What Does Quality of Care Mean in Lower-Grade Glioma Patients: A Precision Molecular-Based Management of the Tumor or an Individualized Medicine Centered on Patient’s Choices?

Taillandier, Luc; Obara, Tiphaine; Duffau, Hugues

doi:10.3389/fonc.2021.719014

Cited by 7 publications

(5 citation statements)

References 59 publications

(68 reference statements)

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“…Due to a longer life expectancy in patients with GIIG with individualized management taking care of patients' long-term wishes, 4,19 the desire to have a child is increasingly expressed. However, few data are available on this topic 5,9,10 because pregnancy remains scarce in women with GIIG, especially when they are already diagnosed with a glioma.…”

Section: Discussionmentioning

confidence: 99%

Factors Associated With Long-term Survival in Women Who Get Pregnant After Surgery for WHO Grade II Glioma

Duffau

2022

Neurology

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Background and Objectives:Women with a WHO grade II glioma (GIIG) often question clinicians on the effects of pregnancy on their disease. Previous reports have indicated a higher risk of glioma progression during and after pregnancy. Here, the aim was to investigate post-pregnancy outcomes and predictive factors impacting overall survival in female patients who underwent GIIG surgery.Methods:Inclusion criteria were adult women who have been pregnant after a GIIG resection and with a stable oncological status at the time of pregnancy (no ongoing oncological treatment, no contrast enhancement, no debilitating clinical condition). Relevant cases were identified from a databank (1998-2021) of patients who underwent surgical resection for a histologically-confirmed GIIG in our department.Results:Among 345 GIIG women within their reproductive years (age<45y), 16 patients (4.6%, mean age at delivery: 30.9±5.1 years) were pregnant (twice in 5 cases). The mean interval between the last oncological treatment (surgery alone in 11 patients while followed by chemotherapy and/or radiotherapy in 5 patients) and pregnancy was 3.5 years (range 0.75-10 years). Two patients experienced seizures during pregnancy. The delivery was vaginal and uneventful in all cases but one (1 caesarean). All children had normal mental and physical development. The glioma behavior changed in 7 patients (43.7%), with an acceleration of the velocity of diameter expansion (VDE) and/or the occurrence of a contrast enhancement during or within the 3 months after pregnancy, resulting in medical treatment and/or reoperation in the early post-partum period in 7 cases. The median clinical follow-up from delivery was 5.3 years (range 1.25-11.6 years). Four other patients received delayed adjuvant therapy for glioma progression. Seven patients (43.7%) died at a median time from delivery of 3.9 years (range 1.25-5.9 years). Overall, the median survival from delivery was 5.75 years. Crucially, patients who underwent a complete surgical resection and patients with stable lesions before pregnancy lived longer (log rank, p=0.046 and p=0.0026, respectively).Conclusions:Tumor residual volume and tumor speed growth are strong predictive factors conditioning post-pregnancy long-term survival in patients with GIIG. Identifying patients at risk is critical to provide relevant counsel to GIIG women with a desire for motherhood.

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Section: Discussionmentioning

confidence: 99%

Factors Associated With Long-term Survival in Women Who Get Pregnant After Surgery for WHO Grade II Glioma

Duffau

2022

Neurology

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“…In addition, in cases of reoperation(s), updated neuropathological examinations may show changes in the grade of malignancy as well as in the molecular profile [ 32 ]. As a consequence, LGG genetics represents only part of the history, and caution should be exercised before applying a therapeutic strategy on the basis of molecular markers, which risks overlooking other critical clinical and radiological factors [ 67 , 68 ].…”

Section: Predicting Oncological Interindividual Variability and Its C...mentioning

confidence: 99%

A Personalized Longitudinal Strategy in Low-Grade Glioma Patients: Predicting Oncological and Neural Interindividual Variability and Its Changes over Years to Think One Step Ahead

