Decision making is one of the most complex skills required of an oncologist and is affected by a broad range of parameters. For example, the wide variety of treatment options, with various outcomes, side-effects and costs present challenges in selecting the most appropriate treatment. Many treatment choices are affected by limited scientific evidence, availability of therapies or patient-specific factors. In the decision making process, standardized approaches can be useful, but a multitude of criteria are relevant to this process. Thus, the aim of this review is to summarize common types of decision criteria used in oncology by focusing on 3 main categories: criteria associated with the decision maker (both patient and doctor), decision specific criteria, and the often-overlooked contextual factors. Our review aims to highlight the broad range of decision criteria in use, as well as variations in their interpretation.

The process of clinical decision-making is the essence of everyday clinical practice. Various factors influence our judgments and decisions. For various reasons, multiple options are often available for oncological problems [1]. Decision makers’ individual characteristics (decision maker as a person), decision-specific characteristics (the nature of the decision itself), and contextual factors (environment in which the decision is being made) shape our decisions [2, 3]. Medical decision making can be particularly complex and multi-layered, involving diagnostic and therapeutic uncertainties, patient preferences and values, and includes the complexities of the healthcare environment. Many decisions made in oncology are not based solely on evidence-based medicine, that is, clinical experience and the best available research. Consequently, eminence-based decision making, representing the opinion of an experienced colleague for example, comes into play [4]. In addition, decision making in medicine ideally involves the patient and thus can be characterized as shared decision making [5]; however, this process is often far from perfect [6]. Decision making in oncology involves the consideration of a complex set of diagnostic, therapeutic and prognostic uncertainties, potentially leading to considerable disagreement about the best course of action. This narrative review aims to reflect on various aspects of decision criteria in clinical oncology. Due to the lack of conceptual models for decision making criteria in this context, we built on the model developed in the managerial decision making context by Papadakis et al. [2] and later revised by Elbanna [3]. Our review highlights the broad range of decision criteria, the variation in the interpretation of these criteria, as well as practical approaches dealing with the complexity of everyday decision making.

Due to a variety of cancers, healthcare systems, treatment options, and individual factors, a plethora of different criteria are being implemented in routine clinical decision making in oncology. This has been demonstrated in decision making analyses of clinical experts. For example, treatment algorithms for the first-line systemic therapy for metastatic clear cell renal cell carcinoma from 11 international experts were analyzed and up to 6 different treatment options were identified for the same specific presentation of the disease. These treatment options were selected based on 7 differentiating treatment criteria [7], excluding universal factors such as, for example, informed consent.

In this review, decision making criteria used in oncology are categorized into 3 categories developed in the field of managerial decision making [2, 3]. We first concentrate on decision maker-related characteristics, including both the physician and the patient. Attributes, such as capabilities, confidence, self-efficacy, emotions, frames of reference, and degree of expertise, also influence decision making [8]; all of these factors affect the decision maker. Second, we consider decision-specific criteria. These involve classical clinical criteria, such as performance status, age, presence of comorbidities, cancer stage, biomarkers, or expected treatment toxicity. Third, we discuss contextual factors that are often overlooked in medical decision making. These factors include the patient’s socioeconomic status, the health care system, treatment costs, or influence from the pharmaceutical industry. We use these 3 domains to categorize decision making criteria in oncology (Fig. 1).

Fig. 1.

Conceptual model of decision making criteria in oncology. The 3 main categories of decision criteria with examples are displayed.

Fig. 1.

Conceptual model of decision making criteria in oncology. The 3 main categories of decision criteria with examples are displayed.

Close modal

Traditionally, decision making has been viewed as involving 2 forms of information processing – rational and non-rational modes of thinking [9]. Behavioral science approaches decision making from the perspective that humans do not make decisions as rational, profit-maximizing individuals. Aligned with this perspective, recent studies show that decision making is influenced by factors such as cognitive biases, or emotions [10-12]. Decision makers are often confronted with the trade-off between decision accuracy (how good decisions are) and decision speed (how quickly decisions are made) [13-15]. Especially when multiple options need to be considered, balancing between accuracy and speed in decision making is challenging. To cope with this challenge, decision makers use intuition [16-18], which draws on our ability to synthesize information quickly and effectively and is often based on expertise [19]. Potentially, this non-rational approach is reflected in eminence-based medicine (EBM) and other alternatives strategies to evidence-based medicine [20]. The importance of shared decision making has been increasingly recognized over the past decades and thus both the patient and the physician act as decision makers.

