different measurement techniques. In this thesis Raman spectroscopy has been applied both as the main measurement modality and in cooperation with other methods. Raman spectroscopy is a label free optical measurement technique that measure inelastic scattering from materials that are illuminated by a monochromatic light source. Raman scattering results in a weak signal that is uniquely proportional to the chemical structure of the sample. When measuring biological samples, Raman scattering is accompanied by a strong intrinsic fluorescent signature that can overshadow the signal. To elucidate the underlying Raman spectrum, it is generally preprocessed to allow further analysis. There are many methods available for preprocessing; a selection of commonly used methodologies has been included here, as well as methods of my own design. The most novel approach being a neural network that was trained on synthetic spectra to perform preprocessing without relying on user defined variables, which is common for other methods (Paper A). The neural network resulted in improved preprocessing when compared to a control predictor, with test data from paraffin, ethanol, and polyethylene, as well as spectra based on simulations. Hypoxic pulmonary hypertension (PH) is a condition where the arteries in the lung-walls are blocked due to prolonged oxygen deprivation or lung disease. People who suffer from PH, often experience shortness of breath and f

Objectives: The main objective of the study is to assess the feasibility and reproducibility of routine MRS to assist in the differential diagnosis between post-radiation necrosis and tumor progression. The secondary objective is to evaluate the accuracy of the method. Method: An additional sequence of MRS was added to the standard protocol routinely used for patient follow-up. To assess discomfort a control group was formed. The time required to perform MRS and analysis of results, and data about artefacts and technical limitations were collected. MRS results analyzed independently by two neuroradiologists were compared. The diagnostic accuracy of MRS was calculated using a composite reference standard. Results: The experimental group included 38 patients, the control group 41. The discomfort felt during the examination, is not significantly different between the groups. The average quality of SRM is rated as low. The frequency of cerebral radionecrosis is 13 % based on the reference standard used, 54 % and 46 % based on MRS results for the two observers. The additional time is 19,5 min. There is strong inter-observer agreement. The sensitivity and specificity of MRS are respectively for the diagnosis of radionecrosis of 60 % and 45 % (PPV = 16 %NPV = 87 %), for the diagnosis of tumor tissue of 25 % and 94 % (PPV = 80 %NPV = 57%). Conclusion: MRS is probably not applicable in routine clinical practice; however, in view of our results and the literature, in selected cases, it could be a support in the diagnosis of radionecrosis or brain tumor progression. Radionecrosis is probably underestimated., SCOPUS: ar.j, info:eu-repo/semantics/published