Duffau

2022

JPM

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Diffuse low-grade glioma (LGG) is a rare cerebral cancer, mostly involving young adults with an active life at diagnosis. If left untreated, LGG widely invades the brain and becomes malignant, generating neurological worsening and ultimately death. Early and repeat treatments for this incurable tumor, including maximal connectome-based surgical resection(s) in awake patients, enable postponement of malignant transformation while preserving quality of life owing to constant neural network reconfiguration. Due to considerable interindividual variability in terms of LGG course and consecutive cerebral reorganization, a multistage longitudinal strategy should be tailored accordingly in each patient. It is crucial to predict how the glioma will progress (changes in growth rate and pattern of migration, genetic mutation, etc.) and how the brain will adapt (changes in patterns of spatiotemporal redistribution, possible functional consequences such as epilepsy or cognitive decline, etc.). The goal is to anticipate therapeutic management, remaining one step ahead in order to select the optimal (re-)treatment(s) (some of them possibly kept in reserve), at the appropriate time(s) in the evolution of this chronic disease, before malignization and clinical worsening. Here, predictive tumoral and non-tumoral factors, and their ever-changing interactions, are reviewed to guide individual decisions in advance based on patient-specific markers, for the treatment of LGG.

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“…For optimal survival and neurocognitive outcome, the timing of surgery and subsequent adjuvant therapy is also essential. Currently, only a few clinical or molecular markers are routinely taken into account when determining which treatment options are best for the patient at each disease stage 8 , 9 . Unfortunately, such a subjective approach frequently introduces bias into patient management and can occasionally result in inappropriate treatment decisions.…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, such a subjective approach frequently introduces bias into patient management and can occasionally result in inappropriate treatment decisions. Consequently, clinicians are compelled to accept potentially avoidable collateral effects, such as the patient's early neuropsychological decline after radiation therapy, which could be delayed or avoided if more accurate information about the disease stage or future progression were available 8 , 10 , 11 . By disclosing what is currently invisible to the naked eye, radiomics show promising results in the non-invasive prediction of glioma behavior such as progression free survival or overall survival 12 – 14 .…”

Section: Introductionmentioning

confidence: 99%

Deep learning-assisted radiomics facilitates multimodal prognostication for personalized treatment strategies in low-grade glioma

Rauch,

Stefanits,

Aichholzer

et al. 2023

Sci Rep

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Determining the optimal course of treatment for low grade glioma (LGG) patients is challenging and frequently reliant on subjective judgment and limited scientific evidence. Our objective was to develop a comprehensive deep learning assisted radiomics model for assessing not only overall survival in LGG, but also the likelihood of future malignancy and glioma growth velocity. Thus, we retrospectively included 349 LGG patients to develop a prediction model using clinical, anatomical, and preoperative MRI data. Before performing radiomics analysis, a U2-model for glioma segmentation was utilized to prevent bias, yielding a mean whole tumor Dice score of 0.837. Overall survival and time to malignancy were estimated using Cox proportional hazard models. In a postoperative model, we derived a C-index of 0.82 (CI 0.79–0.86) for the training cohort over 10 years and 0.74 (Cl 0.64–0.84) for the test cohort. Preoperative models showed a C-index of 0.77 (Cl 0.73–0.82) for training and 0.67 (Cl 0.57–0.80) test sets. Our findings suggest that we can reliably predict the survival of a heterogeneous population of glioma patients in both preoperative and postoperative scenarios. Further, we demonstrate the utility of radiomics in predicting biological tumor activity, such as the time to malignancy and the LGG growth rate.

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What Does Quality of Care Mean in Lower-Grade Glioma Patients: A Precision Molecular-Based Management of the Tumor or an Individualized Medicine Centered on Patient’s Choices?

Cited by 7 publications

References 59 publications

Factors Associated With Long-term Survival in Women Who Get Pregnant After Surgery for WHO Grade II Glioma

Factors Associated With Long-term Survival in Women Who Get Pregnant After Surgery for WHO Grade II Glioma

A Personalized Longitudinal Strategy in Low-Grade Glioma Patients: Predicting Oncological and Neural Interindividual Variability and Its Changes over Years to Think One Step Ahead

Deep learning-assisted radiomics facilitates multimodal prognostication for personalized treatment strategies in low-grade glioma

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