Patient-Related Criteria

Patient-related criteria are central to decision making. Decisions made in tumor boards might be overruled by patients or care-givers – for various reasons, for example, a patient’s adherence to treatment or inappropriate behavior that may influence adherence (e.g., disorganized life style and frequent non-attendance at follow-up appointments) or a decision against active cancer treatment (e.g., for best supportive care and focusing on quality of life instead of survival). Non-adherence in cancer patients is a prevalent problem with varying adherence rates reported in literature (16–100%) [21-23]. There are many psychological factors involved when patients consider treatment options, including prior experience, quality of life during or after treatment, life expectancy, opinion of their care provider, and preference of the patient’s family. Studies show that if patient preferences and individual goals are being heard, they are more likely to be satisfied and compliant with treatment regimens [24].

Physician-Related Criteria

The knowledge level of the physician affects the process of treatment decision-making. Ideally, decision making is based on high-level evidence, and making good decisions requires familiarity with current evidence and the ability to interpret and apply this in a clinical setting. The professional background of the physician is also a relevant factor. A national survey by Fowler et al. [25] reported that urologists tend to prefer surgery, while radiation oncologists tend to favor radiation therapy in managing patients with localized prostate cancer. Another survey [26] showed similar discipline-specific recommendations: gastroenterologists tend to favor surgery, while hematologists and medical oncologists preferred conservative therapy for treating gastric lymphoma. Based on the National Lung Cancer Audit in the United Kingdom, patients with lung cancer first seen in thoracic surgery centers were 51% more likely to have surgery compared to those seen in non-surgical centers (surgery being the strongest determinant of outcome) [27]. Physician-behavior may influence treatment decisions as well: physician’s time constraints and work overload in clinical routine can impact recommendations.

Patient-Physician Interaction

The patient-physician interaction also influences decision making. It is important to recognize that both patient and physician may approach the clinical encounter with different priorities. Physicians often seek to diagnose and treat an illness based on the patient’s symptoms and objective information derived from physical examinations, laboratory tests, or the patient’s medical history. Conversely, patients may only seek care when symptoms signal there is a problem because of disruptions in their work or social life. Including patient preferences in medical decision-making helps treatment selection, especially when no clear treatment preference exists based on objective outcomes. Even though shared decision making is a laudable goal and routine medical decision making is moving toward this model, there are several limitations to shared decision making, as it is exposed to a variety of biases [6]. Especially in the setting of poor evidence, many clinicians are still more familiar with the paternalistic model [28, 29]. Additionally, in this model, physicians exert control over information and decision making, and the patient may simply comply with what the physician recommends. This approach impacts the decision-making process and influences outcome [30], leading to the under-treatment of elderly women with breast or ovarian cancer [31].

A substantial proportion of individuals making preference-sensitive cancer-related decisions experience decisional conflict. For example, 43% of patients with advanced cancer were uncertain about whether to receive end-of-life care at home or in a health care institution [32]. In another study of cancer patients with advanced non-small-cell lung cancer, only 30% felt sure about choosing chemotherapy or best supportive therapy [33]. Key factors contributing to patients’ decisional conflict across these studies included feeling uninformed, having uncertainty about their values, and being unsupported in decision making.

Optimal physician-patient communication has the potential to help regulate patients’ emotions, facilitate comprehension of medical information, and allow for better identification of patients’ needs, perceptions, and expectations. Trust is one of the central features of patient-physician relationship. A positive relationship and good communication between the patient and physician may lead to higher-quality outcomes and better satisfaction, lower costs of care, greater patient understanding of health issues, and better adherence to the treatment process [34].

Each physician or patient brings their own personal values and beliefs to the decision-making process. Physicians might rank comorbidity and trial results as important factors in treatment decision-making, while patients might rank family preference, family burden, and physician’s opinion as important factors in making treatment decisions. To avoid this dissonance, well-organized interdisciplinary collaboration may be essential to facilitate adequate treatment considering all aspects of care, especially including patient preference.

Decision-specific characteristics are related to the nature of the decision itself. These include all basic decision criteria used in clinical practice. Referring to Elbanna [3] these are criteria important for a decision motive, decision uncertainty and for the importance of a decision. Under acute stress (short-lived, high intensity) we focus on short-term rapid responses at the expense of complex thinking. This type of response can be life-saving when we need to react to immediate danger, but can also lead to “tunnel vision” and ill-thought-through decisions [35]. We need to keep this in mind when we jump to clear conclusions.

Age is a commonly used criterion in oncology. There are different treatment recommendations for children/adolescents (≤18 years of age) and for adults; for example, medulloblastoma patients are treated with different doses of radiation therapy depending on age [36]. Similar impacts of age are observed in the elderly [37]. Elderly patients may not tolerate treatments as well as their younger counterparts. There is emerging evidence that “elderly” people are offered less intensive treatment [38, 39]. However, the interpretation of what “elderly” means and how it is interpreted can be very heterogeneous [40]. Underrepresentation of elderly patients in clinical trials adds to our dilemma in dealing with this population [41, 42].

Comorbidities may result in more conservative treatment, despite evidence that more active treatments may be well tolerated [43]. The evidence concerning potential benefits and harms of cancer treatments among patients with comorbidities is scarce. Clinicians may overestimate potential toxicity or effectiveness of treatments among patients with comorbidities, or underestimate their life expectancies, both leading to potential under-treatment. This has been shown in referral and treatment recommendation patterns [44]. Decisions made for patients with comorbidities or elderly people are less likely to be concordant with clinical guidelines. Vinod et al. [45] reported that 29% of lung cancer patients reviewed in tumor boards were treated outside of guidelines, and comorbidity was the cause in 1 out of 4 cases [43].

For many treatment options, a good performance status is a prerequisite. The Karnofsky performance status and the Eastern Cooperative Oncology Group Performance Status scales [46] are commonly used to quantify performance status. When in experienced hands, their measurement can be consistent [47]; however, the cut-off values for decision making are widely variable in clinical routine [40].

The entity of a tumor and the tumor-stage play an important role in the decision making process. Treatment is based largely on the stage/extent of the cancer. Treatment recommendations for localized tumors are different to more advanced disease or metastasized tumors. The position of a single lymph node may determine operability.

Biological features can support decision making as well. An example for such parameters is the promoter methylation of the gene encoding for MGMT in glioblastoma patients. MGMT methylation is a predictive factor of favorable survival in glioblastoma patients undergoing chemotherapy with alkylating agents [48]. Especially for the elderly subpopulation, phase III trials showed that overall survival in methylated patients was better if temozolomide treatment was applied, whereas in unmethylated patients, radiotherapy alone was more effective [49, 50]. Another example for biological features influencing decision making in oncology is OncoType DX assay. Jaafar et al. [51] reported that the use of the assay was associated with a significant change in treatment decisions and an overall reduction of chemotherapy use. Also, the Ki-67 Index, although there is no objectively established cutoff point [52-54], has predictive and prognostic value and is a utilized marker in clinical practice, for example, it independently improves the prediction of treatment response and prognosis in a group of breast cancer patients receiving neoadjuvant treatment [55].

Deciding on the goal of a treatment is sometimes a problem. For oligo-metastatic prostate cancer [56], do we want to improve progression-free survival, androgen deprivation therapy free time or even overall survival? Deciding on one of these goals in order to improve quality or quantity of life will influence the recommendation to treat or not to treat, the level of data available to support the decision and potentially the level of side effects the patient is willing to risk. The chosen goal of a specific therapy influences the selection of decision criteria.

Papadakis et al. [2] described contextual factors referring to external corporate environment and internal firm characteristics. Translated to medicine, these are factors that include for example the structure of an institute, the environment including government policies, reimbursement, structure of the healthcare provider as well as the patient’s socioeconomic status.

While the physician recommendation plays a major role in decision making, insurance coverage cannot be ignored. In some settings, clinical benefit may be considered relevant, but reimbursement is rejected on the grounds of insufficient cost-effectiveness [57]. Insurance coverage influences many medical decisions, including which tests and procedures are implemented, sometimes even determining which patients are cared for. The increasing costs of cancer care impacts all elements of the healthcare system. Decisions in healthcare policy and individual clinical problems require careful weighing of risks and benefits and are commonly a trade-off of competing objectives: maximizing quality of life versus maximizing life expectancy vs. minimizing the resources required. Hopefully costs are not the primary driver when choosing treatments, but they must be acknowledged. Nadler et al. [58] reported that for most oncologists, costs do not influence their clinical practice and should not limit access to what they consider effective care. Around 80% of physicians would prescribe effective therapy regardless of cost, but the majority did not believe that these therapies necessarily offered good value [58]. Associated factors such as insurance status, availability of technology/drugs, and even rural or urban location influence decision making [1, 59-61].

The type of practice (e.g., private vs. public), size, and organization might influence decision making [62-64]. Physicians practicing in client-dependent practices respond more readily to the wishes of patients. On the other hand, physicians practicing in a colleague-dependent practice respond more readily to influences from their professional community [63]. Physicians are more likely to be early adopters of new drugs if they are involved in the medical community, for example, having regular contact with colleagues and hospital consultants [65-67].

Medical representatives visiting physicians can affect prescribing practices [68]. One of the major generators of the scientific output is the industry, a known influencer of clinical decision making [69]. A study in Saudi Arabia [70] reported 41% of the physicians’ decisions regarding drug prescriptions were influenced by medical representatives.

Clinicians who engage in EBM need to acknowledge the social and cultural factors that affect the health-care encounter, understand the important role of those factors in health-care decision making, and expand the paradigm of EBM to incorporate sociocultural influences more explicitly.

Most recommendations in oncology are not based solely on high-level evidence [71, 72]. While guideline adherence can rightfully be viewed as positive, one should be aware that only 6% of the recommendations found in NCCN guidelines are based on high-level evidence [73]. Even in the clinical routine of academic oncology centers, most treatments are not based on high-level evidence [74]. In these settings, lower levels of evidence are used, for example, the results of phase II trials or observational studies. Where none of this evidence is available, recommendations are based on expert opinion.

Decision theory and related research concentrates on choice – the selection of the best option from a choice set containing 2 or more options, and the process of reasoning over multiple options [75]. Clinical recommendations in the form of decision trees, ideally based on high-level evidence, aim to identify the best option from a predefined set based on parameters (branches of the decision tree). When multiple options are considered, pre-choice (or screening) is important, as it reduces the decision maker’s workload and risk for wrong choices [75, 76]. An approach purely based on clinical algorithms (e.g., decision trees) and traditional EBM alone may be insufficient, especially when the limited availability of information and complexity of the patient and the environment are not sufficiently considered.

Using structured approaches to decision making involving multiple criteria can provide insight into objective parameters of decision making. An example for such an approach is the objective consensus methodology [77]. By analyzing and comparing decision trees based on the same rules and terminology, information on medical decision making among medical experts can be obtained. Based on information from multiple sources in the decision tree format, treatment recommendations can be assessed for every possible parameter combination (permutation; Fig. 2) [7]. For various cancer forms and settings, even among highly specialized medical centers or experts, the use of decision criteria varies considerably [7, 40, 78-80].

Fig. 2.

The consensus for 11 decision trees for systemic therapy of metastatic clear-cell renal cell cancer. A decision tree showing combinations of decision criteria where at least 8 of 11 centers agreed on the same recommendation. All relevant criteria are displayed and combined to reach every possible combination. For example, for the top-most row, when these criteria are fulfilled there is no consensus. For the fourth row, the most common recommendation is pazopanib in 73% (8 out of 11 centers recommend PAZ). CI, cardiac insufficiency; mccRCC, metastatic clear cell renal cell carcinoma; MSKCC, Memorial Sloan Kettering Cancer Center; OLM, only or mainly lung metastases; PAZ, pazopanib; PS, performance status; SUN, sunitinib; ZZ, zugzwang (the compulsion to move).

Fig. 2.

The consensus for 11 decision trees for systemic therapy of metastatic clear-cell renal cell cancer. A decision tree showing combinations of decision criteria where at least 8 of 11 centers agreed on the same recommendation. All relevant criteria are displayed and combined to reach every possible combination. For example, for the top-most row, when these criteria are fulfilled there is no consensus. For the fourth row, the most common recommendation is pazopanib in 73% (8 out of 11 centers recommend PAZ). CI, cardiac insufficiency; mccRCC, metastatic clear cell renal cell carcinoma; MSKCC, Memorial Sloan Kettering Cancer Center; OLM, only or mainly lung metastases; PAZ, pazopanib; PS, performance status; SUN, sunitinib; ZZ, zugzwang (the compulsion to move).

Close modal

How criteria are weighed is influenced by the healthcare setting, the institution or the individual physician as well as the patient. The decision making process is very complex. Variable criteria weights and different aggregation rules lead to a multitude of possible interpretations and implications. Also, the impact of each of the above-mentioned categories of decision criteria varies, the lines between these categories can blur at times and there is interaction between these categories. For example, the “contextual” factors may dominate in some settings when they influence the number of available options: selecting from treatment options is only possible when the infrastructure for these treatments is available. In highly symptomatic patients, for example, due to the tumor-related compression of the spinal canal, the need for quick intervention dominates and thus the “decision-specific” criteria define the decision making pattern. In such a scenario, zugzwang (the compulsion to move) is based in decision-specific factors (the disease requiring urgent treatment); however, the compulsion is perceived by the patient, or even the physician [7].

When oncologists and patients are confronted with multiple decision options, their choice is influenced by several factors extending beyond rational or analytical decision making models. For decision makers, whether they are individuals or committees, it is challenging to process and evaluate all relevant information. As demonstrated, a myriad of different decision criteria is used in oncology. While the list presented is by far not exhaustive, it demonstrates the complexities and variability of decision making criteria in oncology. For any improvements in our decision making to be possible, we first need to acknowledge the complexity of decision making criteria and the impact they have.

The authors declare they have no competing interests.

Panje CM, Glatzer M, Sirén C, Plasswilm L, Putora PM: Treatment options in oncology. JCO Clin Cancer Inform 2018, in press.
Papadakis VM, Lioukas S, Chambers D: Strategic decision-making processes: the role of management and context. Strateg Manag J 1998; 19: 115–147.
Elbanna S: The influence of decision, environmental and firm characteristics on the rationality of strategic decision-making. J Manag Stud 2007; 44: 4.
Isaacs D, Fitzgerald D: Seven alternatives to evidence based medicine. BMJ 1999; 319: 1618.
Beers E, Lee Nilsen M, Johnson JT: The role of patients: shared decision-making. Otolaryngol Clin North Am 2017; 50: 689–708.
Finklestein EO, S: Cognitive bias: the downside of shared decision making. JCO Clin Cancer Inform 2018, in press.
Rothermundt C, Bailey A, Cerbone L, Eisen T, Escudier B, Gillessen S, Grunwald V, Larkin J, McDermott D, Oldenburg J, Porta C, Rini B, Schmidinger M, Sternberg C, Putora PM: Algorithms in the first-line treatment of metastatic clear cell renal cell carcinoma – analysis using diagnostic nodes. Oncologist 2015; 20: 1028–1035.
Keren G, Teigen KH: Yet another look at the heuristics and biases approach; in Koehler DJ, Harvey N (eds): Blackwell Handbook of Judgment and Decision Making. Malden, Blackwell Publishing, 2004, pp 89–10.
Sloman SA: The empirical case for two systems of reasoning. Psychol Bull 1996; 119: 3–22.
Denes-Raj V, Epstein S: Conflict between intuitive and rational processing: when people behave against their better judgment. J Pers Soc Psychol 1994; 66: 819–829.
Tversky A, Kahneman D: Extensional versus intuitive reasoning: the conjunction fallacy in probability judgment. Psychol Rev 1983; 90: 293.
Kahneman D: A perspective on judgment and choice: mapping bounded rationality. Am Psychol 2003; 58: 697–720.
Woodworth RS: The Accuracy of Voluntary Movement. Columbia University, 1899.
Ivanoff J, Branning P, Marois R: fMRI evidence for a dual process account of the speed-accuracy tradeoff in decision-making. PLoS One 2008; 3:e2635.
Van Veen V, Krug MK, Carter CS: The neural and computational basis of controlled speed-accuracy tradeoff during task performance. J Cogn Neurosci 2008; 20: 1952–1965.
Hayashi AM: When to trust your gut. Harv Bus Rev 2001; 79: 59–65.
Khatri N, Ng HA: The role of intuition in strategic decision making. Hum Relations 2000; 53: 57–86.
Burke LA, Miller MK: Taking the mystery out of intuitive decision making. Acad Manag Exec 1999; 13: 91–99.
Dane E, Pratt MG: Exploring intuition and its role in managerial decision making. Acad Manag Rev 2007; 32: 33–54.
Putora PM, Oldenburg J: Swarm-based medicine. J Med Internet Res 2013; 15:e207.
Hall AE, Paul C, Bryant J, Lynagh MC, Rowlings P, Enjeti A, Small H: To adhere or not to adhere: rates and reasons of medication adherence in hematological cancer patients. Crit Rev Oncol Hematol 2016; 97: 247–262.
Foulon V, Schoffski P, Wolter P: Patient adherence to oral anticancer drugs: an emerging issue in modern oncology. Acta Clin Belg 2011; 66: 85–96.
De Geest S, Sabate E: Adherence to long-term therapies: evidence for action. Eur J Cardiovasc Nurs 2003; 2: 323.
Hajjaj FM, Salek MS, Basra MK, Finlay AY: Non-clinical influences on clinical decision-making: a major challenge to evidence-based practice. J R Soc Med 2010; 103: 178–187.
Fowler FJ Jr, McNaughton Collins M, Albertsen PC, Zietman A, Elliott DB, Barry MJ: Comparison of recommendations by urologists and radiation oncologists for treatment of clinically localized prostate cancer. JAMA 2000; 283: 3217–3222.
de Jong D, Aleman BM, Taal BG, Boot H: Controversies and consensus in the diagnosis, work-up and treatment of gastric lymphoma: an international survey. Ann Oncol 1999; 10: 275–280.
Rich AL, Tata LJ, Free CM, Stanley RA, Peake MD, Baldwin DR, Hubbard RB: Inequalities in outcomes for non-small cell lung cancer: the influence of clinical characteristics and features of the local lung cancer service. Thorax 2011; 66: 1078–1084.
McKinstry B: Paternalism and the doctor-patient relationship in general practice. Br J Gen Pract 1992; 42: 340–342.
Rodriguez-Osorio CA, Dominguez-Cherit G: Medical decision making: paternalism versus patient-centered (autonomous) care. Curr Opin Crit Care 2008; 14: 708–713.
Beisecker AE: Physicians are key to breast cancer early detection. Kans Med 1994; 95: 280–281.
Bouchardy C, Rapiti E, Blagojevic S, Vlastos AT, Vlastos G: Older female cancer patients: importance, causes, and consequences of undertreatment. J Clin Oncol 2007; 25: 1858–1869.
Murray MA, O’Connor AM, Fiset V, Viola R: Women’s decision-making needs regarding place of care at end of life. J Palliat Care 2003; 19: 176–184.
Fiset V, O’Connor AM, Evans W, Graham I, Degrasse C, Logan J: Development and evaluation of a decision aid for patients with stage IV non-small cell lung cancer. Health Expect 2000; 3: 125–136.
Ha JF, Longnecker N: Doctor-patient communication: a review. Ochsner J 2010; 10: 38–43.
Starcke K, Brand M: Decision making under stress: a selective review. Neurosci Biobehav Rev 2012; 36: 1228–1248.
Martin AM, Raabe E, Eberhart C, Cohen KJ: Management of pediatric and adult patients with medulloblastoma. Curr Treat Options Oncol 2014; 15: 581–594.
Wildiers H, Kunkler I, Biganzoli L, Fracheboud J, Vlastos G, Bernard-Marty C, Hurria A, Extermann M, Girre V, Brain E: Management of breast cancer in elderly individuals: recommendations of the international society of geriatric oncology. Lancet Oncol 2007; 8: 1101–1115.
Hurria A, Leung D, Trainor K, Borgen P, Norton L, Hudis C: Factors influencing treatment patterns of breast cancer patients age 75 and older. Crit Rev Oncol Hematol 2003; 46: 121–126.
Glatzer M, Rittmeyer A, Muller J, Opitz I, Papachristofilou A, Psallidas I, Fruh M, Born D, Putora PM: Treatment of limited disease small cell lung cancer: the multidisciplinary team. Eur Respir J 2017; 50.
Hundsberger T, Hottinger AF, Roelcke U, Roth P, Migliorini D, Dietrich PY, Conen K, Pesce G, Hermann E, Pica A, Gross MW, Brugge D, Plasswilm L, Weller M, Putora PM: Patterns of care in recurrent glioblastoma in Switzerland: a multicentre national approach based on diagnostic nodes. J Neurooncol 2016; 126: 175–183.
Fortin M, Dionne J, Pinho G, Gignac J, Almirall J, Lapointe L: Randomized controlled trials: do they have external validity for patients with multiple comorbidities? Ann Fam Med 2006; 4: 104–108.
Di Maio M, Perrone F: Quality of life in elderly patients with cancer. Health Qual Life Outcomes 2003; 1: 44.
Stairmand J, Signal L, Sarfati D, Jackson C, Batten L, Holdaway M, Cunningham C: Consideration of comorbidity in treatment decision making in multidisciplinary cancer team meetings: a systematic review. Ann Oncol 2015; 26: 1325–1332.
Keating NL, Landrum MB, Klabunde CN, Fletcher RH, Rogers SO, Doucette WR, Tisnado D, Clauser S, Kahn KL: Adjuvant chemotherapy for stage III colon cancer: do physicians agree about the importance of patient age and comorbidity? J Clin Oncol 2008; 26: 2532–2537.
Vinod SK, Sidhom MA, Delaney GP: Do multidisciplinary meetings follow guideline-based care? J Oncol Pract 2010; 6: 276–281.
Agarwal JP, Chakraborty S, Laskar SG, Mummudi N, Patil VM, Prabhash K, Noronha V, Purandare N, Joshi A, Tandon S, Arora J, Badhe R: Prognostic value of a patient-reported functional score versus physician-reported Karnofsky Performance Status Score in brain metastases. Ecancermedicalscience 2017; 11: 779.
Mor V, Laliberte L, Morris JN, Wiemann M: The Karnofsky Performance Status Scale. An examination of its reliability and validity in a research setting. Cancer 1984; 53: 2002–2007.
Thon N, Kreth S, Kreth FW: Personalized treatment strategies in glioblastoma: MGMT promoter methylation status. Onco Targets Ther 2013; 6: 1363–1372.
Wick W, Platten M, Meisner C, Felsberg J, Tabatabai G, Simon M, Nikkhah G, Papsdorf K, Steinbach JP, Sabel M, Combs SE, Vesper J, Braun C, Meixensberger J, Ketter R, Mayer-Steinacker R, Reifenberger G, Weller M; NOA-08 Study Group of Neuro-oncology Working Group (NOA) of German Cancer Society: Temozolomide chemotherapy alone versus radiotherapy alone for malignant astrocytoma in the elderly: the NOA-08 randomised, phase 3 trial. Lancet Oncol 2012; 13: 707–715.
Malmstrom A, Gronberg BH, Marosi C, Stupp R, Frappaz D, Schultz H, Abacioglu U, Tavelin B, Lhermitte B, Hegi ME, Rosell J, Henriksson R; Nordic Clinical Brain Tumour Study Group (NCBTSG): Temozolomide versus standard 6-week radiotherapy versus hypofractionated radiotherapy in patients older than 60 years with glioblastoma: the Nordic randomised, phase 3 trial. Lancet Oncol 2012; 13: 916–926.
Jaafar H, Bashir MA, Taher A, Qawasmeh K, Jaloudi M: Impact of Oncotype DX testing on adjuvant treatment decisions in patients with early breast cancer: a single-center study in the United Arab Emirates. Asia Pac J Clin Oncol 2014; 10: 354–360.
Cheang MC, Chia SK, Voduc D, Gao D, Leung S, Snider J, Watson M, Davies S, Bernard PS, Parker JS, Perou CM, Ellis MJ, Nielsen TO: Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. J Natl Cancer Inst 2009; 101: 736–750.
Goldhirsch A, Winer EP, Coates AS, Gelber RD, Piccart-Gebhart M, Thurlimann B, Senn HJ, Panel m: Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol 2013; 24: 2206–2223.
Coates AS, Winer EP, Goldhirsch A, Gelber RD, Gnant M, Piccart-Gebhart M, Thurlimann B, Senn HJ, Panel M: Tailoring therapies – improving the management of early breast cancer: St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2015. Ann Oncol 2015; 26: 1533–1546.
Fasching PA, Heusinger K, Haeberle L, Niklos M, Hein A, Bayer CM, Rauh C, Schulz-Wendtland R, Bani MR, Schrauder M, Kahmann L, Lux MP, Strehl JD, Hartmann A, Dimmler A, Beckmann MW, Wachter DL: Ki67, chemotherapy response, and prognosis in breast cancer patients receiving neoadjuvant treatment. BMC Cancer 2011; 11: 486.
Ost P, Reynders D, Decaestecker K, Fonteyne V, Lumen N, De Bruycker A, Lambert B, Delrue L, Bultijnck R, Claeys T, Goetghebeur E, Villeirs G, De Man K, Ameye F, Billiet I, Joniau S, Vanhaverbeke F, De Meerleer G: Surveillance or metastasis-directed therapy for oligometastatic prostate cancer recurrence: a prospective, randomized, multicenter phase II trial. J Clin Oncol 2017; 36: 446–453.
Kreeftmeijer JR, Van Engen A, Heemstra L: Hierarchy of clinical endpoints in HTA decision making in oncology. Value Health 2015; 18:A1–A307.
Nadler E, Eckert B, Neumann PJ: Do oncologists believe new cancer drugs offer good value? Oncologist 2006; 11: 90–95.
Giuliani M, Sun A, Bezjak A, Ma C, Le LW, Brade A, Cho J, Leighl NB, Shepherd FA, Hope AJ: Utilization of prophylactic cranial irradiation in patients with limited stage small cell lung carcinoma. Cancer 2010; 116: 5694–5699.
Lee K, Kloecker G, Pan J, Rai S, Dunlap NE: The integration of multimodality care for the treatment of small cell lung cancer in a rural population and its impact on survival. Am J Clin Oncol 2015; 38: 448–456.
Langer CJ, Moughan J, Movsas B, Komaki R, Ettinger D, Owen J, Wilson JF: Patterns of care survey (PCS) in lung cancer: how well does current U.S. practice with chemotherapy in the non-metastatic setting follow the literature? Lung Cancer 2005; 48: 93–102.
McKinlay JB, Potter DA, Feldman HA: Non-medical influences on medical decision-making. Soc Sci Med 1996; 42: 769–776.
Eisenberg JM: Sociologic influences on decision-making by clinicians. Ann Intern Med 1979; 90: 957–964.
Bernheim SM, Ross JS, Krumholz HM, Bradley EH: Influence of patients’ socioeconomic status on clinical management decisions: a qualitative study. Ann Fam Med 2008; 6: 53–59.
Prosser H, Walley T: New drug uptake: qualitative comparison of high and low prescribing GPs’ attitudes and approach. Fam Pract 2003; 20: 583–591.
Feely J, Chan R, McManus J, O’Shea B: The influence of hospital-based prescribers on prescribing in general practice. Pharmacoeconomics 1999; 16: 175–181.
Schumock GT, Walton SM, Park HY, Nutescu EA, Blackburn JC, Finley JM, Lewis RK: Factors that influence prescribing decisions. Ann Pharmacother 2004; 38: 557–562.
Lieb K, Scheurich A: Contact between doctors and the pharmaceutical industry, their perceptions, and the effects on prescribing habits. PLoS One 2014; 9:e110130.
Kessel M: Restoring the pharmaceutical industry’s reputation. Nat Biotechnol 2014; 32: 983–990.
Zahrani A, Kandil M, Badar T, Abdelsalam M, Al-Faiar A, Ismail A: Clinico-pathological study of K-ras mutations in colorectal tumors in Saudi Arabia. Tumori 2014; 100: 75–79.
Djulbegovic B, Loughran TP Jr, Hornung CA, Kloecker G, Efthimiadis EN, Hadley TJ, Englert J, Hoskins M, Goldsmith GH: The quality of medical evidence in hematology-oncology. Am J Med 1999; 106: 198–205.
Vincent S, Djulbegovic B: Oncology treatment recommendations can be supported only by 1–2% of high-quality published evidence. Cancer Treat Rev 2005; 31: 319–322.
Poonacha TK, Go RS: Level of scientific evidence underlying recommendations arising from the National Comprehensive Cancer Network clinical practice guidelines. J Clin Oncol 2011; 29: 186–191.
Apisarnthanarax S, Swisher-McClure S, Chiu WK, Kimple RJ, Harris SL, Morris DE, Tepper JE: Applicability of randomized trials in radiation oncology to standard clinical practice. Cancer 2013; 119: 3092–3099.
Tversky A, Shafir E: Choice under conflict: the dynamics of deferred decision. Psychol Sci 1992; 3: 358–361.
Beach LR: Broadening the definition of -decision making: the role of prechoice screening of options. Psychol Sci 1993; 4: 215–220.
Putora PM, Panje CM, Papachristofilou A, Dal Pra A, Hundsberger T, Plasswilm L: Objective consensus from decision trees. Radiat Oncol 2014; 9: 270.
Panje CM, Dal Pra A, Zilli T, R Zwahlen D, Papachristofilou A, Herrera FG, Matzinger O, Plasswilm L, Putora PM: Consensus and differences in primary radiotherapy for localized and locally advanced prostate cancer in Switzerland: a survey on patterns of practice. Strahlenther Onkol 2015; 191: 778–786.
Panje CM, Glatzer M, von Rappard J, Rothermundt C, Hundsberger T, Zumstein V, Plasswilm L, Putora PM: Applied Swarm-based medicine: collecting decision trees for patterns of algorithms analysis. BMC Med Res Methodol 2017; 17: 123.
Rothermundt C, Fischer GF, Bauer S, Blay JY, Grunwald V, Italiano A, Kasper B, Kollar A, Lindner LH, Miah A, Sleijfer S, Stacchiotti S, Putora PM: Pre- and postoperative chemotherapy in localized extremity soft tissue sarcoma: a European organization for research and treatment of cancer expert survey. Oncologist 2018; 23: 461–467.
Copyright / Drug Dosage / Disclaimer
Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.